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SeqSphereVLAD: Sequence Matching Enhanced Orientation-invariant Place Recognition | https://ieeexplore.ieee.org/document/9341727/ | [
"Peng Yin",
"Fuying Wang",
"Anton Egorov",
"Jiafan Hou",
"Ji Zhang",
"Howie Choset",
"Peng Yin",
"Fuying Wang",
"Anton Egorov",
"Jiafan Hou",
"Ji Zhang",
"Howie Choset"
] | Human beings and animals are capable of recognizing places from a previous journey when viewing them under different environmental conditions (e.g., illuminations and weathers). This paper seeks to provide robots with a human-like place recognition ability using a new point cloud feature learning method. This is a challenging problem due to the difficulty of extracting invariant local descriptors ... |
Online Visual Place Recognition via Saliency Re-identification | https://ieeexplore.ieee.org/document/9341703/ | [
"Han Wang",
"Chen Wang",
"Lihua Xie",
"Han Wang",
"Chen Wang",
"Lihua Xie"
] | As an essential component of visual simultaneous localization and mapping (SLAM), place recognition is crucial for robot navigation and autonomous driving. Existing methods often formulate visual place recognition as feature matching, which is computationally expensive for many robotic applications with limited computing power, e.g., autonomous driving and cleaning robot. Inspired by the fact that... |
ARAS: Ambiguity-aware Robust Active SLAM based on Multi-hypothesis State and Map Estimations | https://ieeexplore.ieee.org/document/9341384/ | [
"Ming Hsiao",
"Joshua G. Mangelson",
"Sudharshan Suresh",
"Christian Debrunner",
"Michael Kaess",
"Ming Hsiao",
"Joshua G. Mangelson",
"Sudharshan Suresh",
"Christian Debrunner",
"Michael Kaess"
] | In this paper, we introduce an ambiguity-aware robust active SLAM (ARAS) framework that makes use of multi-hypothesis state and map estimations to achieve better robustness. Ambiguous measurements can result in multiple probable solutions in a multi-hypothesis SLAM (MH-SLAM) system if they are temporarily unsolvable (due to insufficient information), our ARAS aims at taking all these probable esti... |
On-plate localization and mapping for an inspection robot using ultrasonic guided waves: a proof of concept | https://ieeexplore.ieee.org/document/9340936/ | [
"Cédric Pradalier",
"Othmane-Latif Ouabi",
"Pascal Pomarede",
"Jan Steckel",
"Cédric Pradalier",
"Othmane-Latif Ouabi",
"Pascal Pomarede",
"Jan Steckel"
] | This paper presents a proof-of-concept for a localization and mapping system for magnetic crawlers performing inspection tasks on structures made of large metal plates. By relying on ultrasonic guided waves reflected from the plate edges, we show that it is possible to recover the plate geometry and robot trajectory to a precision comparable to the signal wavelength. The approach is tested using r... |
Plug-and-Play SLAM: A Unified SLAM Architecture for Modularity and Ease of Use | https://ieeexplore.ieee.org/document/9341611/ | [
"Mirco Colosi",
"Irvin Aloise",
"Tiziano Guadagnino",
"Dominik Schlegel",
"Bartolomeo Della Corte",
"Kai O. Arras",
"Giorgio Grisetti",
"Mirco Colosi",
"Irvin Aloise",
"Tiziano Guadagnino",
"Dominik Schlegel",
"Bartolomeo Della Corte",
"Kai O. Arras",
"Giorgio Grisetti"
] | Simultaneous Localization and Mapping (SLAM) is considered a mature research field with numerous applications and publicly available open-source systems. Despite this maturity, existing SLAM systems often rely on ad-hoc implementations or are tailored to predefined sensor setups. In this work, we tackle these issues, proposing a novel unified SLAM architecture specifically designed to standardize ... |
Majorization Minimization Methods for Distributed Pose Graph Optimization with Convergence Guarantees | https://ieeexplore.ieee.org/document/9341063/ | [
"Taosha Fan",
"Todd Murphey",
"Taosha Fan",
"Todd Murphey"
] | In this paper, we consider the problem of distributed pose graph optimization (PGO) that has extensive applications in multi-robot simultaneous localization and mapping (SLAM). We propose majorization minimization methods for distributed PGO and show that our methods are guaranteed to converge to first-order critical points under mild conditions. Furthermore, since our methods rely a proximal oper... |
Variational Filtering with Copula Models for SLAM | https://ieeexplore.ieee.org/document/9341404/ | [
"John D. Martin",
"Kevin Doherty",
"Caralyn Cyr",
"Brendan Englot",
"John Leonard",
"John D. Martin",
"Kevin Doherty",
"Caralyn Cyr",
"Brendan Englot",
"John Leonard"
] | The ability to infer map variables and estimate pose is crucial to the operation of autonomous mobile robots. In most cases the shared dependency between these variables is modeled through a multivariate Gaussian distribution, but there are many situations where that assumption is unrealistic. Our paper shows how it is possible to relax this assumption and perform simultaneous localization and map... |
A Theory of Fermat Paths for 3D Imaging Sonar Reconstruction | https://ieeexplore.ieee.org/document/9341613/ | [
"Eric Westman",
"Ioannis Gkioulekas",
"Michael Kaess",
"Eric Westman",
"Ioannis Gkioulekas",
"Michael Kaess"
] | In this work, we present a novel method for reconstructing particular 3D surface points using an imaging sonar sensor. We derive the two-dimensional Fermat flow equation, which may be applied to the planes defined by each discrete azimuth angle in the sonar image. We show that the Fermat flow equation applies to boundary points and surface points which correspond to specular reflections within the... |
Tightly-coupled Fusion of Global Positional Measurements in Optimization-based Visual-Inertial Odometry | https://ieeexplore.ieee.org/document/9341697/ | [
"Giovanni Cioffi",
"Davide Scaramuzza",
"Giovanni Cioffi",
"Davide Scaramuzza"
] | Motivated by the goal of achieving robust, drift-free pose estimation in long-term autonomous navigation, in this work we propose a methodology to fuse global positional information with visual and inertial measurements in a tightly-coupled nonlinear-optimization-based estimator. Differently from previous works, which are loosely-coupled, the use of a tightly-coupled approach allows exploiting the... |
GR-SLAM: Vision-Based Sensor Fusion SLAM for Ground Robots on Complex Terrain | https://ieeexplore.ieee.org/document/9341387/ | [
"Yun Su",
"Ting Wang",
"Chen Yao",
"Shiliang Shao",
"Zhidong Wang",
"Yun Su",
"Ting Wang",
"Chen Yao",
"Shiliang Shao",
"Zhidong Wang"
] | In recent years, many excellent SLAM methods based on cameras, especially the camera-IMU fusion (VIO), have emerged, which has greatly improved the accuracy and robustness of SLAM. However, we find through experiments that most of the existing VIO methods perform well on drones or drone datasets, but for ground robots on complex terrain, they cannot continuously provide accurate and robust localiz... |
OrcVIO: Object residual constrained Visual-Inertial Odometry | https://ieeexplore.ieee.org/document/9341660/ | [
"Mo Shan",
"Qiaojun Feng",
"Nikolay Atanasov",
"Mo Shan",
"Qiaojun Feng",
"Nikolay Atanasov"
] | Introducing object-level semantic information into simultaneous localization and mapping (SLAM) system is critical. It not only improves the performance but also enables tasks specified in terms of meaningful objects. This work presents OrcVIO, for visual-inertial odometry tightly coupled with tracking and optimization over structured object models. OrcVIO differentiates through semantic feature a... |
LIC-Fusion 2.0: LiDAR-Inertial-Camera Odometry with Sliding-Window Plane-Feature Tracking | https://ieeexplore.ieee.org/document/9340704/ | [
"Xingxing Zuo",
"Yulin Yang",
"Patrick Geneva",
"Jiajun Lv",
"Yong Liu",
"Guoquan Huang",
"Marc Pollefeys",
"Xingxing Zuo",
"Yulin Yang",
"Patrick Geneva",
"Jiajun Lv",
"Yong Liu",
"Guoquan Huang",
"Marc Pollefeys"
] | Multi-sensor fusion of multi-modal measurements from commodity inertial, visual and LiDAR sensors to provide robust and accurate 6DOF pose estimation holds great potential in robotics and beyond. In this paper, building upon our prior work (i.e., LIC-Fusion), we develop a sliding-window filter based LiDAR-Inertial-Camera odometry with online spatiotemporal calibration (i.e., LIC-Fusion 2.0), which... |
Leveraging Planar Regularities for Point Line Visual-Inertial Odometry | https://ieeexplore.ieee.org/document/9341278/ | [
"Xin Li",
"Yijia He",
"Jinlong Lin",
"Xiao Liu",
"Xin Li",
"Yijia He",
"Jinlong Lin",
"Xiao Liu"
] | With monocular Visual-Inertial Odometry (VIO) system, 3D point cloud and camera motion can be estimated simultaneously. Because pure sparse 3D points provide a structureless representation of the environment, generating 3D mesh from sparse points can further model the environment topology and produce dense mapping. To improve the accuracy of 3D mesh generation and localization, we propose a tightl... |
SplitFusion: Simultaneous Tracking and Mapping for Non-Rigid Scenes | https://ieeexplore.ieee.org/document/9341082/ | [
"Yang Li",
"Tianwei Zhang",
"Yoshihiko Nakamura",
"Tatsuya Harada",
"Yang Li",
"Tianwei Zhang",
"Yoshihiko Nakamura",
"Tatsuya Harada"
] | We present SplitFusion, a novel dense RGB-D SLAM framework that simultaneously performs tracking and dense reconstruction for both rigid and non-rigid components of the scene. SplitFusion first adopts deep learning based semantic instant segmentation technique to split the scene into rigid or non-rigid surfaces. The split surfaces are independently tracked via rigid or non-rigid ICP and reconstruc... |
LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping | https://ieeexplore.ieee.org/document/9341176/ | [
"Tixiao Shan",
"Brendan Englot",
"Drew Meyers",
"Wei Wang",
"Carlo Ratti",
"Daniela Rus",
"Tixiao Shan",
"Brendan Englot",
"Drew Meyers",
"Wei Wang",
"Carlo Ratti",
"Daniela Rus"
] | We propose a framework for tightly-coupled lidar inertial odometry via smoothing and mapping, LIO-SAM, that achieves highly accurate, real-time mobile robot trajectory estimation and map-building. LIO-SAM formulates lidar-inertial odometry atop a factor graph, allowing a multitude of relative and absolute measurements, including loop closures, to be incorporated from different sources as factors i... |
LiTAMIN: LiDAR-based Tracking And Mapping by Stabilized ICP for Geometry Approximation with Normal Distributions | https://ieeexplore.ieee.org/document/9341341/ | [
"Masashi Yokozuka",
"Kenji Koide",
"Shuji Oishi",
"Atsuhiko Banno",
"Masashi Yokozuka",
"Kenji Koide",
"Shuji Oishi",
"Atsuhiko Banno"
] | This paper proposes a 3D LiDAR simultaneous localization and mapping (SLAM) method that improves accuracy, robustness, and computational efficiency for an iterative closest point (ICP) algorithm employing a locally approximated geometry with clusters of normal distributions. In comparison with previous normal distribution-based ICP methods, such as normal distribution transformation and generalize... |
GOSMatch: Graph-of-Semantics Matching for Detecting Loop Closures in 3D LiDAR data | https://ieeexplore.ieee.org/document/9341299/ | [
"Yachen Zhu",
"Yanyang Ma",
"Long Chen",
"Cong Liu",
"Maosheng Ye",
"Lingxi Li",
"Yachen Zhu",
"Yanyang Ma",
"Long Chen",
"Cong Liu",
"Maosheng Ye",
"Lingxi Li"
] | Detecting loop closures in 3D Light Detection and Ranging (LiDAR) data is a challenging task since point-level methods always suffer from instability. This paper presents a semantic-level approach named GOSMatch to perform reliable place recognition. Our method leverages novel descriptors, which are generated from the spatial relationship between semantics, to perform frame description and data as... |
Seed: A Segmentation-Based Egocentric 3D Point Cloud Descriptor for Loop Closure Detection | https://ieeexplore.ieee.org/document/9341517/ | [
"Yunfeng Fan",
"Yichang He",
"U-Xuan Tan",
"Yunfeng Fan",
"Yichang He",
"U-Xuan Tan"
] | Place recognition is essential for SLAM system since it is critical for loop closure and can help to correct the accumulated drift and result in a globally consistent map. Unlike the visual slam which can use diverse feature detection methods to describe the scene, there are limited works reported to represent a place using single LiDAR scan. In this paper, we propose a segmentation-based egocentr... |
RadarSLAM: Radar based Large-Scale SLAM in All Weathers | https://ieeexplore.ieee.org/document/9341287/ | [
"Ziyang Hong",
"Yvan Petillot",
"Sen Wang",
"Ziyang Hong",
"Yvan Petillot",
"Sen Wang"
] | Numerous Simultaneous Localization and Mapping (SLAM) algorithms have been presented in last decade using different sensor modalities. However, robust SLAM in extreme weather conditions is still an open research problem. In this paper, RadarSLAM, a full radar based graph SLAM system, is proposed for reliable localization and mapping in large-scale environments. It is composed of pose tracking, loc... |
GP-SLAM+: real-time 3D lidar SLAM based on improved regionalized Gaussian process map reconstruction | https://ieeexplore.ieee.org/document/9341028/ | [
"Jianyuan Ruan",
"Bo Li",
"Yinqiang Wang",
"Zhou Fang",
"Jianyuan Ruan",
"Bo Li",
"Yinqiang Wang",
"Zhou Fang"
] | This paper presents a 3D lidar SLAM system based on improved regionalized Gaussian process (GP) map reconstruction to provide both low-drift state estimation and mapping in real-time for robotics applications. We utilize spatial GP regression to model the environment. This tool enables us to recover surfaces including those in sparsely scanned areas and obtain uniform samples with uncertainty. Tho... |
Domain-Adversarial and -Conditional State Space Model for Imitation Learning | https://ieeexplore.ieee.org/document/9341705/ | [
"Ryo Okumura",
"Masashi Okada",
"Tadahiro Taniguchi",
"Ryo Okumura",
"Masashi Okada",
"Tadahiro Taniguchi"
] | State representation learning (SRL) in partially observable Markov decision processes has been studied to learn abstract features of data useful for robot control tasks. For SRL, acquiring domain-agnostic states is essential for achieving efficient imitation learning. Without these states, imitation learning is hampered by domain-dependent information useless for control. However, existing methods... |
Planning on the fast lane: Learning to interact using attention mechanisms in path integral inverse reinforcement learning | https://ieeexplore.ieee.org/document/9340636/ | [
"Sascha Rosbach",
"Xing Li",
"Simon Großjohann",
"Silviu Homoceanu",
"Stefan Roth",
"Sascha Rosbach",
"Xing Li",
"Simon Großjohann",
"Silviu Homoceanu",
"Stefan Roth"
] | General-purpose trajectory planning algorithms for automated driving utilize complex reward functions to perform a combined optimization of strategic, behavioral, and kinematic features. The specification and tuning of a single reward function is a tedious task and does not generalize over a large set of traffic situations. Deep learning approaches based on path integral inverse reinforcement lear... |
A Geometric Perspective on Visual Imitation Learning | https://ieeexplore.ieee.org/document/9341758/ | [
"Jun Jin",
"Laura Petrich",
"Masood Dehghan",
"Martin Jagersand",
"Jun Jin",
"Laura Petrich",
"Masood Dehghan",
"Martin Jagersand"
] | We consider the problem of visual imitation learning without human kinesthetic teaching or teleoperation, nor access to an interactive reinforcement learning training environment. We present a geometric perspective to this problem where geometric feature correspondences are learned from one training video and used to execute tasks via visual servoing. Specifically, we propose VGS-IL (Visual Geomet... |
Learn by Observation: Imitation Learning for Drone Patrolling from Videos of A Human Navigator | https://ieeexplore.ieee.org/document/9340691/ | [
"Yue Fan",
"Shilei Chu",
"Wei Zhang",
"Ran Song",
"Yibin Li",
"Yue Fan",
"Shilei Chu",
"Wei Zhang",
"Ran Song",
"Yibin Li"
] | We present an imitation learning method for autonomous drone patrolling based only on raw videos. Different from previous methods, we propose to let the drone learn patrolling in the air by observing and imitating how a human navigator does it on the ground. The observation process enables the automatic collection and annotation of data using inter-frame geometric consistency, resulting in less ma... |
Multi-Instance Aware Localization for End-to-End Imitation Learning | https://ieeexplore.ieee.org/document/9341185/ | [
"Sagar Gubbi Venkatesh",
"Raviteja Upadrashta",
"Shishir Kolathaya",
"Bharadwaj Amrutur",
"Sagar Gubbi Venkatesh",
"Raviteja Upadrashta",
"Shishir Kolathaya",
"Bharadwaj Amrutur"
] | Existing architectures for imitation learning using image-to-action policy networks perform poorly when presented with an input image containing multiple instances of the object of interest, especially when the number of expert demonstrations available for training are limited. We show that end-to-end policy networks can be trained in a sample efficient manner by (a) appending the feature map outp... |
ImitationFlow: Learning Deep Stable Stochastic Dynamic Systems by Normalizing Flows | https://ieeexplore.ieee.org/document/9341035/ | [
"Julen Urain",
"Michele Ginesi",
"Davide Tateo",
"Jan Peters",
"Julen Urain",
"Michele Ginesi",
"Davide Tateo",
"Jan Peters"
] | We introduce ImitationFlow, a novel Deep generative model that allows learning complex globally stable, stochastic, nonlinear dynamics. Our approach extends the Normalizing Flows framework to learn stable Stochastic Differential Equations. We prove the Lyapunov stability for a class of Stochastic Differential Equations and we propose a learning algorithm to learn them from a set of demonstrated tr... |
Standard Deep Generative Models for Density Estimation in Configuration Spaces: A Study of Benefits, Limits and Challenges | https://ieeexplore.ieee.org/document/9340994/ | [
"Robert Gieselmann",
"Florian T. Pokorny",
"Robert Gieselmann",
"Florian T. Pokorny"
] | Deep Generative Models such as Generative Adversarial Networks (GAN) and Variational Autoencoders (VAE) have found multiple applications in Robotics, with recent works suggesting the potential use of these methods as a generic solution for the estimation of sampling distributions for motion planning in parameterized sets of environments. In this work we provide a first empirical study of challenge... |
Progressive automation of periodic tasks on planar surfaces of unknown pose with hybrid force/position control | https://ieeexplore.ieee.org/document/9341374/ | [
"Fotios Dimeas",
"Zoe Doulgeri",
"Fotios Dimeas",
"Zoe Doulgeri"
] | This paper presents a teaching by demonstration method for contact tasks with periodic movement on planar surfaces of unknown pose. To learn the motion on the plane, we utilize frequency oscillators with periodic movement primitives and we propose modified adaptation rules along with an extraction method of the task’s fundamental frequency by automatically discarding near-zero frequency components... |
Learning Hybrid Object Kinematics for Efficient Hierarchical Planning Under Uncertainty | https://ieeexplore.ieee.org/document/9340749/ | [
"Ajinkya Jain",
"Scott Niekum",
"Ajinkya Jain",
"Scott Niekum"
] | Sudden changes in the dynamics of robotic tasks, such as contact with an object or the latching of a door, are often viewed as inconvenient discontinuities that make manipulation difficult. However, when these transitions are well-understood, they can be leveraged to reduce uncertainty or aid manipulation-for example, wiggling a screw to determine if it is fully inserted or not. Current model-free... |
Learning State-Dependent Losses for Inverse Dynamics Learning | https://ieeexplore.ieee.org/document/9341701/ | [
"Kristen Morse",
"Neha Das",
"Yixin Lin",
"Austin S. Wang",
"Akshara Rai",
"Franziska Meier",
"Kristen Morse",
"Neha Das",
"Yixin Lin",
"Austin S. Wang",
"Akshara Rai",
"Franziska Meier"
] | Being able to quickly adapt to changes in dynamics is paramount in model-based control for object manipulation tasks. In order to influence fast adaptation of the inverse dynamics model's parameters, data efficiency is crucial. Given observed data, a key element to how an optimizer updates model parameters is the loss function. In this work, we propose to apply meta-learning to learn structured, s... |
Fast Online Adaptation in Robotics through Meta-Learning Embeddings of Simulated Priors | https://ieeexplore.ieee.org/document/9341462/ | [
"Rituraj Kaushik",
"Timothée Anne",
"Jean-Baptiste Mouret",
"Rituraj Kaushik",
"Timothée Anne",
"Jean-Baptiste Mouret"
] | Meta-learning algorithms can accelerate the model-based reinforcement learning (MBRL) algorithms by finding an initial set of parameters for the dynamical model such that the model can be trained to match the actual dynamics of the system with only a few data-points. However, in the real world, a robot might encounter any situation starting from motor failures to finding itself in a rocky terrain ... |
Self-Adapting Recurrent Models for Object Pushing from Learning in Simulation | https://ieeexplore.ieee.org/document/9341076/ | [
"Lin Cong",
"Michael Grner",
"Philipp Ruppel",
"Hongzhuo Liang",
"Norman Hendrich",
"Jianwei Zhang",
"Lin Cong",
"Michael Grner",
"Philipp Ruppel",
"Hongzhuo Liang",
"Norman Hendrich",
"Jianwei Zhang"
] | Planar pushing remains a challenging research topic, where building the dynamic model of the interaction is the core issue. Even an accurate analytical dynamic model is inherently unstable because physics parameters such as inertia and friction can only be approximated. Data-driven models usually rely on large amounts of training data, but data collection is time consuming when working with real r... |
A Probabilistic Model for Planar Sliding of Objects with Unknown Material Properties: Identification and Robust Planning | https://ieeexplore.ieee.org/document/9341468/ | [
"Changkyu Song",
"Abdeslam Boularias",
"Changkyu Song",
"Abdeslam Boularias"
] | This paper introduces a new technique for learning probabilistic models of mass and friction distributions of unknown objects, and performing robust sliding actions by using the learned models. The proposed method is executed in two consecutive phases. In the exploration phase, a table-top object is poked by a robot from different angles. The observed motions of the object are compared against sim... |
Hindsight for Foresight: Unsupervised Structured Dynamics Models from Physical Interaction | https://ieeexplore.ieee.org/document/9341491/ | [
"Iman Nematollahi",
"Oier Mees",
"Lukas Hermann",
"Wolfram Burgard",
"Iman Nematollahi",
"Oier Mees",
"Lukas Hermann",
"Wolfram Burgard"
] | A key challenge for an agent learning to interact with the world is to reason about physical properties of objects and to foresee their dynamics under the effect of applied forces. In order to scale learning through interaction to many objects and scenes, robots should be able to improve their own performance from real-world experience without requiring human supervision. To this end, we propose a... |
Multi-Sparse Gaussian Process: Learning based Semi-Parametric Control | https://ieeexplore.ieee.org/document/9341506/ | [
"Mouhyemen Khan",
"Akash Patel",
"Abhijit Chatterjee",
"Mouhyemen Khan",
"Akash Patel",
"Abhijit Chatterjee"
] | A key challenge with controlling complex dynamical systems is to accurately model them. However, this requirement is very hard to satisfy in practice. Data-driven approaches such as Gaussian processes (GPs) have proved quite effective by employing regression based methods to capture the unmodeled dynamical effects. However, GPs scale cubically with number of data points n, and it is often a challe... |
Decentralized Deep Reinforcement Learning for a Distributed and Adaptive Locomotion Controller of a Hexapod Robot | https://ieeexplore.ieee.org/document/9341754/ | [
"Malte Schilling",
"Kai Konen",
"Frank W. Ohl",
"Timo Korthals",
"Malte Schilling",
"Kai Konen",
"Frank W. Ohl",
"Timo Korthals"
] | Locomotion is a prime example for adaptive behavior in animals and biological control principles have inspired control architectures for legged robots. While machine learning has been successfully applied to many tasks in recent years, Deep Reinforcement Learning approaches still appear to struggle when applied to real world robots in continuous control tasks and in particular do not appear as rob... |
First Steps: Latent-Space Control with Semantic Constraints for Quadruped Locomotion | https://ieeexplore.ieee.org/document/9340737/ | [
"Alexander L. Mitchell",
"Martin Engelcke",
"Oiwi Parker Jones",
"David Surovik",
"Siddhant Gangapurwala",
"Oliwier Melon",
"Ioannis Havoutis",
"Ingmar Posner",
"Alexander L. Mitchell",
"Martin Engelcke",
"Oiwi Parker Jones",
"David Surovik",
"Siddhant Gangapurwala",
"Oliwier Melon",
"Ioannis Havoutis",
"Ingmar Posner"
] | Traditional approaches to quadruped control frequently employ simplified, hand-derived models. This significantly reduces the capability of the robot since its effective kinematic range is curtailed. In addition, kinodynamic constraints are often non-differentiable and difficult to implement in an optimisation approach. In this work, these challenges are addressed by framing quadruped control as o... |
Stir to Pour: Efficient Calibration of Liquid Properties for Pouring Actions | https://ieeexplore.ieee.org/document/9340852/ | [
"Tatiana Lopez-Guevara",
"Rita Pucci",
"Nicholas K. Taylor",
"Michael U. Gutmann",
"Suhramanian Ramamoorthy",
"Kartic Suhr",
"Tatiana Lopez-Guevara",
"Rita Pucci",
"Nicholas K. Taylor",
"Michael U. Gutmann",
"Suhramanian Ramamoorthy",
"Kartic Suhr"
] | Humans use simple probing actions to develop intuition about the physical behavior of common objects. Such intuition is particularly useful for adaptive estimation of favorable manipulation strategies of those objects in novel contexts. For example, observing the effect of tilt on a transparent bottle containing an unknown liquid provides clues on how the liquid might be poured. It is desirable to... |
Haptic Knowledge Transfer Between Heterogeneous Robots using Kernel Manifold Alignment | https://ieeexplore.ieee.org/document/9340770/ | [
"Gyan Tatiya",
"Yash Shukla",
"Michael Edegware",
"Jivko Sinapov",
"Gyan Tatiya",
"Yash Shukla",
"Michael Edegware",
"Jivko Sinapov"
] | Humans learn about object properties using multiple modes of perception. Recent advances show that robots can use non-visual sensory modalities (i.e., haptic and tactile sensory data) coupled with exploratory behaviors (i.e., grasping, lifting, pushing, dropping, etc.) for learning objects' properties such as shape, weight, material and affordances. However, non-visual sensory representations cann... |
robo-gym – An Open Source Toolkit for Distributed Deep Reinforcement Learning on Real and Simulated Robots | https://ieeexplore.ieee.org/document/9340956/ | [
"Matteo Lucchi",
"Friedemann Zindler",
"Stephan Mühlbacher-Karrer",
"Horst Pichler",
"Matteo Lucchi",
"Friedemann Zindler",
"Stephan Mühlbacher-Karrer",
"Horst Pichler"
] | Applying Deep Reinforcement Learning (DRL) to complex tasks in the field of robotics has proven to be very successful in the recent years. However, most of the publications focus either on applying it to a task in simulation or to a task in a real world setup. Although there are great examples of combining the two worlds with the help of transfer learning, it often requires a lot of additional wor... |
Crossing the Gap: A Deep Dive into Zero-Shot Sim-to-Real Transfer for Dynamics | https://ieeexplore.ieee.org/document/9341617/ | [
"Eugene Valassakis",
"Zihan Ding",
"Edward Johns",
"Eugene Valassakis",
"Zihan Ding",
"Edward Johns"
] | Zero-shot sim-to-real transfer of tasks with complex dynamics is a highly challenging and unsolved problem. A number of solutions have been proposed in recent years, but we have found that many works do not present a thorough evaluation in the real world, or underplay the significant engineering effort and task-specific fine tuning that is required to achieve the published results. In this paper, ... |
Tensor Action Spaces for Multi-agent Robot Transfer Learning | https://ieeexplore.ieee.org/document/9341696/ | [
"Devin Schwab",
"Yifeng Zhu",
"Manuela Veloso",
"Devin Schwab",
"Yifeng Zhu",
"Manuela Veloso"
] | We explore using reinforcement learning on single and multi-agent systems such that after learning is finished we can apply a policy zero-shot to new environment sizes, as well as different number of agents and entities. Building off previous work, we show how to map back and forth between the state and action space of a standard Markov Decision Process (MDP) and multi-dimensional tensors such tha... |
TrueÆdapt: Learning Smooth Online Trajectory Adaptation with Bounded Jerk, Acceleration and Velocity in Joint Space | https://ieeexplore.ieee.org/document/9341001/ | [
"Jonas C. Kiemel",
"Robin Weitemeyer",
"Pascal Meißner",
"Torsten Kröger",
"Jonas C. Kiemel",
"Robin Weitemeyer",
"Pascal Meißner",
"Torsten Kröger"
] | We present TrueÆdapt, a model-free method to learn online adaptations of robot trajectories based on their effects on the environment. Given sensory feedback and future waypoints of the original trajectory, a neural network is trained to predict joint accelerations at regular intervals. The adapted trajectory is generated by linear interpolation of the predicted accelerations, leading to continuou... |
Active Improvement of Control Policies with Bayesian Gaussian Mixture Model | https://ieeexplore.ieee.org/document/9341187/ | [
"Hakan Girgin",
"Emmanuel Pignat",
"Noémie Jaquier",
"Sylvain Calinon",
"Hakan Girgin",
"Emmanuel Pignat",
"Noémie Jaquier",
"Sylvain Calinon"
] | Learning from demonstration (LfD) is an intuitive framework allowing non-expert users to easily (re-)program robots. However, the quality and quantity of demonstrations have a great influence on the generalization performances of LfD approaches. In this paper, we introduce a novel active learning framework in order to improve the generalization capabilities of control policies. The proposed approa... |
Collaborative Programming of Conditional Robot Tasks | https://ieeexplore.ieee.org/document/9341212/ | [
"Christoph Willibald",
"Thomas Eiband",
"Dongheui Lee",
"Christoph Willibald",
"Thomas Eiband",
"Dongheui Lee"
] | Conventional robot programming methods are not suited for non-experts to intuitively teach robots new tasks. For this reason, the potential of collaborative robots for production cannot yet be fully exploited. In this work, we propose an active learning framework, in which the robot and the user collaborate to incrementally program a complex task. Starting with a basic model, the robot's task know... |
Learning constraint-based planning models from demonstrations | https://ieeexplore.ieee.org/document/9341535/ | [
"João Loula",
"Kelsey Allen",
"Tom Silver",
"Josh Tenenbaum",
"João Loula",
"Kelsey Allen",
"Tom Silver",
"Josh Tenenbaum"
] | How can we learn representations for planning that are both efficient and flexible? Task and motion planning models are a good candidate, having been very successful in long-horizon planning tasks-however, they've proved challenging for learning, relying mostly on hand-coded representations. We present a framework for learning constraint-based task and motion planning models using gradient descent... |
Learning Object Manipulation with Dexterous Hand-Arm Systems from Human Demonstration | https://ieeexplore.ieee.org/document/9340966/ | [
"Philipp Ruppel",
"Jianwei Zhang",
"Philipp Ruppel",
"Jianwei Zhang"
] | We present a novel learning and control framework that combines artificial neural networks with online trajectory optimization to learn dexterous manipulation skills from human demonstration and to transfer the learned behaviors to real robots. Humans can perform the demonstrations with their own hands and with real objects. An instrumented glove is used to record motions and tactile data. Our sys... |
MixGAIL: Autonomous Driving Using Demonstrations with Mixed Qualities | https://ieeexplore.ieee.org/document/9341104/ | [
"Gunmin Lee",
"Dohyeong Kim",
"Wooseok Oh",
"Kyungjae Lee",
"Songhwai Oh",
"Gunmin Lee",
"Dohyeong Kim",
"Wooseok Oh",
"Kyungjae Lee",
"Songhwai Oh"
] | In this paper, we consider autonomous driving of a vehicle using imitation learning. Generative adversarial imitation learning (GAIL) is a widely used algorithm for imitation learning. This algorithm leverages positive demonstrations to imitate the behavior of an expert. In this paper, we propose a novel method, called mixed generative adversarial imitation learning (MixGAIL), which incorporates b... |
Driving Through Ghosts: Behavioral Cloning with False Positives | https://ieeexplore.ieee.org/document/9340639/ | [
"Andreas Bühler",
"Adrien Gaidon",
"Andrei Cramariuc",
"Rares Ambrus",
"Guy Rosman",
"Wolfram Burgard",
"Andreas Bühler",
"Adrien Gaidon",
"Andrei Cramariuc",
"Rares Ambrus",
"Guy Rosman",
"Wolfram Burgard"
] | Safe autonomous driving requires robust detection of other traffic participants. However, robust does not mean perfect, and safe systems typically minimize missed detections at the expense of a higher false positive rate. This results in conservative and yet potentially dangerous behavior such as avoiding imaginary obstacles. In the context of behavioral cloning, perceptual errors at training time... |
Proximal Deterministic Policy Gradient | https://ieeexplore.ieee.org/document/9341559/ | [
"Marco Maggipinto",
"Gian Antonio Susto",
"Pratik Chaudhari",
"Marco Maggipinto",
"Gian Antonio Susto",
"Pratik Chaudhari"
] | This paper introduces two simple techniques to improve off-policy Reinforcement Learning (RL) algorithms. First, we formulate off-policy RL as a stochastic proximal point iteration. The target network plays the role of the variable of optimization and the value network computes the proximal operator. Second, we exploits the two value functions commonly employed in state-of-the-art off-policy algor... |
Online BayesSim for Combined Simulator Parameter Inference and Policy Improvement | https://ieeexplore.ieee.org/document/9341401/ | [
"Rafael Possas",
"Lucas Barcelos",
"Rafael Oliveira",
"Dieter Fox",
"Fabio Ramos",
"Rafael Possas",
"Lucas Barcelos",
"Rafael Oliveira",
"Dieter Fox",
"Fabio Ramos"
] | Recent advancements in Bayesian likelihood-free inference enables a probabilistic treatment for the problem of estimating simulation parameters and their uncertainty given sequences of observations. Domain randomization can be performed much more effectively when a posterior distribution provides the correct uncertainty over parameters in a simulated environment. In this paper, we study the integr... |
An Online Training Method for Augmenting MPC with Deep Reinforcement Learning | https://ieeexplore.ieee.org/document/9341021/ | [
"Guillaume Bellegarda",
"Katie Byl",
"Guillaume Bellegarda",
"Katie Byl"
] | Recent breakthroughs both in reinforcement learning and trajectory optimization have made significant advances towards real world robotic system deployment. Reinforcement learning (RL) can be applied to many problems without needing any modeling or intuition about the system, at the cost of high sample complexity and the inability to prove any metrics about the learned policies. Trajectory optimiz... |
Stochastic Neural Control using Raw Pointcloud Data and Building Information Models | https://ieeexplore.ieee.org/document/9340774/ | [
"Max Ferguson",
"Kincho H. Law",
"Max Ferguson",
"Kincho H. Law"
] | Recently, there has been a lot of excitement surrounding the use of reinforcement learning for robot control and navigation. However, many of these algorithms encounter difficulty navigating long or complex trajectories. This paper presents a new mobile robot control system called Stochastic Neural Control (SNC), that uses a stochastic policy gradient algorithm for local control and a modified pro... |
TTR-Based Reward for Reinforcement Learning with Implicit Model Priors | https://ieeexplore.ieee.org/document/9341477/ | [
"Xubo Lyu",
"Mo Chen",
"Xubo Lyu",
"Mo Chen"
] | Model-free reinforcement learning (RL) is a powerful approach for learning control policies directly from high-dimensional state and observation. However, it tends to be data-inefficient, which is especially costly in robotic learning tasks. On the other hand, optimal control does not require data if the system model is known, but cannot scale to models with high-dimensional states and observation... |
Learning Hierarchical Acquisition Functions for Bayesian Optimization | https://ieeexplore.ieee.org/document/9341335/ | [
"Nils Rottmann",
"Tjaša Kunavar",
"Jan Babič",
"Jan Peters",
"Elmar Rueckert",
"Nils Rottmann",
"Tjaša Kunavar",
"Jan Babič",
"Jan Peters",
"Elmar Rueckert"
] | Learning control policies in robotic tasks requires a large number of interactions due to small learning rates, bounds on the updates or unknown constraints. In contrast humans can infer protective and safe solutions after a single failure or unexpected observation. In order to reach similar performance, we developed a hierarchical Bayesian optimization algorithm that replicates the cognitive infe... |
Reinforcement Learning in Latent Action Sequence Space | https://ieeexplore.ieee.org/document/9341629/ | [
"Heecheol Kim",
"Masanori Yamada",
"Kosuke Miyoshi",
"Tomoharu Iwata",
"Hiroshi Yamakawa",
"Heecheol Kim",
"Masanori Yamada",
"Kosuke Miyoshi",
"Tomoharu Iwata",
"Hiroshi Yamakawa"
] | One problem in real-world applications of reinforcement learning is the high dimensionality of the action search spaces, which comes from the combination of actions over time. To reduce the dimensionality of action sequence search spaces, macro actions have been studied, which are sequences of primitive actions to solve tasks. However, previous studies relied on humans to define macro actions or a... |
Deep Adversarial Reinforcement Learning for Object Disentangling | https://ieeexplore.ieee.org/document/9341578/ | [
"Melvin Laux",
"Oleg Arenz",
"Jan Peters",
"Joni Pajarinen",
"Melvin Laux",
"Oleg Arenz",
"Jan Peters",
"Joni Pajarinen"
] | Deep learning in combination with improved training techniques and high computational power has led to recent advances in the field of reinforcement learning (RL) and to successful robotic RL applications such as in-hand manipulation. However, most robotic RL relies on a well known initial state distribution. In real-world tasks, this information is however often not available. For example, when d... |
Contextual Policy Search for Micro-Data Robot Motion Learning through Covariate Gaussian Process Latent Variable Models | https://ieeexplore.ieee.org/document/9340709/ | [
"Juan Antonio Delgado-Guerrero",
"Adrià Colomé",
"Carme Torras",
"Juan Antonio Delgado-Guerrero",
"Adrià Colomé",
"Carme Torras"
] | In the next few years, the amount and variety of context-aware robotic manipulator applications is expected to increase significantly, especially in household environments. In such spaces, thanks to programming by demonstration, non-expert people will be able to teach robots how to perform specific tasks, for which the adaptation to the environment is imperative, for the sake of effectiveness and ... |
Efficiency and Equity are Both Essential: A Generalized Traffic Signal Controller with Deep Reinforcement Learning | https://ieeexplore.ieee.org/document/9340784/ | [
"Shengchao Yan",
"Jingwei Zhang",
"Daniel Büscher",
"Wolfram Burgard",
"Shengchao Yan",
"Jingwei Zhang",
"Daniel Büscher",
"Wolfram Burgard"
] | Traffic signal controllers play an essential role in today's traffic system. However, the majority of them currently is not sufficiently flexible or adaptive to generate optimal traffic schedules. In this paper we present an approach to learn policies for signal controllers using deep reinforcement learning aiming for optimized traffic flow. Our method uses a novel formulation of the reward functi... |
Ultrasound-Guided Robotic Navigation with Deep Reinforcement Learning | https://ieeexplore.ieee.org/document/9340913/ | [
"Hannes Hase",
"Mohammad Farid Azampour",
"Maria Tirindelli",
"Magdalini Paschali",
"Walter Simson",
"Emad Fatemizadeh",
"Nassir Navab",
"Hannes Hase",
"Mohammad Farid Azampour",
"Maria Tirindelli",
"Magdalini Paschali",
"Walter Simson",
"Emad Fatemizadeh",
"Nassir Navab"
] | In this paper we introduce the first reinforcement learning (RL) based robotic navigation method which utilizes ultrasound (US) images as an input. Our approach combines state-of-the-art RL techniques, specifically deep Q-networks (DQN) with memory buffers and a binary classifier for deciding when to terminate the task.Our method is trained and evaluated on an in-house collected data-set of 34 vol... |
Deep R-Learning for Continual Area Sweeping | https://ieeexplore.ieee.org/document/9341626/ | [
"Rishi Shah",
"Yuqian Jiang",
"Justin Hart",
"Peter Stone",
"Rishi Shah",
"Yuqian Jiang",
"Justin Hart",
"Peter Stone"
] | Coverage path planning is a well-studied problem in robotics in which a robot must plan a path that passes through every point in a given area repeatedly, usually with a uniform frequency. To address the scenario in which some points need to be visited more frequently than others, this problem has been extended to non-uniform coverage planning. This paper considers the variant of non-uniform cover... |
Deep Reinforcement Learning for Industrial Insertion Tasks with Visual Inputs and Natural Rewards | https://ieeexplore.ieee.org/document/9341714/ | [
"Gerrit Schoettler",
"Ashvin Nair",
"Jianlan Luo",
"Shikhar Bahl",
"Juan Aparicio Ojea",
"Eugen Solowjow",
"Sergey Levine",
"Gerrit Schoettler",
"Ashvin Nair",
"Jianlan Luo",
"Shikhar Bahl",
"Juan Aparicio Ojea",
"Eugen Solowjow",
"Sergey Levine"
] | Connector insertion and many other tasks commonly found in modern manufacturing settings involve complex contact dynamics and friction. Since it is difficult to capture related physical effects with first-order modeling, traditional control methods often result in brittle and inaccurate controllers, which have to be manually tuned. Reinforcement learning (RL) methods have been demonstrated to be c... |
Robotic Table Tennis with Model-Free Reinforcement Learning | https://ieeexplore.ieee.org/document/9341191/ | [
"Wenbo Gao",
"Laura Graesser",
"Krzysztof Choromanski",
"Xingyou Song",
"Nevena Lazic",
"Pannag Sanketi",
"Vikas Sindhwani",
"Navdeep Jaitly",
"Wenbo Gao",
"Laura Graesser",
"Krzysztof Choromanski",
"Xingyou Song",
"Nevena Lazic",
"Pannag Sanketi",
"Vikas Sindhwani",
"Navdeep Jaitly"
] | We propose a model-free algorithm for learning efficient policies capable of returning table tennis balls by controlling robot joints at a rate of 100Hz. We demonstrate that evolutionary search (ES) methods acting on CNN-based policy architectures for non-visual inputs and convolving across time learn compact controllers leading to smooth motions. Furthermore, we show that with appropriately tuned... |
Optimizing a Continuum Manipulator’s Search Policy Through Model-Free Reinforcement Learning | https://ieeexplore.ieee.org/document/9341378/ | [
"Chase Frazelle",
"Jonathan Rogers",
"Ioannis Karamouzas",
"Ian Walker",
"Chase Frazelle",
"Jonathan Rogers",
"Ioannis Karamouzas",
"Ian Walker"
] | Continuum robots have long held a great potential for applications in inspection of remote, hard-to-reach environments. In future environments such as the Deep Space Gateway, remote deployment of robotic solutions will require a high level of autonomy due to communication delays and unavailability of human crews. In this work, we explore the application of policy optimization methods through Actor... |
Hypothesis-Driven Skill Discovery for Hierarchical Deep Reinforcement Learning | https://ieeexplore.ieee.org/document/9340891/ | [
"Caleb Chuck",
"Supawit Chockchowwat",
"Scott Niekum",
"Caleb Chuck",
"Supawit Chockchowwat",
"Scott Niekum"
] | Deep reinforcement learning (DRL) is capable of learning high-performing policies on a variety of complex high-dimensional tasks, ranging from video games to robotic manipulation. However, standard DRL methods often suffer from poor sample efficiency, partially because they aim to be entirely problem-agnostic. In this work, we introduce a novel approach to exploration and hierarchical skill learni... |
Robot Sound Interpretation: Combining Sight and Sound in Learning-Based Control | https://ieeexplore.ieee.org/document/9341196/ | [
"Peixin Chang",
"Shuijing Liu",
"Haonan Chen",
"Katherine Driggs-Campbell",
"Peixin Chang",
"Shuijing Liu",
"Haonan Chen",
"Katherine Driggs-Campbell"
] | We explore the interpretation of sound for robot decision making, inspired by human speech comprehension. While previous methods separate sound processing unit and robot controller, we propose an end-to-end deep neural network which directly interprets sound commands for visual-based decision making. The network is trained using reinforcement learning with auxiliary losses on the sight and sound n... |
Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulas | https://ieeexplore.ieee.org/document/9341325/ | [
"Yen-Ling Kuo",
"Boris Katz",
"Andrei Barbu",
"Yen-Ling Kuo",
"Boris Katz",
"Andrei Barbu"
] | We demonstrate a reinforcement learning agent which uses a compositional recurrent neural network that takes as input an LTL formula and determines satisfying actions. The input LTL formulas have never been seen before, yet the network performs zero-shot generalization to satisfy them. This is a novel form of multi-task learning for RL agents where agents learn from one diverse set of tasks and ge... |
PlaNet of the Bayesians: Reconsidering and Improving Deep Planning Network by Incorporating Bayesian Inference | https://ieeexplore.ieee.org/document/9340873/ | [
"Masashi Okada",
"Norio Kosaka",
"Tadahiro Taniguchi",
"Masashi Okada",
"Norio Kosaka",
"Tadahiro Taniguchi"
] | In the present paper, we propose an extension of the Deep Planning Network (PlaNet), also referred to as PlaNet of the Bayesians (PlaNet-Bayes). There has been a growing demand in model predictive control (MPC) in partially observable environments in which complete information is unavailable because of, for example, lack of expensive sensors. PlaNet is a promising solution to realize such latent M... |
Latent Space Roadmap for Visual Action Planning of Deformable and Rigid Object Manipulation | https://ieeexplore.ieee.org/document/9340764/ | [
"Martina Lippi",
"Petra Poklukar",
"Michael C. Welle",
"Anastasiia Varava",
"Hang Yin",
"Alessandro Marino",
"Danica Kragic",
"Martina Lippi",
"Petra Poklukar",
"Michael C. Welle",
"Anastasiia Varava",
"Hang Yin",
"Alessandro Marino",
"Danica Kragic"
] | We present a framework for visual action planning of complex manipulation tasks with high-dimensional state spaces such as manipulation of deformable objects. Planning is performed in a low-dimensional latent state space that embeds images. We define and implement a Latent Space Roadmap (LSR) which is a graph-based structure that globally captures the latent system dynamics. Our framework consists... |
Learning the Latent Space of Robot Dynamics for Cutting Interaction Inference | https://ieeexplore.ieee.org/document/9341446/ | [
"Sahand Rezaei-Shoshtari",
"David Meger",
"Inna Sharf",
"Sahand Rezaei-Shoshtari",
"David Meger",
"Inna Sharf"
] | Utilization of latent space to capture a lower-dimensional representation of a complex dynamics model is explored in this work. The targeted application is of a robotic manipulator executing a complex environment interaction task, in particular, cutting a wooden object. We train two flavours of Variational Autoencoders-standard and Vector-Quantised-to learn the latent space which is then used to i... |
SwingBot: Learning Physical Features from In-hand Tactile Exploration for Dynamic Swing-up Manipulation | https://ieeexplore.ieee.org/document/9341006/ | [
"Chen Wang",
"Shaoxiong Wang",
"Branden Romero",
"Filipe Veiga",
"Edward Adelson",
"Chen Wang",
"Shaoxiong Wang",
"Branden Romero",
"Filipe Veiga",
"Edward Adelson"
] | Several robot manipulation tasks are extremely sensitive to variations of the physical properties of the manipulated objects. One such task is manipulating objects by using gravity or arm accelerations, increasing the importance of mass, center of mass, and friction information. We present SwingBot, a robot that is able to learn the physical features of an held object through tactile exploration. ... |
Representation and Experience-Based Learning of Explainable Models for Robot Action Execution | https://ieeexplore.ieee.org/document/9341470/ | [
"Alex Mitrevski",
"Paul G. Plöger",
"Gerhard Lakemeyer",
"Alex Mitrevski",
"Paul G. Plöger",
"Gerhard Lakemeyer"
] | For robots acting in human-centered environments, the ability to improve based on experience is essential for reliable and adaptive operation; however, particularly in the context of robot failure analysis, experience-based improvement is practically useful only if robots are also able to reason about and explain the decisions they make during execution. In this paper, we describe and analyse a re... |
Improving Unimodal Object Recognition with Multimodal Contrastive Learning | https://ieeexplore.ieee.org/document/9341029/ | [
"Johannes Meyer",
"Andreas Eitel",
"Thomas Brox",
"Wolfram Burgard",
"Johannes Meyer",
"Andreas Eitel",
"Thomas Brox",
"Wolfram Burgard"
] | Robots perceive their environment using various sensor modalities, e.g., vision, depth, sound or touch. Each modality provides complementary information for perception. However, while it can be assumed that all modalities are available for training, when deploying the robot in real-world scenarios the sensor setup often varies. In order to gain flexibility with respect to the deployed sensor setup... |
Roadmap Subsampling for Changing Environments | https://ieeexplore.ieee.org/document/9341431/ | [
"Sean Murray",
"George D. Konidaris",
"Daniel J. Sorin",
"Sean Murray",
"George D. Konidaris",
"Daniel J. Sorin"
] | Precomputed roadmaps can enable effective multi-query motion planning: a roadmap can be built for a robot as if no obstacles were present, and then after edges invalidated by obstacles observed at query time are deleted, path search through the remaining roadmap returns a collision-free plan. However, large roadmaps are memory intensive to store, and can be too slow for practical use. We present a... |
Robot Navigation in Crowded Environments Using Deep Reinforcement Learning | https://ieeexplore.ieee.org/document/9341540/ | [
"Lucia Liu",
"Daniel Dugas",
"Gianluca Cesari",
"Roland Siegwart",
"Renaud Dubé",
"Lucia Liu",
"Daniel Dugas",
"Gianluca Cesari",
"Roland Siegwart",
"Renaud Dubé"
] | Mobile robots operating in public environments require the ability to navigate among humans and other obstacles in a socially compliant and safe manner. This work presents a combined imitation learning and deep reinforcement learning approach for motion planning in such crowded and cluttered environments. By separately processing information related to static and dynamic objects, we enable our net... |
Configuration Space Decomposition for Learning-based Collision Checking in High-DOF Robots | https://ieeexplore.ieee.org/document/9341526/ | [
"Yiheng Han",
"Wang Zhao",
"Jia Pan",
"Yong-Jin Liu",
"Yiheng Han",
"Wang Zhao",
"Jia Pan",
"Yong-Jin Liu"
] | Motion planning for robots of high degrees-of-freedom (DOFs) is an important problem in robotics with sampling-based methods in configuration space $\mathcal{C}$ as one popular solution. Recently, machine learning methods have been introduced into sampling-based motion planning methods, which train a classifier to distinguish collision free subspace from in-collision subspace in $\mathcal{C}$. In ... |
A Time Optimal Reactive Collision Avoidance Method for UAVs Based on a Modified Collision Cone Approach | https://ieeexplore.ieee.org/document/9341259/ | [
"Manaram Gnanasekera",
"Jay Katupitiya",
"Manaram Gnanasekera",
"Jay Katupitiya"
] | UAVs or Unmanned Aerial Vehicles are an upcoming technology which has eased human lifestyles in many ways. Due to this trend future skies have a risk of getting congested. In such a situation time optimal collision avoidance would be extremely vital to travel in a shortest possible time by avoiding collisions. The paper proposes a novel method for time optimal collision avoidance for UAVs. The pro... |
Computationally Efficient Obstacle Avoidance Trajectory Planner for UAVs Based on Heuristic Angular Search Method | https://ieeexplore.ieee.org/document/9340778/ | [
"Han Chen",
"Peng Lu",
"Han Chen",
"Peng Lu"
] | For accomplishing a variety of missions in challenging environments, the capability of navigating with full autonomy while avoiding unexpected obstacles is the most crucial requirement for UAVs in real applications. In this paper, we proposed such a computationally efficient obstacle avoidance trajectory planner that can be used in unknown cluttered environments. Because of the narrow view field o... |
Closing the Loop: Real-Time Perception and Control for Robust Collision Avoidance with Occluded Obstacles | https://ieeexplore.ieee.org/document/9341663/ | [
"Andreea Tulbure",
"Oussama Khatib",
"Andreea Tulbure",
"Oussama Khatib"
] | Robots have been successfully used in well-structured and deterministic environments, but they are still unable to function in unstructured environments mainly because of missing reliable real-time systems that integrate perception and control. In this paper, we close the loop between perception and control for real-time obstacle avoidance by introducing a new robust perception algorithm and a new... |
A modified Hybrid Reciprocal Velocity Obstacles approach for multi-robot motion planning without communication | https://ieeexplore.ieee.org/document/9341377/ | [
"Maxime Sainte Catherine",
"Eric Lucet",
"Maxime Sainte Catherine",
"Eric Lucet"
] | Ensuring a safe online motion planning despite a large number of moving agents is the problem addressed in this paper. Collision avoidance is achieved without communication between the agents and without global localization system. The proposed solution is a modification of the Hybrid Reciprocal Velocity Obstacles (HRVO) combined with a tracking error estimation, in order to adapt the Velocity Obs... |
Safe and Effective Picking Paths in Clutter given Discrete Distributions of Object Poses | https://ieeexplore.ieee.org/document/9340922/ | [
"Rui Wang",
"Chaitanya Mitash",
"Shiyang Lu",
"Daniel Boehm",
"Kostas E. Bekris",
"Rui Wang",
"Chaitanya Mitash",
"Shiyang Lu",
"Daniel Boehm",
"Kostas E. Bekris"
] | Picking an item in the presence of other objects can be challenging as it involves occlusions and partial views. Given object models, one approach is to perform object pose estimation and use the most likely candidate pose per object to pick the target without collisions. This approach, however, ignores the uncertainty of the perception process both regarding the target's and the surrounding objec... |
Risk-Averse MPC via Visual-Inertial Input and Recurrent Networks for Online Collision Avoidance | https://ieeexplore.ieee.org/document/9341070/ | [
"Alexander Schperberg",
"Kenny Chen",
"Stephanie Tsuei",
"Michael Jewett",
"Joshua Hooks",
"Stefano Soatto",
"Ankur Mehta",
"Dennis Hong",
"Alexander Schperberg",
"Kenny Chen",
"Stephanie Tsuei",
"Michael Jewett",
"Joshua Hooks",
"Stefano Soatto",
"Ankur Mehta",
"Dennis Hong"
] | In this paper, we propose an online path planning architecture that extends the model predictive control (MPC) formulation to consider future location uncertainties for safer navigation through cluttered environments. Our algorithm combines an object detection pipeline with a recurrent neural network (RNN) which infers the covariance of state estimates through each step of our MPC's finite time ho... |
A Data-driven Framework for Proactive Intention-Aware Motion Planning of a Robot in a Human Environment | https://ieeexplore.ieee.org/document/9341210/ | [
"Rahul Peddi",
"Carmelo Di Franco",
"Shijie Gao",
"Nicola Bezzo",
"Rahul Peddi",
"Carmelo Di Franco",
"Shijie Gao",
"Nicola Bezzo"
] | For safe and efficient human-robot interaction, a robot needs to predict and understand the intentions of humans who share the same space. Mobile robots are traditionally built to be reactive, moving in unnatural ways without following social protocol, hence forcing people to behave very differently from human-human interaction rules, which can be overcome if robots instead were proactive. In this... |
Dynamic Attention-based Visual Odometry | https://ieeexplore.ieee.org/document/9340890/ | [
"Xin-Yu Kuo",
"Chien Liu",
"Kai-Chen Lin",
"Evan Luo",
"Yu-Wen Chen",
"Chun-Yi Lee",
"Xin-Yu Kuo",
"Chien Liu",
"Kai-Chen Lin",
"Evan Luo",
"Yu-Wen Chen",
"Chun-Yi Lee"
] | This paper proposes a dynamic attention-based visual odometry framework (DAVO), a learning-based VO method, for estimating the ego-motion of a monocular camera. DAVO dynamically adjusts the attention weights on different semantic categories for different motion scenarios based on optical flow maps. These weighted semantic categories can then be used to generate attention maps that highlight the re... |
Richer Aggregated Features for Optical Flow Estimation with Edge-aware Refinement | https://ieeexplore.ieee.org/document/9341544/ | [
"Xianshun Wang",
"Dongchen Zhu",
"Jiafei Song",
"Yanqing Liu",
"Jiamao Li",
"Xiaolin Zhang",
"Xianshun Wang",
"Dongchen Zhu",
"Jiafei Song",
"Yanqing Liu",
"Jiamao Li",
"Xiaolin Zhang"
] | Recent CNN-based optical flow approaches have a separated structure of feature extraction and flow estimation. The core task of optical flow is finding the corresponding points while rich representation is just the key part of such matching problems. However, the prior work usually pays more attention to the design of flow decoder than the feature extraction. In this paper, we present a novel opti... |
LiDAR Iris for Loop-Closure Detection | https://ieeexplore.ieee.org/document/9341010/ | [
"Ying Wang",
"Zezhou Sun",
"Cheng-Zhong Xu",
"Sanjay E. Sarma",
"Jian Yang",
"Hui Kong",
"Ying Wang",
"Zezhou Sun",
"Cheng-Zhong Xu",
"Sanjay E. Sarma",
"Jian Yang",
"Hui Kong"
] | In this paper, a global descriptor for a LiDAR point cloud, called LiDAR Iris, is proposed for fast and accurate loop-closure detection. A binary signature image can be obtained for each point cloud after several LoG-Gabor filtering and thresholding operations on the LiDAR-Iris image representation. Given two point clouds, their similarities can be calculated as the Hamming distance of two corresp... |
Confidence Guided Stereo 3D Object Detection with Split Depth Estimation | https://ieeexplore.ieee.org/document/9341188/ | [
"Chengyao Li",
"Jason Ku",
"Steven L. Waslander",
"Chengyao Li",
"Jason Ku",
"Steven L. Waslander"
] | Accurate and reliable 3D object detection is vital to safe autonomous driving. Despite recent developments, the performance gap between stereo-based methods and LiDAR-based methods is still considerable. Accurate depth estimation is crucial to the performance of stereo-based 3D object detection methods, particularly for those pixels associated with objects in the foreground. Moreover, stereo-based... |
End-to-end Contextual Perception and Prediction with Interaction Transformer | https://ieeexplore.ieee.org/document/9341392/ | [
"Lingyun Luke Li",
"Bin Yang",
"Ming Liang",
"Wenyuan Zeng",
"Mengye Ren",
"Sean Segal",
"Raquel Urtasun",
"Lingyun Luke Li",
"Bin Yang",
"Ming Liang",
"Wenyuan Zeng",
"Mengye Ren",
"Sean Segal",
"Raquel Urtasun"
] | In this paper, we tackle the problem of detecting objects in 3D and forecasting their future motion in the context of self-driving. Towards this goal, we design a novel approach that explicitly takes into account the interactions between actors. To capture their spatial-temporal dependencies, we propose a recurrent neural network with a novel Transformer [1] architecture, which we call the Interac... |
Inferring Spatial Uncertainty in Object Detection | https://ieeexplore.ieee.org/document/9340798/ | [
"Zining Wang",
"Di Feng",
"Yiyang Zhou",
"Lars Rosenbaum",
"Fabian Timm",
"Klaus Dietmayer",
"Masayoshi Tomizuka",
"Wei Zhan",
"Zining Wang",
"Di Feng",
"Yiyang Zhou",
"Lars Rosenbaum",
"Fabian Timm",
"Klaus Dietmayer",
"Masayoshi Tomizuka",
"Wei Zhan"
] | The availability of real-world datasets is the prerequisite for developing object detection methods for autonomous driving. While ambiguity exists in object labels due to error-prone annotation process or sensor observation noises, current object detection datasets only provide deterministic annotations without considering their uncertainty. This precludes an in-depth evaluation among different ob... |
One-Shot Informed Robotic Visual Search in the Wild | https://ieeexplore.ieee.org/document/9340914/ | [
"Karim Koreitem",
"Florian Shkurti",
"Travis Manderson",
"Wei-Di Chang",
"Juan Camilo Gamboa Higuera",
"Gregory Dudek",
"Karim Koreitem",
"Florian Shkurti",
"Travis Manderson",
"Wei-Di Chang",
"Juan Camilo Gamboa Higuera",
"Gregory Dudek"
] | We consider the task of underwater robot navigation for the purpose of collecting scientifically relevant video data for environmental monitoring. The majority of field robots that currently perform monitoring tasks in unstructured natural environments navigate via path-tracking a pre-specified sequence of waypoints. Although this navigation method is often necessary, it is limiting because the ro... |
Perception-aware Path Planning for UAVs using Semantic Segmentation | https://ieeexplore.ieee.org/document/9341347/ | [
"Luca Bartolomei",
"Lucas Teixeira",
"Margarita Chli",
"Luca Bartolomei",
"Lucas Teixeira",
"Margarita Chli"
] | In this work, we present a perception-aware path-planning pipeline for Unmanned Aerial Vehicles (UAVs) for navigation in challenging environments. The objective is to reach a given destination safely and accurately by relying on monocular camera-based state estimators, such as Keyframe-based Visual-Inertial Odometry (VIO) systems. Motivated by the recent advances in semantic segmentation using dee... |
Learning Your Way Without Map or Compass: Panoramic Target Driven Visual Navigation | https://ieeexplore.ieee.org/document/9341511/ | [
"David Watkins-Valls",
"Jingxi Xu",
"Nicholas Waytowich",
"Peter Allen",
"David Watkins-Valls",
"Jingxi Xu",
"Nicholas Waytowich",
"Peter Allen"
] | We present a robot navigation system that uses an imitation learning framework to successfully navigate in complex environments. Our framework takes a pre-built 3D scan of a real environment and trains an agent from pre-generated expert trajectories to navigate to any position given a panoramic view of the goal and the current visual input without relying on map, compass, odometry, or relative pos... |
Autonomous Navigation in Complex Environments with Deep Multimodal Fusion Network | https://ieeexplore.ieee.org/document/9341494/ | [
"Anh Nguyen",
"Ngoc Nguyen",
"Kim Tran",
"Erman Tjiputra",
"Quang D. Tran",
"Anh Nguyen",
"Ngoc Nguyen",
"Kim Tran",
"Erman Tjiputra",
"Quang D. Tran"
] | Autonomous navigation in complex environments is a crucial task in time-sensitive scenarios such as disaster response or search and rescue. However, complex environments pose significant challenges for autonomous platforms to navigate due to their challenging properties: constrained narrow passages, unstable pathway with debris and obstacles, or irregular geological structures and poor lighting co... |
Unsupervised Learning of Dense Optical Flow, Depth and Egomotion with Event-Based Sensors | https://ieeexplore.ieee.org/document/9341224/ | [
"Chengxi Ye",
"Anton Mitrokhin",
"Cornelia Fermüller",
"James A. Yorke",
"Yiannis Aloimonos",
"Chengxi Ye",
"Anton Mitrokhin",
"Cornelia Fermüller",
"James A. Yorke",
"Yiannis Aloimonos"
] | We present an unsupervised learning pipeline for dense depth, optical flow and egomotion estimation for autonomous driving applications, using the event-based output of the Dynamic Vision Sensor (DVS) as input. The backbone of our pipeline is a bioinspired encoder-decoder neural network architecture - ECN. To train the pipeline, we introduce a covariance normalization technique which resembles the... |
HouseExpo: A Large-scale 2D Indoor Layout Dataset for Learning-based Algorithms on Mobile Robots | https://ieeexplore.ieee.org/document/9341284/ | [
"Tingguang Li",
"Danny Ho",
"Chenming Li",
"Delong Zhu",
"Chaoqun Wang",
"Max Q.-H. Meng",
"Tingguang Li",
"Danny Ho",
"Chenming Li",
"Delong Zhu",
"Chaoqun Wang",
"Max Q.-H. Meng"
] | As one of the most promising areas, mobile robots draw much attention these years. Current work in this field is often evaluated in a few manually designed scenarios, due to the lack of a common experimental platform. Meanwhile, with the recent development of deep learning techniques, some researchers attempt to apply learning-based methods to mobile robot tasks, which requires a substantial amoun... |
Multimodal Aggregation Approach for Memory Vision-Voice Indoor Navigation with Meta-Learning | https://ieeexplore.ieee.org/document/9341398/ | [
"Liqi Yan",
"Dongfang Liu",
"Yaoxian Song",
"Changbin Yu",
"Liqi Yan",
"Dongfang Liu",
"Yaoxian Song",
"Changbin Yu"
] | Vision and voice are two vital keys for agents’ interaction and learning. In this paper, we present a novel indoor navigation model called Memory Vision-Voice Indoor Navigation (MVV-IN), which receives voice commands and analyzes multimodal information of visual observation in order to enhance robots’ environment understanding. We make use of single RGB images taken by a rst-view monocular camera.... |
Occlusion-Robust MVO: Multimotion Estimation Through Occlusion Via Motion Closure | https://ieeexplore.ieee.org/document/9341355/ | [
"Kevin M. Judd",
"Jonathan D. Gammell",
"Kevin M. Judd",
"Jonathan D. Gammell"
] | Visual motion estimation is an integral and well-studied challenge in autonomous navigation. Recent work has focused on addressing multimotion estimation, which is especially challenging in highly dynamic environments. Such environments not only comprise multiple, complex motions but also tend to exhibit significant occlusion.Previous work in object tracking focuses on maintaining the integrity of... |
IDOL: A Framework for IMU-DVS Odometry using Lines | https://ieeexplore.ieee.org/document/9341208/ | [
"Cedric Le Gentil",
"Florian Tschopp",
"Ignacio Alzugaray",
"Teresa Vidal-Calleja",
"Roland Siegwart",
"Juan Nieto",
"Cedric Le Gentil",
"Florian Tschopp",
"Ignacio Alzugaray",
"Teresa Vidal-Calleja",
"Roland Siegwart",
"Juan Nieto"
] | In this paper, we introduce IDOL, an optimization-based framework for IMU-DVS Odometry using Lines. Event cameras, also called Dynamic Vision Sensors (DVSs), generate highly asynchronous streams of events triggered upon illumination changes for each individual pixel. This novel paradigm presents advantages in low illumination conditions and high-speed motions. Nonetheless, this unconventional sens... |
Point Cloud Based Reinforcement Learning for Sim-to-Real and Partial Observability in Visual Navigation | https://ieeexplore.ieee.org/document/9341771/ | [
"Kenzo Lobos-Tsunekawa",
"Tatsuya Harada",
"Kenzo Lobos-Tsunekawa",
"Tatsuya Harada"
] | Reinforcement Learning (RL), among other learning-based methods, represents powerful tools to solve complex robotic tasks (e.g., actuation, manipulation, navigation, etc.), with the need for real-world data to train these systems as one of its most important limitations. The use of simulators is one way to address this issue, yet knowledge acquired in simulations does not work directly in the real... |
Autonomous Robot Navigation Based on Multi-Camera Perception | https://ieeexplore.ieee.org/document/9341304/ | [
"Kunyan Zhu",
"Wei Chen",
"Wei Zhang",
"Ran Song",
"Yibin Li",
"Kunyan Zhu",
"Wei Chen",
"Wei Zhang",
"Ran Song",
"Yibin Li"
] | In this paper, we propose an autonomous method for robot navigation based on a multi-camera setup that takes advantage of a wide field of view. A new multi-task network is designed for handling the visual information supplied by the left, central and right cameras to find the passable area, detect the intersection and infer the steering. Based on the outputs of the network, three navigation indica... |
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