Despite the progress in legged robotic locomotion, autonomous navigation in unknown environments remains an open problem. Ideally, the navigation system utilizes the full potential of the robots' locomotion capabilities while operating within safety limits under uncertainty. The robot must sense and analyze the traversability of the surrounding terrain, which depends on the hardware, locomotion control, and terrain properties. It may contain information about the risk, energy, or time consumption needed to traverse the terrain. To avoid hand-crafted traversability cost functions we propose to collect traversability information about the robot and locomotion policy by simulating the traversal over randomly generated terrains using a physics simulator. Thousand of robots are simulated in parallel controlled by the same locomotion policy used in reality to acquire 57 years of real-world locomotion experience equivalent. For deployment on the real robot, a sparse convolutional network is trained to predict the simulated traversability cost, which is tailored to the deployed locomotion policy, from an entirely geometric representation of the environment in the form of a 3D voxel-occupancy map. This representation avoids the need for commonly used elevation maps, which are error-prone in the presence of overhanging obstacles and multi-floor or low-ceiling scenarios. The effectiveness of the proposed traversability prediction network is demonstrated for path planning for the legged robot ANYmal in various indoor and natural environments.
PILOT: A Perceptive Integrated Low-level Controller for Loco-manipulation over Unstructured Scenes
School of Automation and Intelligent Sensing（自动化与智能感知学院）；Shanghai Jiao Tong University（上海交通大学）；Global College（全球学院）；school of Computer Science（计算机科学学院）；Shanghai Key Laboratory of Navigation and Location Based Services（导航与位置基于服务上海市重点实验室）
This work reports on developing a deep inverse reinforcement learning method for legged robots terrain traversability modeling that incorporates both exteroceptive and proprioceptive sensory data. Existing works use robot-agnostic exteroceptive environmental features or handcrafted kinematic features; instead, we propose to also learn robot-specific inertial features from proprioceptive sensory data for reward approximation in a single deep neural network. Incorporating the inertial features can improve the model fidelity and provide a reward that depends on the robot's state during deployment…
Attaining animal-like legged locomotion on rough outdoor terrain with sparse foothold affordances -a primary use-case for legs vs other forms of locomotion- is a largely open problem. New advancements in control and perception have enabled bipeds to walk on flat and uneven indoor environments. But tasks that require reliable contact with unstructured world surfaces, for example walking on natural rocky terrain, need new perception and control algorithms. This thesis introduces 3D perception algorithms for contact tasks such as foot placement in rough terrain environments. We introduce a new me…
University of California, Santa Cruz（加州大学圣克鲁兹分校）；Genomics Institute, University of California, Santa Cruz（加州大学圣克鲁兹分校基因组学研究所）；Howard Hughes Medical Institute（霍华德·休斯医学研究所）
We propose a modular architecture for neuromorphic closed-loop control based on bistable relaxation oscillator modules consisting of three spiking neurons each. Like its biological prototypes, this basic component is robust to parameter variation but can be modulated by external inputs. By combining these modules, we can construct a neural state machine capable of generating the cyclic or repetitive behaviors necessary for legged locomotion. A concrete case study for the approach is provided by a modular robot constructed from flexible plastic volumetric pixels, in which we produce a forward c…
The semantics of the environment, such as the terrain type and property, reveals important information for legged robots to adjust their behaviors. In this work, we present a framework that learns semantics-aware locomotion skills from perception for quadrupedal robots, such that the robot can traverse through complex offroad terrains with appropriate speeds and gaits using perception information. Due to the lack of high-fidelity outdoor simulation, our framework needs to be trained directly in the real world, which brings unique challenges in data efficiency and safety. To ensure sample effic…
Watch Your Step: Learning Semantically-Guided Locomotion in Cluttered Environment
School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）；School of Mechanical and Mining Engineering, The University of Queensland（昆士兰大学机械与采矿工程学院）
University of Edinburgh（爱丁堡大学）；University College London（伦敦大学学院）
To proactively navigate and traverse various terrains, active use of visual perception becomes indispensable. We aim to investigate the feasibility and performance of using sparse visual observations to achieve perceptual locomotion over a range of common terrains (steps, ramps, gaps, and stairs) in human-centered environments. We formulate a selection of sparse visual inputs suitable for locomotion over the terrains of interest, and propose a learning framework to integrate exteroceptive and proprioceptive states. We specifically design the state observations and a training curriculum to lear…
End-to-End Reinforcement Learning for Torque Based Variable Height Hopping
Indian Institute of Technology (Banaras Hindu University)（印度理工学院（Banaras Hindu大学））；DFKI GmbH Robotics Innovation Center（德国人工智能研究中心机器人创新中心）；Kyoto University（京都大学）
MOBIUS: A Multi-Modal Bipedal Robot that can Walk, Crawl, Climb, and Roll
Mitsubishi Electric Research Laboratories（三菱电机研究实验室）；Robotic Systems Lab（机器人系统实验室）；Robotics and Mechanisms Laboratory（机器人与机构实验室）；Department of Mechanical and Aerospace Engineering, University of California, Los Angeles（加州大学洛杉矶分校机械与航空航天工程系）
Rectilinear forms of snake-like robotic locomotion are anticipated to be an advantage in obstacle-strewn scenarios characterizing urban disaster zones, subterranean collapses, and other natural environments. The elongated, laterally-narrow footprint associated with these motion strategies is well-suited to traversal of confined spaces and narrow pathways. Navigation and path planning in the absence of global sensing, however, remains a pivotal challenge to be addressed prior to practical deployment of these robotic mechanisms. Several challenges related to visual processing and localization ne…
NASA Jet Propulsion Laboratory, California Institute of Technology（美国国家航空航天局喷气推进实验室，加州理工学院）；University of Goettingen, University Medical Center Goettingen（哥廷根大学，哥廷根大学医学中心）；Institute for Robotics and Intelligent Machines, Georgia Institute of Technology（佐治亚理工学院机器人与智能机器研究所）；Swiss Federal Institute of Technology (ETH Zürich)（苏黎世联邦理工学院）；California Institute of Technology（加州理工学院）
Legged robots can traverse a wide variety of terrains, some of which may be challenging for wheeled robots, such as stairs or highly uneven surfaces. However, quadruped robots face stability challenges on slippery surfaces. This can be resolved by adjusting the robot's locomotion by switching to more conservative and stable locomotion modes, such as crawl mode (where three feet are in contact with the ground always) or amble mode (where one foot touches down at a time) to prevent potential falls. To tackle these challenges, we propose an approach to learn a model from past robot experience for…
Towards Path-Creative Navigation: Robot Navigation through Embodied Interaction
School of Computer Science, Shanghai Jiao Tong University（上海交通大学计算机科学学院）；College of Information Science and Technology, Eastern Institute of Technology（东方理工大学信息科学与技术学院）；School of Mechanical and Power Engineering, Zhengzhou university（郑州大学机械与动力工程学院）；University of Waterloo（滑铁卢大学）
