4.6 Article

Energy-Based Legged Robots Terrain Traversability Modeling via Deep Inverse Reinforcement Learning

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Proceedings Paper Automation & Control Systems

An Error-State Model Predictive Control on Connected Matrix Lie Groups for Legged Robot Control

Sangli Teng et al.

Summary: This paper presents a new error-state Model Predictive Control (MPC) approach for robot control based on connected matrix Lie groups. By linearizing the tracking error dynamics and equations of motion in the Lie algebra and exploiting the problem's symmetry, the proposed approach achieves faster convergence of rotation and position compared to the current state-of-the-art methods. Numerical simulations and experiments on a quadrupedal robot confirm the advantages of the proposed approach.

2022 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) (2022)

Review Chemistry, Analytical

Learning-Based Methods of Perception and Navigation for Ground Vehicles in Unstructured Environments: A Review

Dario Calogero Guastella et al.

Summary: The review focuses on recent contributions in robotics research that use learning-based methods to address the issue of autonomous perception and interpretation for ground vehicles in unstructured environments. Perception is highlighted as crucial for autonomous navigation, providing necessary information for accurate navigation in complex environments. This review is the first of its kind in this context, shedding light on the importance of perception for context-aware navigation.

SENSORS (2021)

Article Robotics

Incorporating Multi-Context Into the Traversability Map for Urban Autonomous Driving Using Deep Inverse Reinforcement Learning

Chanyoung Jung et al.

Summary: This study introduces a novel deep neural network that can predict traversability maps for autonomous driving in a dynamic environment considering multiple contexts without using expensive prior information. Experimental results demonstrate that the proposed method improves prediction accuracy and can predict future trajectories similar to those followed by a human driver.

IEEE ROBOTICS AND AUTOMATION LETTERS (2021)

Article Robotics

BADGR: An Autonomous Self-Supervised Learning-Based Navigation System

Gregory Kahn et al.

Summary: Traditional mobile robot navigation solutions focus on the geometric structure of the environment, but this approach may not always be effective. BADGR utilizes a reinforcement learning approach to move beyond purely geometric navigation solutions, learning physical navigational affordances in order to navigate mobile robots without the need for simulation or human supervision.

IEEE ROBOTICS AND AUTOMATION LETTERS (2021)

Article Computer Science, Artificial Intelligence

A survey of inverse reinforcement learning: Challenges, methods and progress

Saurabh Arora et al.

Summary: This article categorically surveys the extant literature in Inverse Reinforcement Learning (IRL) as a comprehensive reference for understanding the challenges and selecting suitable approaches. It introduces central challenges of IRL and how various foundational methods mitigate those challenges. The article also discusses extensions to traditional IRL methods and highlights broad advances in the research area alongside current open research questions.

ARTIFICIAL INTELLIGENCE (2021)

Article Computer Science, Artificial Intelligence

Navigating by touch: haptic Monte Carlo localization via geometric sensing and terrain classification

Russell Buchanan et al.

Summary: This study proposes a purely proprioceptive localization algorithm for legged robot navigation in extreme environments, which fuses information from both geometry and terrain type to localize the robot within a prior map. Experimental results demonstrate its effective operation in various terrain and geometric environments.

AUTONOMOUS ROBOTS (2021)

Article Chemistry, Multidisciplinary

Regressed Terrain Traversability Cost for Autonomous Navigation Based on Image Textures

Mohammed Abdessamad Bekhti et al.

APPLIED SCIENCES-BASEL (2020)

Proceedings Paper Automation & Control Systems

Autonomous Navigation in Complex Environments with Deep Multimodal Fusion Network

Anh Nguyen et al.

2020 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) (2020)

Article Robotics

Bayesian Spatial Kernel Smoothing for Scalable Dense Semantic Mapping

Lu Gan et al.

IEEE ROBOTICS AND AUTOMATION LETTERS (2020)

Article Robotics

Sampling-based incremental information gathering with applications to robotic exploration and environmental monitoring

Maani Ghaffari Jadidi et al.

INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH (2019)

Proceedings Paper Computer Science, Artificial Intelligence

On Unsupervised Learning of Traversal Cost and Terrain Types Identification Using Self-organizing Maps

Jan Faigl et al.

ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2019: THEORETICAL NEURAL COMPUTATION, PT I (2019)

Proceedings Paper Automation & Control Systems

What am I touching? Learning to classify terrain via haptic sensing

Jakub Bednarek et al.

2019 INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA) (2019)

Article Robotics

Where Should I Walk? Predicting Terrain Properties From Images Via Self-Supervised Learning

Lorenz Wellhausen et al.

IEEE ROBOTICS AND AUTOMATION LETTERS (2019)

Article Geochemistry & Geophysics

Road Extraction by Deep Residual U-Net

Zhengxin Zhang et al.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2018)

Article Robotics

Probabilistic Terrain Mapping for Mobile Robots With Uncertain Localization

Peter Fankhauser et al.

IEEE ROBOTICS AND AUTOMATION LETTERS (2018)

Article Robotics

ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras

Raul Mur-Artal et al.

IEEE TRANSACTIONS ON ROBOTICS (2017)

Article Robotics

Large-scale cost function learning for path planning using deep inverse reinforcement learning

Markus Wulfmeier et al.

INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH (2017)

Article Automation & Control Systems

Terrain traversability analysis methods for unmanned ground vehicles: A survey

Panagiotis Papadakis

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2013)

Article Robotics

Learning from Demonstration for Autonomous Navigation in Complex Unstructured Terrain

David Silver et al.

INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH (2010)