Recent work has shown results on learning navigation policies for idealized cylinder agents in simulation and transferring them to real wheeled robots. Deploying such navigation policies on legged robots can be challenging due to their complex dynamics, and the large dynamical difference between cylinder agents and legged systems. In this work, we learn hierarchical navigation policies that account for the low-level dynamics of legged robots, such as maximum speed, slipping, contacts, and learn to successfully navigate cluttered indoor environments. To enable transfer of policies learned in simulation to new legged robots and hardware, we learn dynamics-aware navigation policies across multiple robots with robot-specific embeddings. The learned embedding is optimized on new robots, while the rest of the policy is kept fixed, allowing for quick adaptation. We train our policies across three legged robots in simulation - 2 quadrupeds (A1, AlienGo) and a hexapod (Daisy). At test time, we study the performance of our learned policy on two new legged robots in simulation (Laikago, 4-legged Daisy), and one real-world quadrupedal robot (A1). Our experiments show that our learned policy can sample-efficiently generalize to previously unseen robots, and enable sim-to-real transfer of navigation policies for legged robots.
MUJICA: Multi-skill Unified Joint Integration of Control Architecture for Wheeled-Legged Robots
College of Intelligent Robotics and Advanced Manufacturing, Fudan University（智能机器人与先进制造学院，复旦大学）；Power China Huadong Engineering Corporation Limited（中国电力工程顾问集团华东分公司）
REASAN: Learning Reactive Safe Navigation for Legged Robots
Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, The Netherlands（格罗宁根大学伯努利数学、计算机科学与人工智能研究所）；Department of Electrical Engineering, Linköping University, Sweden（利厄普大学电气工程系）；Engineering and Technology Institute Groningen, University of Groningen, The Netherlands（格罗宁根大学格罗宁根工程与技术研究所）
WAVE-Go: World-Model Navigation with Adaptive Execution for Wheel-Legged Robots
Beijing Institute of Technology（北京理工大学）；The University of Hong Kong（香港大学）；GAC R&D Center（广汽研究院）；LimX Dynamics；Beijing Academy of Artificial Intelligence (BAAI)（北京人工智能研究院）
Mobile robot navigation in complex and dynamic environments is a challenging but important problem. Reinforcement learning approaches fail to solve these tasks efficiently due to reward sparsities, temporal complexities and high-dimensionality of sensorimotor spaces which are inherent in such problems. We present a novel approach to train action policies to acquire navigation skills for wheel-legged robots using deep reinforcement learning. The policy maps height-map image observations to motor commands to navigate to a target position while avoiding obstacles. We propose to acquire the multif…
Legged robots pose one of the greatest challenges in robotics. Dynamic and agile maneuvers of animals cannot be imitated by existing methods that are crafted by humans. A compelling alternative is reinforcement learning, which requires minimal craftsmanship and promotes the natural evolution of a control policy. However, so far, reinforcement learning research for legged robots is mainly limited to simulation, and only few and comparably simple examples have been deployed on real systems. The primary reason is that training with real robots, particularly with dynamically balancing systems, is …
Building Forest Inventories with Autonomous Legged Robots -- System, Lessons, and Challenges Ahead
Oxford Robotics Institute, Department of Engineering Science, University of Oxford, Oxford, UK（牛津大学机器人研究所、工程科学系、牛津大学）；Department of Mechanical and Process Engineering, ETH Zurich, Zurich, Switzerland（机械与过程工程系、苏黎世联邦理工学院）；Max Planck Institute for Intelligent Systems, Tübingen, Germany（智能系统马克斯·普朗克研究所、图宾根德国）；Technical University of Munich, Munich, Germany（慕尼黑技术大学）
We exploit the complementary strengths of vision and proprioception to develop a point-goal navigation system for legged robots, called VP-Nav. Legged systems are capable of traversing more complex terrain than wheeled robots, but to fully utilize this capability, we need a high-level path planner in the navigation system to be aware of the walking capabilities of the low-level locomotion policy in varying environments. We achieve this by using proprioceptive feedback to ensure the safety of the planned path by sensing unexpected obstacles like glass walls, terrain properties like slipperiness…
Soft pneumatic legged robots show promise in their ability to traverse a range of different types of terrain, including natural unstructured terrain met in applications like precision agriculture. They can adapt their body morphology to the intricacies of the terrain at hand, thus enabling robust and resilient locomotion. In this paper we capitalize upon recent developments on soft pneumatic legged robots to introduce a closed-loop trajectory tracking control scheme for operation over flat ground. Closed-loop pneumatic actuation feedback is achieved via a compact and portable pneumatic regulat…
GeCCo -- a Generalist Contact-Conditioned Policy for Loco-Manipulation Skills on Legged Robots
Dynamic Robot Systems Group, Oxford Robotics Institute, University of Oxford（牛津大学机器人研究所动态机器人系统组）；Sony AI（索尼人工智能）；Dyson Institute of Engineering and Technology（戴森工程与技术研究所）
NaviTrace: Evaluating Embodied Navigation of Vision-Language Models
Robotic Systems Lab, ETH Zurich（苏黎世联邦理工学院机器人系统实验室）；Intuitive Robots Lab, KIT（卡尔斯鲁厄理工学院直观机器人实验室）；FZI Research Center for Information Technology（弗劳恩霍夫信息技术研究中心）
Autonomous Forest Inventory with Legged Robots: System Design and Field Deployment
University of Oxford（牛津大学）；Oxford Robotics Institute（牛津机器人研究所）；Technical University of Munich（慕尼黑工业大学）；ETH Zurich（苏黎世联邦理工学院）；Robotic Systems Lab（机器人系统实验室）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
Istituto Italiano di Tecnologia（意大利技术研究院）；Università degli Studi di Genova（热那亚大学）；Swiss Federal Institute of Technology at Lausanne（洛桑联邦理工学院）；University of Texas（德克萨斯大学）；University of Naples Federico II（那不勒斯费德里科二世大学）
(Simplified Abstract) To accomplish breakthroughs in dynamic whole-body locomotion, legged robots have to be terrain aware. Terrain-Aware Locomotion (TAL) implies that the robot can perceive the terrain with its sensors, and can take decisions based on this information. This thesis presents TAL strategies both from a proprioceptive and an exteroceptive perspective. The strategies are implemented at the level of locomotion planning, control, and state estimation, and using optimization and learning techniques. The first part is on TAL strategies at the Whole-Body Control (WBC) level. We introdu…
Unleashing the Agility of Wheeled-Legged Robots for High-Dynamic Reflexive Obstacle Evasion
School of Mechanical Engineering, Tianjin University（天津大学机械工程学院）；School of Computing, National University of Singapore（新加坡国立大学计算机学院）；Beijing Zhongguancun Academy（北京中关村学院）；DeepRobotics（深睿科技）
Institute of Computer Science, Foundation for Research and Technology - Hellas (FORTH)（希腊基础研究与技术基金会计算机科学研究所）；Ownage Dynamics L.P.（Ownage Dynamics有限合伙企业）
Legged robot navigation in unstructured and slippery terrains depends heavily on the ability to accurately identify the quality of contact between the robot's feet and the ground. Contact state estimation is regarded as a challenging problem and is typically addressed by exploiting force measurements, joint encoders and/or robot kinematics and dynamics. In contrast to most state of the art approaches, the current work introduces a novel probabilistic method for estimating the contact state based solely on proprioceptive sensing, as it is readily available by Inertial Measurement Units (IMUs) m…
Oxford Robotics Institute（牛津机器人研究所）；University of Oxford（牛津大学）
Legged robots, specifically quadrupeds, are becoming increasingly attractive for industrial applications such as inspection. However, to leave the laboratory and to become useful to an end user requires reliability in harsh conditions. From the perspective of state estimation, it is essential to be able to accurately estimate the robot's state despite challenges such as uneven or slippery terrain, textureless and reflective scenes, as well as dynamic camera occlusions. We are motivated to reduce the dependency on foot contact classifications, which fail when slipping, and to reduce position dr…
Arm-Constrained Curriculum Learning for Loco-Manipulation of the Wheel-Legged Robot
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院）；Tsinghua University（清华大学）；Harbin Institute of Technology（哈尔滨工业大学）；Xi’an Jiaotong-Liverpool University（西交利物浦大学）
Proprioceptive-only State Estimation for Legged Robots with Set-Coverage Measurements of Learned Dynamics
Department of Mechanical Engineering, University of Delaware（德雷塞尔大学机械工程系）；Robotics Institute, Athena Research Center（阿提卡研究中心机器人研究所）；School of Mechanical Engineering, National Technical University of Athens（雅典技术大学机械工程学院）；HERON–Center of Excellence in Robotics（机器人卓越中心）
This work is on vision-based planning strategies for legged robots that separate locomotion planning into foothold selection and pose adaptation. Current pose adaptation strategies optimize the robot's body pose relative to given footholds. If these footholds are not reached, the robot may end up in a state with no reachable safe footholds. Therefore, we present a Vision-Based Terrain-Aware Locomotion (ViTAL) strategy that consists of novel pose adaptation and foothold selection algorithms. ViTAL introduces a different paradigm in pose adaptation that does not optimize the body pose relative t…
College of Engineering, University of Michigan（密歇根大学工程学院）
This paper derives a contact-aided inertial navigation observer for a 3D bipedal robot using the theory of invariant observer design. Aided inertial navigation is fundamentally a nonlinear observer design problem; thus, current solutions are based on approximations of the system dynamics, such as an Extended Kalman Filter (EKF), which uses a system's Jacobian linearization along the current best estimate of its trajectory. On the basis of the theory of invariant observer design by Barrau and Bonnabel, and in particular, the Invariant EKF (InEKF), we show that the error dynamics of the point co…
Scientific Exploration of Challenging Planetary Analog Environments with a Team of Legged Robots
ETH Zurich（苏黎世联邦理工学院）；Harbin Institute of Technology（哈尔滨工业大学）；University of Bern（伯尔尼大学）；University of Basel（巴塞尔大学）；University of Zurich（苏黎世大学）；Lucerne University of Applied Sciences and Art（卢塞恩应用科学与艺术大学）
Berkeley AI Research, UC Berkeley（加州大学伯克利分校伯克利人工智能研究院）；Google Research（谷歌研究院）；Georgia Institute of Technology（佐治亚理工学院）
Legged robots are physically capable of traversing a wide range of challenging environments, but designing controllers that are sufficiently robust to handle this diversity has been a long-standing challenge in robotics. Reinforcement learning presents an appealing approach for automating the controller design process and has been able to produce remarkably robust controllers when trained in a suitable range of environments. However, it is difficult to predict all likely conditions the robot will encounter during deployment and enumerate them at training-time. What if instead of training contr…
University of Edinburgh（爱丁堡大学）；University of Porto（波尔图大学）；University of Aveiro（阿威罗大学）
This work aims to combine machine learning and control approaches for legged robots, and developed a hybrid framework to achieve new capabilities of balancing against external perturbations. The framework embeds a kernel which is a fully parametric closed-loop gait generator based on analytical control. On top of that, a neural network with symmetric partial data augmentation learns to automatically adjust the parameters for the gait kernel and to generate compensatory actions for all joints as the residual dynamics, thus significantly augmenting the stability under unexpected perturbations. T…
X-IONet: Cross-Platform Inertial Odometry Network for Pedestrian and Legged Robot
Intelligent Transportation Thrust, The Hong Kong University of Science and Technology (Guangzhou)（科技大学（广州）智能交通研究组）；Intelligent Transportation Thrust and Artificial Intelligence Thrust, The Hong Kong University of Science and Technology (Guangzhou)（科技大学（广州）智能交通研究组和人工智能研究组）；Division of Emerging Interdisciplinary Areas, The Hong Kong University of Science and Technology（科技大学新兴交叉领域研究部）
Learning and Adapting Agile Locomotion Skills by Transferring Experience
UC Berkeley（加州大学伯克利分校）；Berkeley AI Research（伯克利人工智能研究院）；Google Research（谷歌研究院）；Georgia Institute of Technology（佐治亚理工学院）；Simon Fraser University（西蒙菲莎大学）
Robotics at Google（谷歌机器人）；Georgia Institute of Technology（佐治亚理工学院）
Developing agile behaviors for legged robots remains a challenging problem. While deep reinforcement learning is a promising approach, learning truly agile behaviors typically requires tedious reward shaping and careful curriculum design. We formulate agile locomotion as a multi-stage learning problem in which a mentor guides the agent throughout the training. The mentor is optimized to place a checkpoint to guide the movement of the robot's center of mass while the student (i.e. the robot) learns to reach these checkpoints. Once the student can solve the task, we teach the student to perform …
Decision-Driven Semantic Object Exploration for Legged Robots via Confidence-Calibrated Perception and Topological Subgoal Selection
Robotics and Autonomous Systems Thrust, The Hong Kong University of Science and Technology (Guangzhou), China（机器人与自主系统方向，香港科技大学（广州））；Department of Mechanical and Energy Engineering, Southern University of Science and Technology, China（机械与能源工程系，南方科技大学）
Due to the highly complex environment present during the DARPA Subterranean Challenge, all six funded teams relied on legged robots as part of their robotic team. Their unique locomotion skills of being able to step over obstacles require special considerations for navigation planning. In this work, we present and examine ArtPlanner, the navigation planner used by team CERBERUS during the Finals. It is based on a sampling-based method that determines valid poses with a reachability abstraction and uses learned foothold scores to restrict areas considered safe for stepping. The resulting planni…
Teaching Robots Like Dogs: Learning Agile Navigation from Luring, Gesture, and Speech
Department of Artificial Intelligence, Korea University（人工智能系，韩国大学）；Department of Computer Science, ETH Zurich（计算机科学系，苏黎世联邦理工学院）；Department of Mechanical and Aerospace Engineering, UCLA（机械与航空航天工程系，加州大学洛杉矶分校）
GUIDE: Goal-Initialized Directional Understanding for End-to-End Legged Navigation
Institute of Cyber-Systems and Control, Zhejiang University（浙江大学控制科学与工程学院）；Institute of Artificial Intelligence (TeleAI), China Telecom（中国电信人工智能研究院（TeleAI））；Oxford Robotics Institute, University of Oxford（牛津大学牛津机器人研究所）；Center for Robotics, University of Bonn（波恩大学机器人中心）；Department of Mechanical Engineering, Massachusetts Institute of Technology（麻省理工学院机械工程系）
