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Scalable Task-Driven Robotic Swarm Control via Collision Avoidance and Learning Mean-Field Control | https://ieeexplore.ieee.org/document/10161498/ | [
"Kai Cui",
"Mengguang Li",
"Christian Fabian",
"Heinz Koeppl",
"Kai Cui",
"Mengguang Li",
"Christian Fabian",
"Heinz Koeppl"
] | In recent years, reinforcement learning and its multi-agent analogue have achieved great success in solving various complex control problems. However, multi-agent rein-forcement learning remains challenging both in its theoretical analysis and empirical design of algorithms, especially for large swarms of embodied robotic agents where a definitive toolchain remains part of active research. We use ... |
STD-Trees: Spatio-temporal Deformable Trees for Multirotors Kinodynamic Planning | https://ieeexplore.ieee.org/document/10161555/ | [
"Hongkai Ye",
"Chao Xu",
"Fei Gao",
"Hongkai Ye",
"Chao Xu",
"Fei Gao"
] | In constrained solution spaces with a huge number of homotopy classes, standalone sampling-based kinodynamic planners suffer low efficiency in convergence. Local optimization is integrated to alleviate this problem. In this paper, we propose to thrive the trajectory tree growing by optimizing the tree in the forms of deformation units, and each unit contains one tree node and all the edges connect... |
PredRecon: A Prediction-boosted Planning Framework for Fast and High-quality Autonomous Aerial Reconstruction | https://ieeexplore.ieee.org/document/10160933/ | [
"Chen Feng",
"Haojia Li",
"Fei Gao",
"Boyu Zhou",
"Shaojie Shen",
"Chen Feng",
"Haojia Li",
"Fei Gao",
"Boyu Zhou",
"Shaojie Shen"
] | Autonomous UAV path planning for 3D reconstruction has been actively studied in various applications for high-quality 3D models. However, most existing works have adopted explore-then-exploit, prior-based or exploration-based strategies, demonstrating inefficiency with repeated flight and low autonomy. In this paper, we propose PredRecon, a prediction-boosted planning framework that can autonomous... |
Vision-aided UAV Navigation and Dynamic Obstacle Avoidance using Gradient-based B-spline Trajectory Optimization | https://ieeexplore.ieee.org/document/10160638/ | [
"Zhefan Xu",
"Yumeng Xiu",
"Xiaoyang Zhan",
"Baihan Chen",
"Kenji Shimada",
"Zhefan Xu",
"Yumeng Xiu",
"Xiaoyang Zhan",
"Baihan Chen",
"Kenji Shimada"
] | Navigating dynamic environments requires the robot to generate collision-free trajectories and actively avoid moving obstacles. Most previous works designed path planning algorithms based on one single map representation, such as the geometric, occupancy, or ESDF map. Although they have shown success in static environments, due to the limitation of map representation, those methods cannot reliably... |
Multi-Agent Spatial Predictive Control with Application to Drone Flocking | https://ieeexplore.ieee.org/document/10160617/ | [
"Andreas Brandstätter",
"Scott A. Smolka",
"Scott D. Stoller",
"Ashish Tiwari",
"Radu Grosu",
"Andreas Brandstätter",
"Scott A. Smolka",
"Scott D. Stoller",
"Ashish Tiwari",
"Radu Grosu"
] | We introduce Spatial Predictive Control (SPC), a technique for solving the following problem: given a collection of robotic agents with black-box positional low-level controllers (PLLCs) and a mission-specific distributed cost function, how can a distributed controller achieve and maintain cost-function minimization without a plant model and only positional observations of the environment? Our ful... |
Multimodal Image Registration for GPS-denied UAV Navigation Based on Disentangled Representations | https://ieeexplore.ieee.org/document/10161567/ | [
"Huandong Li",
"Zhunga Liu",
"Yanyi Lyu",
"Feiyan Wu",
"Huandong Li",
"Zhunga Liu",
"Yanyi Lyu",
"Feiyan Wu"
] | Visual navigation plays an important role for Unmanned Aerial Vehicles(UAVs). In some applications, the landmark image and the real-time image may be heterogeneous, like near-infrared and visible images. In this work, we propose a multimodal image registration method to deal with near-infrared and visible images so that it can be applied to visual navigation system for the localization of UAVs in ... |
SEER: Safe Efficient Exploration for Aerial Robots using Learning to Predict Information Gain | https://ieeexplore.ieee.org/document/10160295/ | [
"Yuezhan Tao",
"Yuwei Wu",
"Beiming Li",
"Fernando Cladera",
"Alex Zhou",
"Dinesh Thakur",
"Vijay Kumar",
"Yuezhan Tao",
"Yuwei Wu",
"Beiming Li",
"Fernando Cladera",
"Alex Zhou",
"Dinesh Thakur",
"Vijay Kumar"
] | We address the problem of efficient 3-D exploration in indoor environments for micro aerial vehicles with limited sensing capabilities and payload/power constraints. We develop an indoor exploration framework that uses learning to predict the occupancy of unseen areas, extracts semantic features, samples viewpoints to predict information gains for different exploration goals, and plans informative... |
Trajectory Planning for the Bidirectional Quadrotor as a Differentially Flat Hybrid System | https://ieeexplore.ieee.org/document/10160320/ | [
"Katherine Mao",
"Jake Welde",
"M. Ani Hsieh",
"Vijay Kumar",
"Katherine Mao",
"Jake Welde",
"M. Ani Hsieh",
"Vijay Kumar"
] | The use of bidirectional propellers provides quadrotors with greater maneuverability which is advantageous in constrained environments. This paper addresses the development of a trajectory planning algorithm for quadrotors with bidirectional motors. Previous work has shown that the property of differential flatness can be leveraged for efficient trajectory planning. However, planners that leverage... |
Fisher Information Based Active Planning for Aerial Photogrammetry | https://ieeexplore.ieee.org/document/10161136/ | [
"Jaeyoung Lim",
"Nicholas Lawrance",
"Florian Achermann",
"Thomas Stastny",
"Rik Bähnemann",
"Roland Siegwart",
"Jaeyoung Lim",
"Nicholas Lawrance",
"Florian Achermann",
"Thomas Stastny",
"Rik Bähnemann",
"Roland Siegwart"
] | Small uncrewed aerial systems (sUASs) are useful tools for 3D reconstruction due to their speed, ease of use, and ability to access high-utility viewpoints. Today, most aerial survey approaches generate a preplanned coverage pattern assuming a planar target region. However, this is inefficient since it results in superfluous overlap and suboptimal viewing angles and does not utilize the entire fli... |
Integrated vector field and backstepping control for quadcopters | https://ieeexplore.ieee.org/document/10160824/ | [
"Arthur H. D. Nunes",
"Guilherme V. Raffo",
"Luciano C. A. Pimenta",
"Arthur H. D. Nunes",
"Guilherme V. Raffo",
"Luciano C. A. Pimenta"
] | In this work, we present an Integrated Guidance and Controller (IGC) scheme to drive quadcopters in path-following tasks with obstacle avoidance and constant uncertainty rejection. This scheme is based on the combination of a time-varying artificial vector field and Backstepping with integral action control. The vector field switches between two behaviors: (i) path-following; and (ii) obstacle cir... |
Learning a Single Near-hover Position Controller for Vastly Different Quadcopters | https://ieeexplore.ieee.org/document/10160836/ | [
"Dingqi Zhang",
"Antonio Loquercio",
"Xiangyu Wu",
"Ashish Kumar",
"Jitendra Malik",
"Mark W. Mueller",
"Dingqi Zhang",
"Antonio Loquercio",
"Xiangyu Wu",
"Ashish Kumar",
"Jitendra Malik",
"Mark W. Mueller"
] | This paper proposes an adaptive near-hover position controller for quadcopters, which can be deployed to quadcopters of very different mass, size and motor constants, and also shows rapid adaptation to unknown disturbances during runtime. The core algorithmic idea is to learn a single policy that can adapt online at test time not only to the disturbances applied to the drone, but also to the robot... |
Forming and Controlling Hitches in Midair Using Aerial Robots | https://ieeexplore.ieee.org/document/10160741/ | [
"Diego S. D’Antonio",
"Subhrajit Bhattacharya",
"David Saldaña",
"Diego S. D’Antonio",
"Subhrajit Bhattacharya",
"David Saldaña"
] | The use of cables for aerial manipulation has shown to be a lightweight and versatile way to interact with objects. However, fastening objects using cables is still a challenge and human is required. In this work, we propose a novel way to secure objects using hitches. The hitch can be formed and morphed in midair using a team of aerial robots with cables. The hitch's shape is modeled as a convex ... |
AirTrack: Onboard Deep Learning Framework for Long-Range Aircraft Detection and Tracking | https://ieeexplore.ieee.org/document/10160627/ | [
"Sourish Ghosh",
"Jay Patrikar",
"Brady Moon",
"Milad Moghassem Hamidi",
"Sebastian Scherer",
"Sourish Ghosh",
"Jay Patrikar",
"Brady Moon",
"Milad Moghassem Hamidi",
"Sebastian Scherer"
] | Detect-and-Avoid (DAA) capabilities are critical for safe operations of unmanned aircraft systems (UAS). This paper introduces, AirTrack, a real-time vision-only detect and tracking framework that respects the size, weight, and power (SWaP) constraints of sUAS systems. Given the low Signal-to-Noise ratios (SNR) of far away aircraft, we propose using full resolution images in a deep learning framew... |
Towards a Reliable and Lightweight Onboard Fault Detection in Autonomous Unmanned Aerial Vehicles | https://ieeexplore.ieee.org/document/10161183/ | [
"Sai Srinadhu Katta",
"Eduardo Kugler Viegas",
"Sai Srinadhu Katta",
"Eduardo Kugler Viegas"
] | This paper proposes a new model for onboard physical fault detection on autonomous unmanned aerial vehicles (UAV) through machine learning (ML) techniques. The proposal performs the detection task with high accuracies and minimal processing requirements while signaling an unreliable ML model to the operator, implemented in two main phases. First, a wrapper-based feature selection is performed to d... |
Variable Admittance Interaction Control of UAVs via Deep Reinforcement Learning | https://ieeexplore.ieee.org/document/10160558/ | [
"Yuting Feng",
"Chuanbeibei Shi",
"Jianrui Du",
"Yushu Yu",
"Fuchun Sun",
"Yixu Song",
"Yuting Feng",
"Chuanbeibei Shi",
"Jianrui Du",
"Yushu Yu",
"Fuchun Sun",
"Yixu Song"
] | A compliant control model based on reinforcement learning (RL) is proposed to allow robots to interact with the environment more effectively and autonomously execute force control tasks. The admittance model learns an optimal adjustment policy for interactions with the external environment using RL algorithms. The model combines energy consumption and trajectory tracking of the agent state using a... |
Learning Tethered Perching for Aerial Robots | https://ieeexplore.ieee.org/document/10161135/ | [
"Fabian Hauf",
"Basaran Bahadir Kocer",
"Alan Slatter",
"Hai-Nguyen Nguyen",
"Oscar Pang",
"Ronald Clark",
"Edward Johns",
"Mirko Kovac",
"Fabian Hauf",
"Basaran Bahadir Kocer",
"Alan Slatter",
"Hai-Nguyen Nguyen",
"Oscar Pang",
"Ronald Clark",
"Edward Johns",
"Mirko Kovac"
] | Aerial robots have a wide range of applications, such as collecting data in hard-to-reach areas. This requires the longest possible operation time. However, because currently available commercial batteries have limited specific energy of roughly 300 W h kg-1, a drone's flight time is a bottleneck for sustainable long-term data collection. Inspired by birds in nature, a possible approach to tackle ... |
Credible Online Dynamics Learning for Hybrid UAVs | https://ieeexplore.ieee.org/document/10160517/ | [
"David Rohr",
"Nicholas Lawrance",
"Olov Andersson",
"Roland Siegwart",
"David Rohr",
"Nicholas Lawrance",
"Olov Andersson",
"Roland Siegwart"
] | Hybrid unmanned aerial vehicles (H-UAVs) are highly versatile platforms with the ability to transition between rotary- and fixed-wing flight. However, their (aero)dynamics tend to be highly nonlinear which increases the risk of introducing safety-critical modeling errors in a controller. Designing a safe, yet not too cautious controller, requires a credible model which provides accurate dynamics u... |
AZTR: Aerial Video Action Recognition with Auto Zoom and Temporal Reasoning | https://ieeexplore.ieee.org/document/10160564/ | [
"Xijun Wang",
"Ruiqi Xian",
"Tianrui Guan",
"Celso M. de Melo",
"Stephen M. Nogar",
"Aniket Bera",
"Dinesh Manocha",
"Xijun Wang",
"Ruiqi Xian",
"Tianrui Guan",
"Celso M. de Melo",
"Stephen M. Nogar",
"Aniket Bera",
"Dinesh Manocha"
] | We propose a novel approach for aerial video action recognition. Our method is designed for videos captured using UAVs and can run on edge or mobile devices. We present a learning-based approach that uses customized auto zoom to automatically identify the human target and scale it appropriately. This makes it easier to extract the key features and reduces the computational overhead. We also presen... |
Follow The Rules: Online Signal Temporal Logic Tree Search for Guided Imitation Learning in Stochastic Domains | https://ieeexplore.ieee.org/document/10160953/ | [
"Jasmine Jerry Aloor",
"Jay Patrikar",
"Parv Kapoor",
"Jean Oh",
"Sebastian Scherer",
"Jasmine Jerry Aloor",
"Jay Patrikar",
"Parv Kapoor",
"Jean Oh",
"Sebastian Scherer"
] | Seamlessly integrating rules in Learning-from-Demonstrations (LfD) policies is a critical requirement to enable the real-world deployment of AI agents. Recently, Signal Temporal Logic (STL) has been shown to be an effective language for encoding rules as spatio-temporal constraints. This work uses Monte Carlo Tree Search (MCTS) as a means of integrating STL specification into a vanilla LfD policy ... |
Continuity-Aware Latent Interframe Information Mining for Reliable UAV Tracking | https://ieeexplore.ieee.org/document/10160673/ | [
"Changhong Fu",
"Mutian Cai",
"Sihang Li",
"Kunhan Lu",
"Haobo Zuo",
"Chongjun Liu",
"Changhong Fu",
"Mutian Cai",
"Sihang Li",
"Kunhan Lu",
"Haobo Zuo",
"Chongjun Liu"
] | Unmanned aerial vehicle (UAV) tracking is crucial for autonomous navigation and has broad applications in robotic automation fields. However, reliable UAV tracking remains a challenging task due to various difficulties like frequent occlusion and aspect ratio change. Additionally, most of the existing work mainly focuses on explicit information to improve tracking performance, ignoring potential i... |
Weighted Maximum Likelihood for Controller Tuning | https://ieeexplore.ieee.org/document/10161417/ | [
"Angel Romero",
"Shreedhar Govil",
"Gonca Yilmaz",
"Yunlong Song",
"Davide Scaramuzza",
"Angel Romero",
"Shreedhar Govil",
"Gonca Yilmaz",
"Yunlong Song",
"Davide Scaramuzza"
] | Recently, Model Predictive Contouring Control (MPCC) has arisen as the state-of-the-art approach for model-based agile flight. MPCC benefits from great flexibility in trading-off between progress maximization and path following at runtime without relying on globally optimized trajectories. However, finding the optimal set of tuning parameters for MPCC is challenging because (i) the full quadrotor ... |
User-Conditioned Neural Control Policies for Mobile Robotics | https://ieeexplore.ieee.org/document/10160851/ | [
"Leonard Bauersfeld",
"Elia Kaufmann",
"Davide Scaramuzza",
"Leonard Bauersfeld",
"Elia Kaufmann",
"Davide Scaramuzza"
] | Recently, learning-based controllers have been shown to push mobile robotic systems to their limits and provide the robustness needed for many real-world applications. However, only classical optimization-based control frameworks offer the inherent flexibility to be dynamically adjusted during execution by, for example, setting target speeds or actuator limits. We present a framework to overcome t... |
Training Efficient Controllers via Analytic Policy Gradient | https://ieeexplore.ieee.org/document/10160581/ | [
"Nina Wiedemann",
"Valentin Wüest",
"Antonio Loquercio",
"Matthias Müller",
"Dario Floreano",
"Davide Scaramuzza",
"Nina Wiedemann",
"Valentin Wüest",
"Antonio Loquercio",
"Matthias Müller",
"Dario Floreano",
"Davide Scaramuzza"
] | Control design for robotic systems is complex and often requires solving an optimization to follow a trajectory accurately. Online optimization approaches like Model Predictive Control (MPC) have been shown to achieve great tracking performance, but require high computing power. Conversely, learning-based offline optimization approaches, such as Reinforcement Learning (RL), allow fast and efficien... |
Parallel Reinforcement Learning Simulation for Visual Quadrotor Navigation | https://ieeexplore.ieee.org/document/10160675/ | [
"Jack Saunders",
"Sajad Saeedi",
"Wenbin Lil",
"Jack Saunders",
"Sajad Saeedi",
"Wenbin Lil"
] | Reinforcement learning (RL) is an agent-based approach for teaching robots to navigate within the physical world. Gathering data for RL is known to be a laborious task, and real-world experiments can be risky. Simulators facilitate the collection of training data in a quicker and more cost-effective manner. However, RL frequently requires a significant number of simulation steps for an agent to be... |
Toward Efficient Physical and Algorithmic Design of Automated Garages | https://ieeexplore.ieee.org/document/10160351/ | [
"Teng Guo",
"Jingjin Yu",
"Teng Guo",
"Jingjin Yu"
] | Parking in large metropolitan areas is often a time-consuming task with further implications for traffic patterns that affect urban landscaping. Reducing the premium space needed for parking has led to the development of automated mechanical parking systems. Compared to regular garages having one or two rows of vehicles on each island, automated garages can have multiple rows of vehicles stacked t... |
Chronos and CRS: Design of a miniature car-like robot and a software framework for single and multi-agent robotics and control | https://ieeexplore.ieee.org/document/10161434/ | [
"Andrea Carron",
"Sabrina Bodmer",
"Lukas Vogel",
"René Zurbrügg",
"David Helm",
"Rahel Rickenbach",
"Simon Muntwiler",
"Jerome Sieber",
"Melanie N. Zeilinger",
"Andrea Carron",
"Sabrina Bodmer",
"Lukas Vogel",
"René Zurbrügg",
"David Helm",
"Rahel Rickenbach",
"Simon Muntwiler",
"Jerome Sieber",
"Melanie N. Zeilinger"
] | From both an educational and research point of view, experiments on hardware are a key aspect of robotics and control. In the last decade, many open-source hardware and software frameworks for wheeled robots have been presented, mainly in the form of unicycles and car-like robots, with the goal of making robotics accessible to a wider audience and to support control systems development. Unicycles ... |
Multi-Agent Path Integral Control for Interaction-Aware Motion Planning in Urban Canals | https://ieeexplore.ieee.org/document/10161511/ | [
"Lucas Streichenberg",
"Elia Trevisan",
"Jen Jen Chung",
"Roland Siegwart",
"Javier Alonso-Mora",
"Lucas Streichenberg",
"Elia Trevisan",
"Jen Jen Chung",
"Roland Siegwart",
"Javier Alonso-Mora"
] | Autonomous vehicles that operate in urban environments shall comply with existing rules and reason about the interactions with other decision-making agents. In this paper, we introduce a decentralized and communication-free interaction-aware motion planner and apply it to Autonomous Surface Vessels (ASVs) in urban canals. We build upon a sampling-based method, namely Model Predictive Path Integral... |
Mixed Observable RRT: Multi-Agent Mission-Planning in Partially Observable Environments | https://ieeexplore.ieee.org/document/10160392/ | [
"Kasper Johansson",
"Ugo Rosolia",
"Wyatt Ubellacker",
"Andrew Singletary",
"Aaron D. Ames",
"Kasper Johansson",
"Ugo Rosolia",
"Wyatt Ubellacker",
"Andrew Singletary",
"Aaron D. Ames"
] | This paper considers centralized mission-planning for a heterogeneous multi-agent system with the aim of locating a hidden target. We propose a mixed observable setting, consisting of a fully observable state-space and a partially observable environment, using a hidden Markov model. First, we construct rapidly exploring random trees (RRTs) to introduce the mixed observable RRT for finding plausibl... |
RTAW: An Attention Inspired Reinforcement Learning Method for Multi-Robot Task Allocation in Warehouse Environments | https://ieeexplore.ieee.org/document/10161310/ | [
"Aakriti Agrawal",
"Amrit Singh Bedi",
"Dinesh Manocha",
"Aakriti Agrawal",
"Amrit Singh Bedi",
"Dinesh Manocha"
] | We present a novel reinforcement learning based algorithm for multi-robot task allocation problem in ware-house environments. We formulate it as a Markov Decision Process and solve via a novel deep multi-agent reinforcement learning method (called RTAW) with attention inspired policy architecture. Hence, our proposed policy network uses global embeddings that are independent of the number of robot... |
Hybrid SUSD-Based Task Allocation for Heterogeneous Multi-Robot Teams | https://ieeexplore.ieee.org/document/10161349/ | [
"Shengkang Chen",
"Tony X. Lin",
"Said Al-Abri",
"Ronald C. Arkin",
"Fumin Zhang",
"Shengkang Chen",
"Tony X. Lin",
"Said Al-Abri",
"Ronald C. Arkin",
"Fumin Zhang"
] | Effective task allocation is an essential component to the coordination of heterogeneous robots. This paper proposes a hybrid task allocation algorithm that improves upon given initial solutions, for example from the popular decentralized market-based allocation algorithm, via a derivative-free optimization strategy called Speeding-Up and Slowing-Down (SUSD). Based on the initial solutions, SUSD p... |
Search Algorithms for Multi-Agent Teamwise Cooperative Path Finding | https://ieeexplore.ieee.org/document/10160864/ | [
"Zhongqiang Ren",
"Chaoran Zhang",
"Sivakumar Rathinam",
"Howie Choset",
"Zhongqiang Ren",
"Chaoran Zhang",
"Sivakumar Rathinam",
"Howie Choset"
] | Multi-Agent Path Finding (MA-PF) computes a set of collision-free paths for multiple agents from their respective starting locations to destinations. This paper considers a generalization of MA-PF called Multi-Agent Teamwise Cooperative Path Finding (MA-TC-PF), where agents are grouped as multiple teams and each team has its own objective to be minimized. For example, an objective can be the sum o... |
Collaborative Scheduling with Adaptation to Failure for Heterogeneous Robot Teams | https://ieeexplore.ieee.org/document/10161502/ | [
"Peng Gao",
"Sriram Siva",
"Anthony Micciche",
"Hao Zhang",
"Peng Gao",
"Sriram Siva",
"Anthony Micciche",
"Hao Zhang"
] | Collaborative scheduling is an essential ability for a team of heterogeneous robots to collaboratively complete complex tasks, e.g., in a multi-robot assembly application. To enable collaborative scheduling, two key problems should be addressed, including allocating tasks to heterogeneous robots and adapting to robot failures in order to guarantee the completion of all tasks. In this paper, we int... |
AMSwarm: An Alternating Minimization Approach for Safe Motion Planning of Quadrotor Swarms in Cluttered Environments | https://ieeexplore.ieee.org/document/10161063/ | [
"Vivek K. Adajania",
"Siqi Zhou",
"Arun Kumar Singh",
"Angela P. Schoellig",
"Vivek K. Adajania",
"Siqi Zhou",
"Arun Kumar Singh",
"Angela P. Schoellig"
] | This paper presents a scalable online algorithm to generate safe and kinematically feasible trajectories for quadrotor swarms. Existing approaches rely on linearizing Euclidean distance-based collision constraints and on axis-wise decoupling of kinematic constraints to reduce the trajectory optimization problem for each quadrotor to a quadratic program (QP). This conservative approximation often f... |
Decentralized Deadlock-free Trajectory Planning for Quadrotor Swarm in Obstacle-rich Environments | https://ieeexplore.ieee.org/document/10160847/ | [
"Jungwon Park",
"Inkyu Jang",
"H. Jin Kim",
"Jungwon Park",
"Inkyu Jang",
"H. Jin Kim"
] | This paper presents a decentralized multi-agent trajectory planning (MATP) algorithm that guarantees to generate a safe, deadlock-free trajectory in an obstacle-rich environment under a limited communication range. The proposed algorithm utilizes a grid-based multi-agent path planning (MAPP) algorithm for deadlock resolution, and we introduce the subgoal optimization method to make the agent conve... |
A Negative Imaginary Theory-Based Time-Varying Group Formation Tracking Scheme for Multi-Robot Systems: Applications to Quadcopters | https://ieeexplore.ieee.org/document/10160850/ | [
"Yu-Hsiang Su",
"Parijat Bhowmick",
"Alexander Lanzon",
"Yu-Hsiang Su",
"Parijat Bhowmick",
"Alexander Lanzon"
] | This paper proposes a new methodology to develop a time-varying group formation tracking scheme for a class of multi-agent systems (e.g. different types of multi-robot systems) utilising Negative Imaginary (NI) theory. It offers a two-loop control scheme in which the inner loop deploys an appropriate feedback linearising control law to transform the nonlinear dynamics of each agent into a double i... |
Data-Driven Risk-sensitive Model Predictive Control for Safe Navigation in Multi-Robot Systems | https://ieeexplore.ieee.org/document/10161002/ | [
"Atharva Navsalkar",
"Ashish R. Hota",
"Atharva Navsalkar",
"Ashish R. Hota"
] | Safe navigation is a fundamental challenge in multi-robot systems due to the uncertainty surrounding the future trajectory of the robots that act as obstacles for each other. In this work, we propose a principled data-driven approach where each robot repeatedly solves a finite horizon optimization problem subject to collision avoidance constraints with latter being formulated as distributionally r... |
Multi-modal Hierarchical Transformer for Occupancy Flow Field Prediction in Autonomous Driving | https://ieeexplore.ieee.org/document/10160855/ | [
"Haochen Liu",
"Zhiyu Huang",
"Chen Lv",
"Haochen Liu",
"Zhiyu Huang",
"Chen Lv"
] | Forecasting the future states of surrounding traffic participants is a crucial capability for autonomous vehicles. The recently proposed occupancy flow field prediction introduces a scalable and effective representation to jointly predict surrounding agents' future motions in a scene. However, the challenging part is to model the underlying social interactions among traffic agents and the relation... |
Annotating Covert Hazardous Driving Scenarios Online: Utilizing Drivers' Electroencephalography (EEG) Signals | https://ieeexplore.ieee.org/document/10161448/ | [
"Chen Zheng",
"Muxiao Zi",
"Wenjie Jiang",
"Mengdi Chu",
"Yan Zhang",
"Jirui Yuan",
"Guyue Zhou",
"Jiangtao Gong",
"Chen Zheng",
"Muxiao Zi",
"Wenjie Jiang",
"Mengdi Chu",
"Yan Zhang",
"Jirui Yuan",
"Guyue Zhou",
"Jiangtao Gong"
] | As autonomous driving systems prevail, it is becoming increasingly critical that the systems learn from databases containing fine-grained driving scenarios. Most databases currently available are human-annotated; they are expensive, time-consuming, and subject to behavioral biases. In this paper, we provide initial evidence supporting a novel technique utilizing drivers' electroencephalography (EE... |
Pedestrian Crossing Action Recognition and Trajectory Prediction with 3D Human Keypoints | https://ieeexplore.ieee.org/document/10160273/ | [
"Jiachen Li",
"Xinwei Shi",
"Feiyu Chen",
"Jonathan Stroud",
"Zhishuai Zhang",
"Tian Lan",
"Junhua Mao",
"Jeonhyung Kang",
"Khaled S. Refaat",
"Weilong Yang",
"Eugene Ie",
"Congcong Li",
"Jiachen Li",
"Xinwei Shi",
"Feiyu Chen",
"Jonathan Stroud",
"Zhishuai Zhang",
"Tian Lan",
"Junhua Mao",
"Jeonhyung Kang",
"Khaled S. Refaat",
"Weilong Yang",
"Eugene Ie",
"Congcong Li"
] | Accurate understanding and prediction of human behaviors are critical prerequisites for autonomous vehicles, especially in highly dynamic and interactive scenarios such as intersections in dense urban areas. In this work, we aim at identifying crossing pedestrians and predicting their future trajectories. To achieve these goals, we not only need the context information of road geometry and other t... |
Model-Agnostic Multi-Agent Perception Framework | https://ieeexplore.ieee.org/document/10161460/ | [
"Runsheng Xu",
"Weizhe Chen",
"Hao Xiang",
"Xin Xia",
"Lantao Liu",
"Jiaqi Ma",
"Runsheng Xu",
"Weizhe Chen",
"Hao Xiang",
"Xin Xia",
"Lantao Liu",
"Jiaqi Ma"
] | Existing multi-agent perception systems assume that every agent utilizes the same model with identical parameters and architecture. The performance can be degraded with different perception models due to the mismatch in their confidence scores. In this work, we propose a model-agnostic multi-agent perception framework to reduce the negative effect caused by the model discrepancies without sharing ... |
Explainable Action Prediction through Self-Supervision on Scene Graphs | https://ieeexplore.ieee.org/document/10161132/ | [
"Pawit Kochakarn",
"Daniele De Martini",
"Daniel Omeiza",
"Lars Kunze",
"Pawit Kochakarn",
"Daniele De Martini",
"Daniel Omeiza",
"Lars Kunze"
] | This work explores scene graphs as a distilled representation of high-level information for autonomous driving, applied to future driver-action prediction. Given the scarcity and strong imbalance of data samples, we propose a self-supervision pipeline to infer representative and well-separated embeddings. Key aspects are interpretability and explainability; as such, we embed in our architecture at... |
CueCAn: Cue-driven Contextual Attention for Identifying Missing Traffic Signs on Unconstrained Roads | https://ieeexplore.ieee.org/document/10161576/ | [
"Varun Gupta",
"Anbumani Subramanian",
"C.V. Jawahar",
"Rohit Saluja",
"Varun Gupta",
"Anbumani Subramanian",
"C.V. Jawahar",
"Rohit Saluja"
] | Unconstrained Asian roads often involve poor infrastructure, affecting overall road safety. Missing traffic signs are a regular part of such roads. Missing or non-existing object detection has been studied for locating missing curbs and estimating reasonable regions for pedestrians on road scene images. Such methods involve analyzing task-specific single object cues. In this paper, we present the ... |
Tackling Clutter in Radar Data - Label Generation and Detection Using PointNet++ | https://ieeexplore.ieee.org/document/10160222/ | [
"Johannes Kopp",
"Dominik Kellner",
"Aldi Piroli",
"Klaus Dietmayer",
"Johannes Kopp",
"Dominik Kellner",
"Aldi Piroli",
"Klaus Dietmayer"
] | Radar sensors employed for environment perception, e.g. in autonomous vehicles, output a lot of unwanted clutter. These points, for which no corresponding real objects exist, are a major source of errors in following processing steps like object detection or tracking. We therefore present two novel neural network setups for identifying clutter. The input data, network architectures and training co... |
Effective Combination of Vertical, Longitudinal and Lateral Data for Vehicle Mass Estimation | https://ieeexplore.ieee.org/document/10160550/ | [
"Younesse El Mrhasli",
"Bruno Monsuez",
"Xavier Mouton",
"Younesse El Mrhasli",
"Bruno Monsuez",
"Xavier Mouton"
] | Real-time knowledge of the vehicle mass is valuable for several applications, mainly: active safety systems design and energy consumption optimization. This work describes a novel strategy for mass estimation in static and dynamic conditions. First, when the vehicle is powered-up, an initial estimation is given by observing the variations of one suspension deflection sensor mounted on the rear. Th... |
Receding Horizon Planning with Rule Hierarchies for Autonomous Vehicles | https://ieeexplore.ieee.org/document/10160622/ | [
"Sushant Veer",
"Karen Leung",
"Ryan K. Cosner",
"Yuxiao Chen",
"Peter Karkus",
"Marco Pavone",
"Sushant Veer",
"Karen Leung",
"Ryan K. Cosner",
"Yuxiao Chen",
"Peter Karkus",
"Marco Pavone"
] | Autonomous vehicles must often contend with conflicting planning requirements, e.g., safety and comfort could be at odds with each other if avoiding a collision calls for slamming the brakes. To resolve such conflicts, assigning importance ranking to rules (i.e., imposing a rule hierarchy) has been proposed, which, in turn, induces rankings on trajectories based on the importance of the rules they... |
Active Probing and Influencing Human Behaviors Via Autonomous Agents | https://ieeexplore.ieee.org/document/10161238/ | [
"Shuangge Wang",
"Yiwei Lyu",
"John M. Dolan",
"Shuangge Wang",
"Yiwei Lyu",
"John M. Dolan"
] | Autonomous agents (robots) face tremendous challenges while interacting with heterogeneous human agents in close proximity. One of these challenges is that the autonomous agent does not have an accurate model tailored to the specific human that the autonomous agent is interacting with, which could sometimes result in inefficient human-robot interaction and suboptimal system dynamics. Developing an... |
TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction | https://ieeexplore.ieee.org/document/10161243/ | [
"Zhejun Zhang",
"Alexander Liniger",
"Dengxin Dai",
"Fisher Yu",
"Luc Van Gool",
"Zhejun Zhang",
"Alexander Liniger",
"Dengxin Dai",
"Fisher Yu",
"Luc Van Gool"
] | Data-driven simulation has become a favorable way to train and test autonomous driving algorithms. The idea of replacing the actual environment with a learned simulator has also been explored in model-based reinforcement learning in the context of world models. In this work, we show data-driven traffic simulation can be formulated as a world model. We present TrafficBots, a multi-agent policy buil... |
SHAIL: Safety-Aware Hierarchical Adversarial Imitation Learning for Autonomous Driving in Urban Environments | https://ieeexplore.ieee.org/document/10161449/ | [
"Arec Jamgochian",
"Etienne Buehrle",
"Johannes Fischer",
"Mykel J. Kochenderfer",
"Arec Jamgochian",
"Etienne Buehrle",
"Johannes Fischer",
"Mykel J. Kochenderfer"
] | Designing a safe and human-like decision-making system for an autonomous vehicle is a challenging task. Generative imitation learning is one possible approach for automating policy-building by leveraging both real-world and simulated decisions. Previous work that applies generative imitation learning to autonomous driving policies focuses on learning a low-level controller for simple settings. How... |
Reinforcement Learning-Based Optimal Multiple Waypoint Navigation | https://ieeexplore.ieee.org/document/10160725/ | [
"Christos Vlachos",
"Panagiotis Rousseas",
"Charalampos P. Bechlioulis",
"Kostas J. Kyriakopoulos",
"Christos Vlachos",
"Panagiotis Rousseas",
"Charalampos P. Bechlioulis",
"Kostas J. Kyriakopoulos"
] | In this paper, a novel method based on Artificial Potential Field (APF) theory is presented, for optimal motion planning in fully-known, static workspaces, for multiple final goal configurations. Optimization is achieved through a Reinforcement Learning (RL) framework. More specifically, the parameters of the underlying potential field are adjusted through a policy gradient algorithm in order to m... |
DriveIRL: Drive in Real Life with Inverse Reinforcement Learning | https://ieeexplore.ieee.org/document/10160449/ | [
"Tung Phan-Minh",
"Forbes Howington",
"Ting-Sheng Chu",
"Momchil S. Tomov",
"Robert E. Beaudoin",
"Sang Uk Lee",
"Nanxiang Li",
"Caglayan Dicle",
"Samuel Findler",
"Francisco Suarez-Ruiz",
"Bo Yang",
"Sammy Omari",
"Eric M. Wolff",
"Tung Phan-Minh",
"Forbes Howington",
"Ting-Sheng Chu",
"Momchil S. Tomov",
"Robert E. Beaudoin",
"Sang Uk Lee",
"Nanxiang Li",
"Caglayan Dicle",
"Samuel Findler",
"Francisco Suarez-Ruiz",
"Bo Yang",
"Sammy Omari",
"Eric M. Wolff"
] | In this paper, we introduce the first published planner to drive a car in dense, urban traffic using Inverse Reinforcement Learning (IRL). Our planner, DriveIRL, generates a diverse set of trajectory proposals and scores them with a learned model. The best trajectory is tracked by our self-driving vehicle's low-level controller. We train our trajectory scoring model on a 500+ hour real-world datas... |
LES: Locally Exploitative Sampling for Robot Path Planning | https://ieeexplore.ieee.org/document/10160279/ | [
"Sagar Suhas Joshi",
"Seth Hutchinson",
"Panagiotis Tsiotras",
"Sagar Suhas Joshi",
"Seth Hutchinson",
"Panagiotis Tsiotras"
] | Sampling-based algorithms solve the path planning problem by generating random samples in the searchspace and incrementally growing a connectivity graph or a tree. Conventionally, the sampling strategy used in these algorithms is biased towards exploration to acquire information about the search-space. In contrast, this work proposes an optimization-based procedure that generates new samples so as... |
Boundary Conditions in Geodesic Motion Planning for Manipulators | https://ieeexplore.ieee.org/document/10160843/ | [
"Mario Laux",
"Andreas Zell",
"Mario Laux",
"Andreas Zell"
] | In dynamic environments, robotic manipulators and especially cobots must be able to react to changing circumstances while in motion. This substantiates the need for quick trajectory planning algorithms that are able to cope with arbitrary velocity and acceleration boundary conditions. Apart from dynamic re-planning, being able to seamlessly join trajectories together opens the door for divide-and-... |
TOFG: A Unified and Fine-Grained Environment Representation in Autonomous Driving | https://ieeexplore.ieee.org/document/10160476/ | [
"Zihao Wen",
"Yifan Zhang",
"Xinhong Chen",
"Jianping Wang",
"Zihao Wen",
"Yifan Zhang",
"Xinhong Chen",
"Jianping Wang"
] | In autonomous driving, an accurate understanding of environment, e.g., the vehicle-to-vehicle and vehicle-to-lane interactions, plays a critical role in many driving tasks such as trajectory prediction and motion planning. Environment information comes from high-definition (HD) map and historical trajectories of vehicles. Due to the heterogeneity of the map data and trajectory data, many data-driv... |
Unidirectional-Road-Network-Based Global Path Planning for Cleaning Robots in Semi-Structured Environments | https://ieeexplore.ieee.org/document/10161557/ | [
"Yong Li",
"Hui Cheng",
"Yong Li",
"Hui Cheng"
] | Practical global path planning is critical for commercializing cleaning robots working in semi-structured environments. In the literature, global path planning methods for free space usually focus on path length and neglect the traffic rule constraints of the environments, which leads to high-frequency re-planning and increases collision risks. In contrast, those for structured environments are de... |
A Hierarchical Decoupling Approach for Fast Temporal Logic Motion Planning | https://ieeexplore.ieee.org/document/10160744/ | [
"Ziyang Chen",
"Zhangli Zhou",
"Shaochen Wang",
"Zhen Kan",
"Ziyang Chen",
"Zhangli Zhou",
"Shaochen Wang",
"Zhen Kan"
] | Fast motion planning is of great significance, espe-cially when a timely mission is desired. However, the complexity of motion planning can grow drastically with the increase of environment details and mission complexity. This challenge can be further exacerbated if the tasks are coupled with the desired locations in the environment. To address these issues, this work aims at fast motion planning ... |
A fast two-stage approach for multi-goal path planning in a fruit tree | https://ieeexplore.ieee.org/document/10160281/ | [
"Werner Kroneman",
"João Valente",
"A. Frank Van Der Stappen",
"Werner Kroneman",
"João Valente",
"A. Frank Van Der Stappen"
] | We consider the problem of planning the motion of a drone equipped with a robotic arm, tasked with bringing its end-effector up to many (150+) targets in a fruit tree; to inspect every piece of fruit, for example. The task is complicated by the intersection of a version of Neighborhood TSP (to find an optimal order and a pose to visit every target), and a robotic motion-planning problem through a ... |
Online Whole-Body Motion Planning for Quadrotor using Multi-Resolution Search | https://ieeexplore.ieee.org/document/10160767/ | [
"Yunfan Ren",
"Siqi Liang",
"Fangcheng Zhu",
"Guozheng Lu",
"Fu Zhang",
"Yunfan Ren",
"Siqi Liang",
"Fangcheng Zhu",
"Guozheng Lu",
"Fu Zhang"
] | In this paper, we address the problem of online quadrotor whole-body motion planning (SE(3) planning) in unknown and unstructured environments. We propose a novel multi-resolution search method, which discovers narrow areas requiring full pose planning and normal areas requiring only position planning. As a consequence, a quadrotor planning problem is decomposed into several SE(3) (if necessary) a... |
Intermittent diffusion-based path planning for heterogeneous groups of mobile sensors in cluttered environments | https://ieeexplore.ieee.org/document/10161324/ | [
"Christina Frederick",
"Haomin Zhou",
"Frank Crosby",
"Christina Frederick",
"Haomin Zhou",
"Frank Crosby"
] | This paper presents a method for task-oriented path planning and collision avoidance for a group of heterogeneous holonomic mobile sensors. It is a generalization of the authors' prior work on diffusion-based path planning. The proposed variant allows one to plan paths in environments cluttered with obstacles. The agents follow flow dynamics, i.e., the negative gradient of a function that is the s... |
GANet: Goal Area Network for Motion Forecasting | https://ieeexplore.ieee.org/document/10160468/ | [
"Mingkun Wang",
"Xinge Zhu",
"Changqian Yu",
"Wei Li",
"Yuexin Ma",
"Ruochun Jin",
"Xiaoguang Ren",
"Dongchun Ren",
"Mingxu Wang",
"Wenjing Yang",
"Mingkun Wang",
"Xinge Zhu",
"Changqian Yu",
"Wei Li",
"Yuexin Ma",
"Ruochun Jin",
"Xiaoguang Ren",
"Dongchun Ren",
"Mingxu Wang",
"Wenjing Yang"
] | Predicting the future motion of road participants is crucial for autonomous driving but is extremely challenging due to staggering motion uncertainty. Recently, most motion forecasting methods resort to the goal-based strategy, i.e., predicting endpoints of motion trajectories as conditions to regress the entire trajectories, so that the search space of solution can be reduced. However, accurate g... |
FlowMap: Path Generation for Automated Vehicles in Open Space Using Traffic Flow | https://ieeexplore.ieee.org/document/10161326/ | [
"Wenchao Ding",
"Jieru Zhao",
"Yubin Chu",
"Haihui Huang",
"Tong Qin",
"Chunjing Xu",
"Yuxiang Guan",
"Zhongxue Gan",
"Wenchao Ding",
"Jieru Zhao",
"Yubin Chu",
"Haihui Huang",
"Tong Qin",
"Chunjing Xu",
"Yuxiang Guan",
"Zhongxue Gan"
] | There is extensive literature on perceiving road structures by fusing various sensor inputs such as lidar point clouds and camera images using deep neural nets. Leveraging the latest advance of neural architects (such as transformers) and bird-eye-view (BEV) representation, the road cognition accuracy keeps improving. However, how to cognize the “road” for automated vehicles where there is no well... |
An Architecture for Reactive Mobile Manipulation On-The-Move | https://ieeexplore.ieee.org/document/10161021/ | [
"Ben Burgess-Limerick",
"Chris Lehnert",
"Jürgen Leitner",
"Peter Corke",
"Ben Burgess-Limerick",
"Chris Lehnert",
"Jürgen Leitner",
"Peter Corke"
] | We present a generalised architecture for reactive mobile manipulation while a robot's base is in motion toward the next objective in a high-level task. By performing tasks on-the-move, overall cycle time is reduced compared to methods where the base pauses during manipulation. Reactive control of the manipulator enables grasping objects with unpredictable motion while improving robustness against... |
Multi-Robot Mission Planning in Dynamic Semantic Environments | https://ieeexplore.ieee.org/document/10160344/ | [
"Samarth Kalluraya",
"George J. Pappas",
"Yiannis Kantaros",
"Samarth Kalluraya",
"George J. Pappas",
"Yiannis Kantaros"
] | This paper addresses a new semantic multi-robot planning problem in uncertain and dynamic environments. Particularly, the environment is occupied with mobile and uncertain semantic targets. These targets are governed by stochastic dynamics while their current and future positions as well as their semantic labels are uncertain. Our goal is to control mobile sensing robots so that they can accomplis... |
A System for Generalized 3D Multi-Object Search | https://ieeexplore.ieee.org/document/10161387/ | [
"Kaiyu Zheng",
"Anirudha Paul",
"Stefanie Tellex",
"Kaiyu Zheng",
"Anirudha Paul",
"Stefanie Tellex"
] | Searching for objects is a fundamental skill for robots. As such, we expect object search to eventually become an off-the-shelf capability for robots, similar to e.g., object detection and SLAM. In contrast, however, no system for 3D object search exists that generalizes across real robots and environments. In this paper, building upon a recent theoretical framework that exploited the octree struc... |
A general class of combinatorial filters that can be minimized efficiently | https://ieeexplore.ieee.org/document/10160479/ | [
"Yulin Zhang",
"Dylan A. Shell",
"Yulin Zhang",
"Dylan A. Shell"
] | State minimization of combinatorial filters is a fundamental problem that arises, for example, in building cheap, resource-efficient robots. But exact minimization is known to be NP-hard. This paper conducts a more nuanced analysis of this hardness than up till now, and uncovers two factors which contribute to this complexity. We show each factor is a distinct source of the problem's hardness and ... |
Cautious Planning with Incremental Symbolic Perception: Designing Verified Reactive Driving Maneuvers | https://ieeexplore.ieee.org/document/10160960/ | [
"Disha Kamale",
"Sofie Haesaert",
"Cristian-Ioan Vasile",
"Disha Kamale",
"Sofie Haesaert",
"Cristian-Ioan Vasile"
] | This work presents a step towards utilizing incrementally-improving symbolic perception knowledge of the robot's surroundings for provably correct reactive control synthesis applied to an autonomous driving problem. Combining abstract models of motion control and information gathering, we show that assume-guarantee specifications (a subclass of Linear Temporal Logic) can be used to define and reso... |
Decision diagrams as plans: Answering observation-grounded queries | https://ieeexplore.ieee.org/document/10161530/ | [
"Dylan A. Shell",
"Jason M. O'Kane",
"Dylan A. Shell",
"Jason M. O'Kane"
] | We consider a robot that answers questions about its environment by traveling to appropriate places and then sensing. Questions are posed as structured queries and may involve conditional or contingent relationships between observable properties. After formulating this problem, and empha-sizing the advantages of exploiting deducible information, we describe how non-trivial knowledge of the world a... |
Obstacle avoidance using Raycasting and Riemannian Motion Policies at kHz rates for MAVs | https://ieeexplore.ieee.org/document/10161365/ | [
"Michael Pantic",
"Isar Meijer",
"Rik Bähnemann",
"Nikhilesh Alatur",
"Olov Andersson",
"Cesar Cadena",
"Roland Siegwart",
"Lionel Ott",
"Michael Pantic",
"Isar Meijer",
"Rik Bähnemann",
"Nikhilesh Alatur",
"Olov Andersson",
"Cesar Cadena",
"Roland Siegwart",
"Lionel Ott"
] | This paper presents a novel method for using Riemannian Motion Policies on volumetric maps, shown in the example of obstacle avoidance for Micro Aerial Vehicles (MAVs), Today, most robotic obstacle avoidance algorithms rely on sampling or optimization-based planners with volumetric maps. However, they are computationally expensive and often have inflexible monolithic architectures. Riemannian Moti... |
Adaptive and Explainable Deployment of Navigation Skills via Hierarchical Deep Reinforcement Learning | https://ieeexplore.ieee.org/document/10160371/ | [
"Kyowoon Lee",
"Seongun Kim",
"Jaesik Choi",
"Kyowoon Lee",
"Seongun Kim",
"Jaesik Choi"
] | For robotic vehicles to navigate robustly and safely in unseen environments, it is crucial to decide the most suitable navigation policy. However, most existing deep reinforcement learning based navigation policies are trained with a hand-engineered curriculum and reward function which are difficult to be deployed in a wide range of real-world scenarios. In this paper, we propose a framework to le... |
Learning Agile Flight Maneuvers: Deep SE(3) Motion Planning and Control for Quadrotors | https://ieeexplore.ieee.org/document/10160712/ | [
"Yixiao Wang",
"Bingheng Wang",
"Shenning Zhang",
"Han Wei Sia",
"Lin Zhao",
"Yixiao Wang",
"Bingheng Wang",
"Shenning Zhang",
"Han Wei Sia",
"Lin Zhao"
] | Agile flights of autonomous quadrotors in clut-tered environments require constrained motion planning and control subject to translational and rotational dynamics. Tra-ditional model-based methods typically demand complicated design and heavy computation. In this paper, we develop a novel deep reinforcement learning-based method that tackles the challenging task of flying through a dynamic narrow ... |
Robust MADER: Decentralized and Asynchronous Multiagent Trajectory Planner Robust to Communication Delay | https://ieeexplore.ieee.org/document/10161244/ | [
"Kota Kondo",
"Jesus Tordesillas",
"Reinaldo Figueroa",
"Juan Rached",
"Joseph Merkel",
"Parker C. Lusk",
"Jonathan P. How",
"Kota Kondo",
"Jesus Tordesillas",
"Reinaldo Figueroa",
"Juan Rached",
"Joseph Merkel",
"Parker C. Lusk",
"Jonathan P. How"
] | Although communication delays can disrupt multiagent systems, most of the existing multiagent trajectory planners lack a strategy to address this issue. State-of-the-art approaches typically assume perfect communication environments, which is hardly realistic in real-world experiments. This paper presents Robust MADER (RMADER), a decentralized and asynchronous multiagent trajectory planner that ca... |
Obstacle Identification and Ellipsoidal Decomposition for Fast Motion Planning in Unknown Dynamic Environments | https://ieeexplore.ieee.org/document/10160444/ | [
"Mehmetcan Kaymaz",
"Nazım Kemal Ure",
"Mehmetcan Kaymaz",
"Nazım Kemal Ure"
] | Collision avoidance in the presence of dynamic obstacles in unknown environments is one of the most critical challenges for unmanned systems. In this paper, we present a method that identifies obstacles in terms of ellipsoids to estimate linear and angular obstacle velocities. Our proposed method is based on the idea of any object can be approximately expressed by ellipsoids. To achieve this, we p... |
Safe Operations of an Aerial Swarm via a Cobot Human Swarm Interface | https://ieeexplore.ieee.org/document/10161343/ | [
"Sydrak S. Abdi",
"Derek A. Paley",
"Sydrak S. Abdi",
"Derek A. Paley"
] | Command and control of an aerial swarm is a complex task. This task increases in difficulty when the flight volume is restricted and the swarm and operator inhabit the same workspace. This work presents a novel method for interacting with and controlling a swarm of quadrotors in a confined space. EMG-based gesture control is used to control the position, orientation, and density of the swarm. Inte... |
MonoGraspNet: 6-DoF Grasping with a Single RGB Image | https://ieeexplore.ieee.org/document/10160779/ | [
"Guangyao Zhai",
"Dianye Huang",
"Shun-Cheng Wu",
"HyunJun Jung",
"Yan Di",
"Fabian Manhardt",
"Federico Tombari",
"Nassir Navab",
"Benjamin Busam",
"Guangyao Zhai",
"Dianye Huang",
"Shun-Cheng Wu",
"HyunJun Jung",
"Yan Di",
"Fabian Manhardt",
"Federico Tombari",
"Nassir Navab",
"Benjamin Busam"
] | 6-DoF robotic grasping is a long-lasting but un-solved problem. Recent methods utilize strong 3D networks to extract geometric grasping representations from depth sensors, demonstrating superior accuracy on common objects but performing unsatisfactorily on photometrically challenging objects, e.g., objects in transparent or reflective materials. The bottleneck lies in that the surface of these obj... |
USEEK: Unsupervised SE(3)-Equivariant 3D Keypoints for Generalizable Manipulation | https://ieeexplore.ieee.org/document/10160631/ | [
"Zhengrong Xue",
"Zhecheng Yuan",
"Jiashun Wang",
"Xueqian Wang",
"Yang Gao",
"Huazhe Xu",
"Zhengrong Xue",
"Zhecheng Yuan",
"Jiashun Wang",
"Xueqian Wang",
"Yang Gao",
"Huazhe Xu"
] | Can a robot manipulate intra-category unseen objects in arbitrary poses with the help of a mere demonstration of grasping pose on a single object instance? In this paper, we try to address this intriguing challenge by using USEEK, an unsupervised SE(3)-equivariant keypoints method that enjoys alignment across instances in a category, to perform generaliz-able manipulation. USEEK follows a teacher-... |
Semantic Mapping with Confidence Scores through Metric Embeddings and Gaussian Process Classification | https://ieeexplore.ieee.org/document/10161342/ | [
"Jungseok Hong",
"Suveer Garg",
"Volkan Isler",
"Jungseok Hong",
"Suveer Garg",
"Volkan Isler"
] | Recent advances in robotic mapping enable robots to use both semantic and geometric understanding of their surroundings to perform complex tasks. Current methods are optimized for reconstruction quality, but they do not provide a measure of how certain they are of their outputs. Therefore, algorithms that use these maps do not have a way of assessing how much they can trust the outputs. We present... |
The Third Generation (G3) Dual-Modal and Dual Sensing Mechanisms (DMDSM) Pretouch Sensor for Robotic Grasping | https://ieeexplore.ieee.org/document/10161337/ | [
"Cheng Fang",
"Shuangliang Li",
"Di Wang",
"Fengzhi Guo",
"Dezhen Song",
"Jun Zou",
"Cheng Fang",
"Shuangliang Li",
"Di Wang",
"Fengzhi Guo",
"Dezhen Song",
"Jun Zou"
] | Fingertip-mounted pretouch sensors are very useful for robotic grasping. In this paper, we report a new (G3) dual-modal and dual sensing mechanisms (DMDSM) pretouch sensor for near-distance ranging and material sensing, which is based on pulse-echo ultrasound (US) and optoacoustics (OA). Different from previously reported versions, the G3 sensor utilizes a self-focused US/OA transceiver, thereby e... |
Learning Height for Top-Down Grasps with the DIGIT Sensor | https://ieeexplore.ieee.org/document/10160955/ | [
"Thais Bernardi",
"Yoann Fleytoux",
"Jean-Baptiste Mouret",
"Serena Ivaldi",
"Thais Bernardi",
"Yoann Fleytoux",
"Jean-Baptiste Mouret",
"Serena Ivaldi"
] | We address the problem of grasping unknown objects identified from top-down images with a parallel gripper. When no object 3D model is available, the state-of-the-art grasp generators identify the best candidate locations for planar grasps using the RGBD image. However, while they generate the Cartesian location and orientation of the gripper, the height of the grasp center is often determined by ... |
Instance-wise Grasp Synthesis for Robotic Grasping | https://ieeexplore.ieee.org/document/10161149/ | [
"Yucheng Xu",
"Mohammadreza Kasaei",
"Hamidreza Kasaei",
"Zhibin Li",
"Yucheng Xu",
"Mohammadreza Kasaei",
"Hamidreza Kasaei",
"Zhibin Li"
] | Generating high-quality instance-wise grasp con-figurations provides critical information of how to grasp specific objects in a multi-object environment and is of high importance for robot manipulation tasks. This work proposed a novel Single-Stage Grasp (SSG) synthesis network, which performs high-quality instance-wise grasp synthesis in a single stage: instance mask and grasp configurations are ... |
Joint Segmentation and Grasp Pose Detection with Multi-Modal Feature Fusion Network | https://ieeexplore.ieee.org/document/10160253/ | [
"Xiaozheng Liu",
"Yunzhou Zhang",
"He Cao",
"Dexing Shan",
"Jiaqi Zhao",
"Xiaozheng Liu",
"Yunzhou Zhang",
"He Cao",
"Dexing Shan",
"Jiaqi Zhao"
] | Efficient grasp pose detection is essential for robotic manipulation in cluttered scenes. However, most methods only utilize point clouds or images for prediction, ignoring the advantages of different features. In this paper, we present a multi-modal fusion network for joint segmentation and grasp pose detection. We design a point cloud and image co-guided feature fusion module that can be used to... |
GraspNeRF: Multiview-based 6-DoF Grasp Detection for Transparent and Specular Objects Using Generalizable NeRF | https://ieeexplore.ieee.org/document/10160842/ | [
"Qiyu Dai",
"Yan Zhu",
"Yiran Geng",
"Ciyu Ruan",
"Jiazhao Zhang",
"He Wang",
"Qiyu Dai",
"Yan Zhu",
"Yiran Geng",
"Ciyu Ruan",
"Jiazhao Zhang",
"He Wang"
] | In this work, we tackle 6-DoF grasp detection for transparent and specular objects, which is an important yet challenging problem in vision-based robotic systems, due to the failure of depth cameras in sensing their geometry. We, for the first time, propose a multiview RGB-based 6-DoF grasp detection network, GraspNeRF, that leverages the generalizable neural radiance field (NeRF) to achieve mater... |
Elastic Context: Encoding Elasticity for Data-driven Models of Textiles Elastic Context: Encoding Elasticity for Data-driven Models of Textiles | https://ieeexplore.ieee.org/document/10160740/ | [
"Alberta Longhini",
"Marco Moletta",
"Alfredo Reichlin",
"Michael C. Welle",
"Alexander Kravberg",
"Yufei Wang",
"David Held",
"Zackory Erickson",
"Danica Kragic",
"Alberta Longhini",
"Marco Moletta",
"Alfredo Reichlin",
"Michael C. Welle",
"Alexander Kravberg",
"Yufei Wang",
"David Held",
"Zackory Erickson",
"Danica Kragic"
] | Physical interaction with textiles, such as assistive dressing or household tasks, requires advanced dexterous skills. The complexity of textile behavior during stretching and pulling is influenced by the material properties of the yarn and by the textile's construction technique, which are often unknown in real-world settings. Moreover, identification of physical properties of textiles through se... |
Vision-based Six-Dimensional Peg-in-Hole for Practical Connector Insertion | https://ieeexplore.ieee.org/document/10161116/ | [
"Kun Zhang",
"Chen Wang",
"Hua Chen",
"Jia Pan",
"Michael Yu Wang",
"Wei Zhang",
"Kun Zhang",
"Chen Wang",
"Hua Chen",
"Jia Pan",
"Michael Yu Wang",
"Wei Zhang"
] | We study six-dimensional (6D) perceptive peg-in-hole problem for practical connector insertion task in this paper. To enable the manipulator system to handle different types of pegs in complex environment, we develop a perceptive robotic assembly system that utilizes an in-hand RGB-D camera for peg-in-hole with multiple types of pegs. The proposed framework addresses the critical hole detection an... |
RGB-Only Reconstruction of Tabletop Scenes for Collision-Free Manipulator Control | https://ieeexplore.ieee.org/document/10160247/ | [
"Zhenggang Tang",
"Balakumar Sundaralingam",
"Jonathan Tremblay",
"Bowen Wen",
"Ye Yuan",
"Stephen Tyree",
"Charles Loop",
"Alexander Schwing",
"Stan Birchfield",
"Zhenggang Tang",
"Balakumar Sundaralingam",
"Jonathan Tremblay",
"Bowen Wen",
"Ye Yuan",
"Stephen Tyree",
"Charles Loop",
"Alexander Schwing",
"Stan Birchfield"
] | We present a system for collision-free control of a robot manipulator that uses only RGB views of the world. Perceptual input of a tabletop scene is provided by multiple images of an RGB camera (without depth) that is either handheld or mounted on the robot end effector. A NeRF-like process is used to reconstruct the 3D geometry of the scene, from which the Euclidean full signed distance function ... |
Multi-view object pose estimation from correspondence distributions and epipolar geometry | https://ieeexplore.ieee.org/document/10161514/ | [
"Rasmus Laurvig Haugaard",
"Thorbjorn Mosekjaer Iversen",
"Rasmus Laurvig Haugaard",
"Thorbjorn Mosekjaer Iversen"
] | In many automation tasks involving manipulation of rigid objects, the poses of the objects must be acquired. Vision-based pose estimation using a single RGB or RGB-D sensor is especially popular due to its broad applicability. However, single-view pose estimation is inherently limited by depth ambiguity and ambiguities imposed by various phenom-ena like occlusion, self-occlusion, reflections, etc.... |
FSG-Net: a Deep Learning model for Semantic Robot Grasping through Few-Shot Learning | https://ieeexplore.ieee.org/document/10160618/ | [
"Leonardo Barcellona",
"Alberto Bacchin",
"Alberto Gottardi",
"Emanuele Menegatti",
"Stefano Ghidoni",
"Leonardo Barcellona",
"Alberto Bacchin",
"Alberto Gottardi",
"Emanuele Menegatti",
"Stefano Ghidoni"
] | Robot grasping has been widely studied in the last decade. Recently, Deep Learning made possible to achieve remarkable results in grasp pose estimation, using depth and RGB images. However, only few works consider the choice of the object to grasp. Moreover, they require a huge amount of data for generalizing to unseen object categories. For this reason, we introduce the Few-shot Semantic Grasping... |
Learning Pre-Grasp Manipulation of Flat Objects in Cluttered Environments using Sliding Primitives | https://ieeexplore.ieee.org/document/10160869/ | [
"Jiaxi Wu",
"Haoran Wu",
"Shanlin Zhong",
"Quqin Sun",
"Yinlin Li",
"Jiaxi Wu",
"Haoran Wu",
"Shanlin Zhong",
"Quqin Sun",
"Yinlin Li"
] | Flat objects with negligible thicknesses like books and disks are challenging to be grasped by the robot because of the width limit of the robot's gripper, especially when they are in cluttered environments. Pre-grasp manipulation is conducive to rearranging objects on the table and moving the flat objects to the table edge, making them graspable. In this paper, we formulate this task as Parameter... |
Learning Category-Level Manipulation Tasks from Point Clouds with Dynamic Graph CNNs | https://ieeexplore.ieee.org/document/10160820/ | [
"Junchi Liang",
"Abdeslam Boularias",
"Junchi Liang",
"Abdeslam Boularias"
] | This paper presents a new technique for learning category-level manipulation from raw RGB-D videos of task demonstrations, with no manual labels or annotations. Category-level learning aims to acquire skills that can be generalized to new objects, with geometries and textures that are different from the ones of the objects used in the demonstrations. We address this problem by first viewing both g... |
Neural Grasp Distance Fields for Robot Manipulation | https://ieeexplore.ieee.org/document/10160217/ | [
"Thomas Weng",
"David Held",
"Franziska Meier",
"Mustafa Mukadam",
"Thomas Weng",
"David Held",
"Franziska Meier",
"Mustafa Mukadam"
] | We formulate grasp learning as a neural field and present Neural Grasp Distance Fields (NGDF). Here, the input is a 6D pose of a robot end effector and output is a distance to a continuous manifold of valid grasps for an object. In contrast to current approaches that predict a set of discrete candidate grasps, the distance-based NGDF representation is easily interpreted as a cost, and minimizing t... |
Planning for Multi-Object Manipulation with Graph Neural Network Relational Classifiers | https://ieeexplore.ieee.org/document/10161204/ | [
"Yixuan Huang",
"Adam Conkey",
"Tucker Hermans",
"Yixuan Huang",
"Adam Conkey",
"Tucker Hermans"
] | Objects rarely sit in isolation in human environments. As such, we'd like our robots to reason about how multiple objects relate to one another and how those relations may change as the robot interacts with the world. To this end, we propose a novel graph neural network framework for multi-object manipulation to predict how inter-object relations change given robot actions. Our model operates on p... |
Local Neural Descriptor Fields: Locally Conditioned Object Representations for Manipulation | https://ieeexplore.ieee.org/document/10160423/ | [
"Ethan Chun",
"Yilun Du",
"Anthony Simeonov",
"Tomas Lozano-Perez",
"Leslie Kaelbling",
"Ethan Chun",
"Yilun Du",
"Anthony Simeonov",
"Tomas Lozano-Perez",
"Leslie Kaelbling"
] | A robot operating in a household environment will see a wide range of unique and unfamiliar objects. While a system could train on many of these, it is infeasible to predict all the objects a robot will see. In this paper, we present a method to generalize object manipulation skills acquired from a limited number of demonstrations, to novel objects from unseen shape categories. Our approach, Local... |
Practical Visual Deep Imitation Learning via Task-Level Domain Consistency | https://ieeexplore.ieee.org/document/10161202/ | [
"Mohi Khansari",
"Daniel Ho",
"Yuqing Du",
"Armando Fuentes",
"Matthew Bennice",
"Nicolas Sievers",
"Sean Kirmani",
"Yunfei Bai",
"Eric Jang",
"Mohi Khansari",
"Daniel Ho",
"Yuqing Du",
"Armando Fuentes",
"Matthew Bennice",
"Nicolas Sievers",
"Sean Kirmani",
"Yunfei Bai",
"Eric Jang"
] | Recent work in visual end-to-end learning for robotics has shown the promise of imitation learning across a variety of tasks. Such approaches are however expensive both because they require large amounts of real world data and rely on time-consuming real-world evaluations to identify the best model for deployment. These challenges can be mitigated by using simulation evaluations to identify high p... |
SEIL: Simulation-augmented Equivariant Imitation Learning | https://ieeexplore.ieee.org/document/10161252/ | [
"Mingxi Jia",
"Dian Wang",
"Guanang Su",
"David Klee",
"Xupeng Zhu",
"Robin Walters",
"Robert Platt",
"Mingxi Jia",
"Dian Wang",
"Guanang Su",
"David Klee",
"Xupeng Zhu",
"Robin Walters",
"Robert Platt"
] | In robotic manipulation, acquiring samples is extremely expensive because it often requires interacting with the real world. Traditional image-level data augmentation has shown the potential to improve sample efficiency in various machine learning tasks. However, image-level data augmentation is insufficient for an imitation learning agent to learn good manipulation policies in a reasonable amount... |
Dextrous Tactile In-Hand Manipulation Using a Modular Reinforcement Learning Architecture | https://ieeexplore.ieee.org/document/10160756/ | [
"Johannes Pitz",
"Lennart Röstel",
"Leon Sievers",
"Berthold Bäuml",
"Johannes Pitz",
"Lennart Röstel",
"Leon Sievers",
"Berthold Bäuml"
] | Dextrous in-hand manipulation with a multi-fingered robotic hand is a challenging task, esp. when performed with the hand oriented upside down, demanding permanent force-closure, and when no external sensors are used. For the task of reorienting an object to a given goal orientation (vs. infinitely spinning it around an axis), the lack of external sensors is an additional fundamental challenge as ... |
Learning Tool Morphology for Contact-Rich Manipulation Tasks with Differentiable Simulation | https://ieeexplore.ieee.org/document/10161453/ | [
"Mengxi Li",
"Rika Antonova",
"Dorsa Sadigh",
"Jeannette Bohg",
"Mengxi Li",
"Rika Antonova",
"Dorsa Sadigh",
"Jeannette Bohg"
] | When humans perform contact-rich manipulation tasks, customized tools are often necessary to simplify the task. For instance, we use various utensils for handling food, such as knives, forks and spoons. Similarly, robots may benefit from specialized tools that enable them to more easily complete a variety of tasks. We present an end-to-end framework to automatically learn tool morphology for conta... |
CabiNet: Scaling Neural Collision Detection for Object Rearrangement with Procedural Scene Generation | https://ieeexplore.ieee.org/document/10161528/ | [
"Adithyavairavan Murali",
"Arsalan Mousavian",
"Clemens Eppner",
"Adam Fishman",
"Dieter Fox",
"Adithyavairavan Murali",
"Arsalan Mousavian",
"Clemens Eppner",
"Adam Fishman",
"Dieter Fox"
] | We address the important problem of generalizing robotic rearrangement to clutter without any explicit object models. We first generate over 650K cluttered scenes-orders of magnitude more than prior work-in diverse everyday environments, such as cabinets and shelves. We render synthetic partial point clouds from this data and use it to train our CabiNet model architecture. CabiNet is a collision m... |
NIFT: Neural Interaction Field and Template for Object Manipulation | https://ieeexplore.ieee.org/document/10160666/ | [
"Zeyu Huang",
"Juzhan Xu",
"Sisi Dai",
"Kai Xu",
"Hao Zhang",
"Hui Huang",
"Ruizhen Hu",
"Zeyu Huang",
"Juzhan Xu",
"Sisi Dai",
"Kai Xu",
"Hao Zhang",
"Hui Huang",
"Ruizhen Hu"
] | We introduce NIFT, Neural Interaction Field and Template, a descriptive and robust interaction representation of object manipulations to facilitate imitation learning. Given a few object manipulation demos, NIFT guides the generation of the interaction imitation for a new object instance by matching the Neural Interaction Template (NIT) extracted from the demos in the target Neural Interaction Fie... |
Place Recognition under Occlusion and Changing Appearance via Disentangled Representations | https://ieeexplore.ieee.org/document/10160506/ | [
"Yue Chen",
"Xingyu Chen",
"Yicen Li",
"Yue Chen",
"Xingyu Chen",
"Yicen Li"
] | Place recognition is a critical and challenging task for mobile robots, aiming to retrieve an image captured at the same place as a query image from a database. Existing methods tend to fail while robots move autonomously under occlusion (e.g., car, bus, truck) and changing appearance (e.g., illumination changes, seasonal variation). Because they encode the image into only one code, entangling pla... |
GIDP: Learning a Good Initialization and Inducing Descriptor Post-enhancing for Large-scale Place Recognition | https://ieeexplore.ieee.org/document/10160415/ | [
"Zhaoxin Fan",
"Zhenbo Song",
"Hongyan Liu",
"Jun He",
"Zhaoxin Fan",
"Zhenbo Song",
"Hongyan Liu",
"Jun He"
] | Large-scale place recognition is a fundamental but challenging task, which plays an increasingly important role in autonomous driving and robotics. Existing methods have achieved acceptable good performance, however, most of them are concentrating on designing elaborate global descriptor learning network structures. The importance of feature generalization and descriptor post-enhancing has long be... |
STD: Stable Triangle Descriptor for 3D place recognition | https://ieeexplore.ieee.org/document/10160413/ | [
"Chongjian Yuan",
"Jiarong Lin",
"Zuhao Zou",
"Xiaoping Hong",
"Fu Zhang",
"Chongjian Yuan",
"Jiarong Lin",
"Zuhao Zou",
"Xiaoping Hong",
"Fu Zhang"
] | In this work, we present a novel global descriptor termed stable triangle descriptor (STD) for 3D place recognition. For a triangle, its shape is uniquely determined by the length of the sides or included angles. Moreover, the shape of triangles is completely invariant to rigid transformations. Based on this property, we first design an algorithm to efficiently extract local key points from the 3D... |
DeepRING: Learning Roto-translation Invariant Representation for LiDAR based Place Recognition | https://ieeexplore.ieee.org/document/10161435/ | [
"Sha Lu",
"Xuecheng Xu",
"Li Tang",
"Rong Xiong",
"Yue Wang",
"Sha Lu",
"Xuecheng Xu",
"Li Tang",
"Rong Xiong",
"Yue Wang"
] | LiDAR based place recognition is popular for loop closure detection and re-localization. In recent years, deep learning brings improvements to place recognition by learnable feature extraction. However, these methods degenerate when the robot re-visits previous places with a large perspective difference. To address the challenge, we propose DeepRING to learn the roto-translation invariant represen... |
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