Legged robots have demonstrated high efficiency and effectiveness in unstructured and dynamic environments. However, it is still challenging for legged robots to achieve rapid and efficient locomotion on deformable, yielding substrates, such as granular terrains. We present an enhanced resistive force model for bipedal walkers on soft granular terrains by introducing effective intrusion depth correction. The enhanced force model captures fundamental kinetic results considering the robot foot shape, walking gait speed variation, and energy expense. The model is validated by extensive foot intrusion experiments with a bipedal robot. The results confirm the model accuracy on the given type of granular terrains. The model can be further integrated with the motion control of bipedal robotic walkers.
Instituto Italiano di Technologia（意大利理工学院）；NASA Jet Propulsion Laboratory（美国国家航空航天局喷气推进实验室）；Northeastern University（东北大学）
Legged robots need to make contact with irregular surfaces, when operating in unstructured natural terrains. Representing and perceiving these areas to reason about potential contact between a robot and its surrounding environment, is still largely an open problem. This paper introduces a new framework to model and map local rough terrain surfaces, for tasks such as bipedal robot foot placement. The system operates in real-time, on data from an RGB-D and an IMU sensor. We introduce a set of parametrized patch models and an algorithm to fit them in the environment. Potential contacts are identi…
University of Oslo（奥斯陆大学）；Norwegian Defence Research Institute（挪威国防研究所）
Legged robots promise higher versatility and the ability to traverse much more difficult terrains than their wheeled counterparts. Even though the use of legged robots have increased drastically in the last few years, they are still not close to the performance seen from legged animals in nature. Robotic legs are typically fairly simple mechanically, and few feature an ankle joint, even though most land mammals have one. The ankle could be a key to better performance and stability for legged robots, and in this paper we investigate how the use of an ankle in a bipedal robot could improve its p…
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…
Terrain-Aware Quadrupedal Locomotion via Reinforcement Learning
Tencent Robotics X（腾讯Robotics X）；The Chinese University of Hong Kong（香港中文大学）；Southern University of Science and Technology（南方科技大学）；University of Alberta（阿尔伯塔大学）
Despite many accomplishments by legged robot designers, state-of-the-art bipedal robots are prone to falling over, cannot negotiate extremely rough terrains and cannot directly regulate unilateral contact forces. Our objective is to integrate merits of legged and aerial robots in a single platform. We will show that the thrusters in a bipedal legged robot called Harpy can be leveraged to stabilize the robot's frontal dynamics and permit jumping over large obstacles which is an unusual capability not reported before. In addition, we will capitalize on the thrusters action in Harpy and will show…
National Institute of Advanced Industrial Science and Technology (AIST)（产业技术综合研究所）；University of Tsukuba（筑波大学）
Deep reinforcement learning (RL) based controllers for legged robots have demonstrated impressive robustness for walking in different environments for several robot platforms. To enable the application of RL policies for humanoid robots in real-world settings, it is crucial to build a system that can achieve robust walking in any direction, on 2D and 3D terrains, and be controllable by a user-command. In this paper, we tackle this problem by learning a policy to follow a given step sequence. The policy is trained with the help of a set of procedurally generated step sequences (also called foot…
CoRIS Institute, Oregon State University（俄勒冈州立大学CoRIS研究所）；Agility Robotics（敏捷机器人公司）
Accurate and precise terrain estimation is a difficult problem for robot locomotion in real-world environments. Thus, it is useful to have systems that do not depend on accurate estimation to the point of fragility. In this paper, we explore the limits of such an approach by investigating the problem of traversing stair-like terrain without any external perception or terrain models on a bipedal robot. For such blind bipedal platforms, the problem appears difficult (even for humans) due to the surprise elevation changes. Our main contribution is to show that sim-to-real reinforcement learning (…
Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS)（深圳市人工智能与机器人研究院）；Institute of Robotics and Intelligent Manufacturing (IRIM), The Chinese University of Hong Kong (CUHK)（香港中文大学机器人与智能制造研究院）；George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology（佐治亚理工学院乔治·W·伍德拉夫机械工程学院）；Beijing Institute for General Artificial Intelligence（北京通用人工智能研究院）
Dynamic quadrupedal locomotion over rough terrains reveals remarkable progress over the last few decades. Small-scale quadruped robots are adequately flexible and adaptable to traverse uneven terrains along sagittal direction, such as slopes and stairs. To accomplish autonomous locomotion navigation in complex environments, spinning is a fundamental yet indispensable functionality for legged robots. However, spinning behaviors of quadruped robots on uneven terrain often exhibit position drifts. Motivated by this problem, this study presents an algorithmic method to enable accurate spinning mot…
We present a highly reactive controller which enables bipedal robots to blindly walk over various kinds of uneven terrains while resisting pushes. The high level motion planner does fast online optimization for footstep locations and Center of Mass (CoM) height using the decoupled actuated Spring Loaded Inverted Pendulum (aSLIP) model. The decoupled aSLIP model simplifies the original aSLIP with Linear Inverted Pendulum (LIP) dynamics in horizontal states and spring dynamics in the vertical state. The motion planning can be formulated as a discrete-time Model Predictive Control (MPC) and solve…
Bipedal-Walking-Dynamics Model on Granular Terrains
Department of Mechanical and Aerospace Engineering, Rutgers University（罗格斯大学机械与航空航天工程系）；Bridgewater Raritan High School（布里奇沃特-拉里坦高中）；State Key Lab of Fluid Power Transmission and Control and the School of Mechanical Engineering, Zhejiang University（浙江大学流体动力传动与控制国家重点实验室和机械工程学院）
Istituto Italiano di Tecnologia（意大利理工学院）；University of Oxford（牛津大学）
Legged robots can outperform wheeled machines for most navigation tasks across unknown and rough terrains. For such tasks, visual feedback is a fundamental asset to provide robots with terrain-awareness. However, robust dynamic locomotion on difficult terrains with real-time performance guarantees remains a challenge. We present here a real-time, dynamic foothold adaptation strategy based on visual feedback. Our method adjusts the landing position of the feet in a fully reactive manner, using only on-board computers and sensors. The correction is computed and executed continuously along the sw…
Whole-Body Control With Terrain Estimation of A 6-DoF Wheeled Bipedal Robot
School of Intelligent Systems Engineering, Sun Yat-sen University（中山大学智能系统工程学院）；Direct Drive Technology Limited（直接驱动技术有限公司）；Suzhou Nuclear Power Research Institute Co., Ltd（苏州核电研究所有限公司）
Indian Institute of Technology (BHU) Varanasi（瓦拉纳西印度理工学院（BHU））；Ohio State University（俄亥俄州立大学）；Indian Institute of Science（印度科学学院）
In this work, we demonstrate robust walking in the bipedal robot Digit on uneven terrains by just learning a single linear policy. In particular, we propose a new control pipeline, wherein the high-level trajectory modulator shapes the end-foot ellipsoidal trajectories, and the low-level gait controller regulates the torso and ankle orientation. The foot-trajectory modulator uses a linear policy and the regulator uses a linear PD control law. As opposed to neural network-based policies, the proposed linear policy has only 13 learnable parameters, thereby not only guaranteeing sample efficient …
Robotics Institute, University of Michigan（密歇根大学机器人研究所）
We propose and experimentally demonstrate a reactive planning system for bipedal robots on unexplored, challenging terrains. The system consists of a low-frequency planning thread (5 Hz) to find an asymptotically optimal path and a high-frequency reactive thread (300 Hz) to accommodate robot deviation. The planning thread includes: a multi-layer local map to compute traversability for the robot on the terrain; an anytime omnidirectional Control Lyapunov Function (CLF) for use with a Rapidly Exploring Random Tree Star (RRT*) that generates a vector field for specifying motion between nodes; a s…
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
Bipedal running is a difficult task to realize in robots, since the trunk is underactuated and control is limited by intermittent ground contacts. Stabilizing the trunk becomes even more challenging if the terrain is uneven and causes perturbations. One bio-inspired method to achieve postural stability is the virtual point (VP) control, which is able to generate natural motion. However, so far it has only been studied for level running. In this work, we investigate whether the VP control method can accommodate single step-downs and downhill terrains. We provide guidelines on the model and cont…
Learning Terrain Aware Bipedal Locomotion via Reduced Dimensional Perceptual Representations
Electrical and Computer Engineering, The Ohio State University（电气与计算机工程系，俄亥俄州立大学）；Mechanical and Aerospace Engineering, The Ohio State University（机械与航空航天工程系，俄亥俄州立大学）
Learning Bipedal Walking for Humanoids with Current Feedback
CNRS-AIST JRL (Joint Robotics Laboratory) IRL（法国国家科学研究中心-产业技术综合研究所联合机器人实验室国际联合实验室）；National Institute of Advanced Industrial Science and Technology (AIST)（产业技术综合研究所）；University of Tsukuba（筑波大学）；Stanford University（斯坦福大学）
Head Stabilization for Wheeled Bipedal Robots via Force-Estimation-Based Admittance Control
School of Intelligent Systems Engineering, Sun Yat-sen University（中山大学智能系统工程学院）；GAC R&D Center（GAC研发中心）；Direct Drive Technology Limited（直接驱动技术有限公司）；Suzhou Nuclear Power Research Institute Co., Ltd（苏州核电研究所有限公司）
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Amirkabir University of Technology（阿米尔卡比尔理工大学）；Tandon School of Engineering, New York University（纽约大学坦登工程学院）
In this paper, we present a novel two-level variable Horizon Model Predictive Control (VH-MPC) framework for bipedal locomotion. In this framework, the higher level computes the landing location and timing (horizon length) of the swing foot to stabilize the unstable part of the center of mass (CoM) dynamics, using feedback from the CoM states. The lower level takes into account the swing foot dynamics and generates dynamically consistent trajectories for landing at the desired time as close as possible to the desired location. To do that, we use a simplified model of the robot dynamics project…
Motivated towards achieving multi-modal locomotion, in this paper, we develop a framework for a bipedal robot to dynamically ride a pair of Hovershoes over various terrain. Our developed control strategy enables the Cassie bipedal robot to interact with the Hovershoes to balance, regulate forward and rotational velocities, achieve fast turns, and move over flat terrain, slopes, stairs, and rough outdoor terrain. Our sensor suite comprising of tracking and depth cameras for visual SLAM as well as our Dijkstra-based global planner and timed elastic band-based local planning framework enables us …
Collaborative Robotics and Intelligent Systems Institute, Oregon State University（俄勒冈州立大学协作机器人与智能系统研究所）；Agility Robotics（Agility Robotics（敏捷机器人公司））
Recently, work on reinforcement learning (RL) for bipedal robots has successfully learned controllers for a variety of dynamic gaits with robust sim-to-real demonstrations. In order to maintain balance, the learned controllers have full freedom of where to place the feet, resulting in highly robust gaits. In the real world however, the environment will often impose constraints on the feasible footstep locations, typically identified by perception systems. Unfortunately, most demonstrated RL controllers on bipedal robots do not allow for specifying and responding to such constraints. This missi…
University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校）；University of Michigan（密歇根大学）；Ohio State University（俄亥俄州立大学）；Purdue University（普渡大学）
Controller design for bipedal walking on dynamic rigid surfaces (DRSes), which are rigid surfaces moving in the inertial frame (e.g., ships and airplanes), remains largely uninvestigated. This paper introduces a hierarchical control approach that achieves stable underactuated bipedal robot walking on a horizontally oscillating DRS. The highest layer of our approach is a real-time motion planner that generates desired global behaviors (i.e., the center of mass trajectories and footstep locations) by stabilizing a reduced-order robot model. One key novelty of this layer is the derivation of the …
University of Tehran（德黑兰大学）；École polytechnique fédérale de Lausanne（洛桑联邦理工学院）
In this paper, we present a novel control architecture for the online adaptation of bipedal locomotion on inclined obstacles. In particular, we introduce a novel, cost-effective, and versatile foot sensor to detect the proximity of the robot's feet to the ground (bump sensor). By employing this sensor, feedback controllers are implemented to reduce the impact forces during the transition of the swing to stance phase or steeping on inclined unseen obstacles. Compared to conventional sensors based on contact reaction force, this sensor detects the distance to the ground or obstacles before the f…
This paper presents a novel Adaptive-frequency MPC framework for bipedal locomotion over terrain with uneven stepping stones. In detail, we intend to achieve adaptive foot placement and gait period for bipedal periodic walking gait with this MPC, in order to traverse terrain with discontinuities without slowing down. We pair this adaptive-frequency MPC with a kino-dynamics trajectory optimization for optimal gait periods, center of mass (CoM) trajectory, and foot placements. We use whole-body control (WBC) along with adaptive-frequency MPC to track the optimal trajectories from the offline opt…
Robust Humanoid Walking on Compliant and Uneven Terrain with Deep Reinforcement Learning
CNRS-AIST JRL (Joint Robotics Laboratory) IRL, National Institute of Advanced Industrial Science and Technology (AIST), Japan（日本国家先进工业科学与技术研究院（AIST））；University of Tsukuba（筑波大学）；Department of Computer Science, Stanford University（斯坦福大学计算机科学系）
