This study proposes a hierarchically integrated framework for safe task and motion planning (TAMP) of bipedal locomotion in a partially observable environment with dynamic obstacles and uneven terrain. The high-level task planner employs linear temporal logic (LTL) for a reactive game synthesis between the robot and its environment and provides a formal guarantee on navigation safety and task completion. To address environmental partial observability, a belief abstraction is employed at the high-level navigation planner to estimate the dynamic obstacles' location. Accordingly, a synthesized action planner sends a set of locomotion actions to the middle-level motion planner, while incorporating safe locomotion specifications extracted from safety theorems based on a reduced-order model (ROM) of the locomotion process. The motion planner employs the ROM to design safety criteria and a sampling algorithm to generate non-periodic motion plans that accurately track high-level actions. At the low level, a foot placement controller based on an angular-momentum linear inverted pendulum model is implemented and integrated with an ankle-actuated passivity-based controller for full-body trajectory tracking. To address external perturbations, this study also investigates safe sequential composition of the keyframe locomotion state and achieves robust transitions against external perturbations through reachability analysis. The overall TAMP framework is validated with extensive simulations and hardware experiments on bipedal walking robots Cassie and Digit designed by Agility Robotics.
Politecnico di Milano（米兰理工大学）；Istituto Italiano di Tecnologia（意大利技术研究院）
Hybrid wheeled-legged locomotion is a navigation paradigm only recently opened up by novel robotic designs,e.g. the centaur-type humanoid CENTAURO [1] or the quadruped ANYmal [2] in its configuration featuring non-steerable wheels. The term Hybrid Locomotion is hereafter used to indicate a particular type of locomotion, achieved with simultaneous and coordinate use of legs and wheels,see Fig. 1. Such choice stems at the intersection between legged locomotion and the simpler wheeled navigation, in order to get the best from both techniques: agility and ability to traverse uneven terrains from t…
This paper reports on developing an integrated framework for safety-aware informative motion planning suitable for legged robots. The information-gathering planner takes a dense stochastic map of the environment into account, while safety constraints are enforced via Control Barrier Functions (CBFs). The planner is based on the Incrementally-exploring Information Gathering (IIG) algorithm and allows closed-loop kinodynamic node expansion using a Model Predictive Control (MPC) formalism. Robotic exploration and information gathering problems are inherently path-dependent problems. That is, the …
ETH Zürich（苏黎世联邦理工学院）；California Institute of Technology（加州理工学院）
The problem of dynamic locomotion over rough terrain requires both accurate foot placement together with an emphasis on dynamic stability. Existing approaches to this problem prioritize immediate safe foot placement over longer term dynamic stability considerations, or relegate the coordination of foot placement and dynamic stability to heuristic methods. We propose a multi-layered locomotion framework that unifies Control Barrier Functions (CBFs) with Model Predictive Control (MPC) to simultaneously achieve safe foot placement and dynamic stability. Our approach incorporates CBF based safety …
This paper presents an integrated system for performing precision harvesting missions using a legged harvester. Our harvester performs a challenging task of autonomous navigation and tree grabbing in a confined, GPS denied forest environment. Strategies for mapping, localization, planning, and control are proposed and integrated into a fully autonomous system. The mission starts with a human mapping the area of interest using a custom-made sensor module. Subsequently, a human expert selects the trees for harvesting. The sensor module is then mounted on the machine and used for localization wit…
Generating natural and physically feasible motions for legged robots has been a challenging problem due to its complex dynamics. In this work, we introduce a novel learning-based framework of autoregressive motion planner (ARMP) for quadruped locomotion and navigation. Our method can generate motion plans with an arbitrary length in an autoregressive fashion, unlike most offline trajectory optimization algorithms for a fixed trajectory length. To this end, we first construct the motion library by solving a dense set of trajectory optimization problems for diverse scenarios and parameter settin…
Princeton University（普林斯顿大学）；Google Research（谷歌研究院）；Georgia Institute of Technology（佐治亚理工学院）
Designing control policies for legged locomotion is complex due to the under-actuated and non-continuous robot dynamics. Model-free reinforcement learning provides promising tools to tackle this challenge. However, a major bottleneck of applying model-free reinforcement learning in real world is safety. In this paper, we propose a safe reinforcement learning framework that switches between a safe recovery policy that prevents the robot from entering unsafe states, and a learner policy that is optimized to complete the task. The safe recovery policy takes over the control when the learner polic…
A Survey of Legged Robotics in Non-Inertial Environments: Past, Present, and Future
School of Mechanical Engineering, Purdue University（帕克大学机械工程学院）；Department of Mechanical and Aerospace Engineering, Rutgers University（新泽西罗格斯大学机械与航空航天工程系）；Department of Robotics, University of Michigan（密歇根大学机器人系）
Georgia Institute of Technology（佐治亚理工学院）；Carnegie Mellon University（卡内基梅隆大学）
This paper describes a hierarchical solution consisting of a multi-phase planner and a low-level safe controller to jointly solve the safe navigation problem in crowded, dynamic, and uncertain environments. The planner employs dynamic gap analysis and trajectory optimization to achieve collision avoidance with respect to the predicted trajectories of dynamic agents within the sensing and planning horizon and with robustness to agent uncertainty. To address uncertainty over the planning horizon and real-time safety, a fast reactive safe set algorithm (SSA) is adopted, which monitors and modifie…
Istituto Italiano di Tecnologia（意大利理工学院）；University of Notre Dame（圣母大学）
We present a novel control strategy for dynamic legged locomotion in complex scenarios, that considers information about the morphology of the terrain in contexts when only on-board mapping and computation are available. The strategy is built on top of two main elements: first a contact sequence task that provides safe foothold locations based on a convolutional neural network to perform fast and continuous evaluation of the terrain in search of safe foothold locations; then a model predictive controller that considers the foothold locations given by the contact sequence task to optimize targe…
Robotic mobility in microgravity is necessary to expand human utilization and exploration of outer space. Bio-inspired multi-legged robots are a possible solution for safe and precise locomotion. However, a dynamic motion of a robot in microgravity can lead to failures due to gripper detachment caused by excessive motion reactions. We propose a novel Reaction-Aware Motion Planning (RAMP) to improve locomotion safety in microgravity, decreasing the risk of losing contact with the terrain surface by reducing the robot's momentum change. RAMP minimizes the swing momentum with a Low-Reaction Swing…
Multi-contact Stochastic Predictive Control for Legged Robots with Contact Locations Uncertainty
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Tandon School of Engineering, New York University（纽约大学坦登工程学院）；Munich Institute of Robotics and Machine Intelligence, Technical University of Munich（慕尼黑工业大学慕尼黑机器人与机器智能研究所）；University of Trento（特伦托大学）
KiVi: Kinesthetic-Visuospatial Integration for Dynamic and Safe Egocentric Legged Locomotion
MARMot Lab, National University of Singapore（新加坡国立大学MARMot实验室）；Center of X-Mechanics, Zhejiang University（浙江大学X力学中心）；Robot and Robot Intelligence Lab, Zhejiang University（浙江大学机器人与机器人智能实验室）
Popular navigation stacks implemented on top of open-source frameworks such as ROS(Robot Operating System) and ROS2 represent the robot workspace using a discretized 2D occupancy grid. This method, while requiring less computation, restricts the use of such navigation stacks to wheeled robots navigating on flat surfaces. In this paper, we present a navigation stack that uses a volumetric representation of the robot workspace, and hence can be extended to aerial and legged robots navigating through uneven terrain. Additionally, we present a new sampling-based motion planning algorithm which int…
Ohio State University（俄亥俄州立大学）；Southern University of Science and Technology (SUSTech)（南方科技大学）
The dynamic response of the legged robot locomotion is non-Lipschitz and can be stochastic due to environmental uncertainties. To test, validate, and characterize the safety performance of legged robots, existing solutions on observed and inferred risk can be incomplete and sampling inefficient. Some formal verification methods suffer from the model precision and other surrogate assumptions. In this paper, we propose a scenario sampling based testing framework that characterizes the overall safety performance of a legged robot by specifying (i) where (in terms of a set of states) the robot is …
Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged Robots
Robotic Systems Lab, ETH Zurich（苏黎世联邦理工学院机器人系统实验室）；Neuromeka；Swiss-Mile Robotics AG（Swiss-Mile机器人公司）；The work was carried out during their stay at（该工作是在他们停留期间完成的）
Safe Navigation in Dynamic Environments Using Data-Driven Koopman Operators and Conformal Prediction
Mechanical Engineering and Mechanics Department at Lehigh University（莱维大学机械工程与力学系）；Department of Mechanical Engineering at University of California Riverside（加州大学河滨分校机械工程系）
Arizona State University（亚利桑那州立大学）；Toyota Research Institute of North America（丰田北美研究院）
In this paper, we present a decentralized control approach based on a Nonlinear Model Predictive Control (NMPC) method that employs barrier certificates for safe navigation of multiple nonholonomic wheeled mobile robots in unknown environments with static and/or dynamic obstacles. This method incorporates a Learned Barrier Function (LBF) into the NMPC design in order to guarantee safe robot navigation, i.e., prevent robot collisions with other robots and the obstacles. We refer to our proposed control approach as NMPC-LBF. Since each robot does not have a priori knowledge about the obstacles a…
Noah’s Ark Lab, Huawei Technologies Canada, Ltd.（华为技术加拿大有限公司诺亚方舟实验室）；University of Alberta（阿尔伯塔大学）
We propose a method to tackle the problem of mapless collision-avoidance navigation where humans are present using 2D laser scans. Our proposed method uses ego-safety to measure collision from the robot's perspective while social-safety to measure the impact of our robot's actions on surrounding pedestrians. Specifically, the social-safety part predicts the intrusion impact of our robot's action into the interaction area with surrounding humans. We train the policy using reinforcement learning on a simple simulator and directly evaluate the learned policy in Gazebo and real robot tests. Experi…
Safety concerns during the operation of legged robots must be addressed to enable their widespread use. Machine learning-based control methods that use model-based constraints provide promising means to improve robot safety. This study presents a modular safety filter to improve the safety of a legged robot, i.e., reduce the chance of a fall. The prerequisite is the availability of a robot that is capable of locomotion, i.e., a nominal controller exists. During locomotion, terrain properties around the robot are estimated through machine learning which uses a minimal set of proprioceptive sign…
Seq-DeepIPC: Sequential Sensing for End-to-End Control in Legged Robot Navigation
Department of Computer Science and Electronics, Universitas Gadjah Mada（计算机科学与电子系，加查马达大学）；Department of Computer Science and Engineering, Toyohashi University of Technology（计算机科学与工程系，东福冈技术大学）
This paper considers the problem of safe autonomous navigation in unknown environments, relying on local obstacle sensing. We consider a control-affine nonlinear robot system subject to bounded input noise and rely on feedback linearization to determine ellipsoid output bounds on the closed-loop robot trajectory under stabilizing control. A virtual governor system is developed to adaptively track a desired navigation path, while relying on the robot trajectory bounds to slow down if safety is endangered and speed up otherwise. The main contribution is the derivation of theoretical guarantees f…
This paper extends the gap-based navigation technique in Potential Gap by guaranteeing safety for nonholonomic robots for all tiers of the local planner hierarchy, so called Safer Gap. The first tier generates a Bezier-based collision-free path through gaps. A subset of navigable free-space from the robot through a gap, called the keyhole, is defined to be the union of the largest collision-free disc centered on the robot and a trapezoidal region directed through the gap. It is encoded by a shallow neural network zeroing barrier function (ZBF). Nonlinear model predictive control (NMPC), with K…
Safe autonomous navigation in unknown environments is an important problem for mobile robots. This paper proposes techniques to learn the dynamics model of a mobile robot from trajectory data and synthesize a tracking controller with safety and stability guarantees. The state of a rigid-body robot usually contains its position, orientation, and generalized velocity and satisfies Hamilton's equations of motion. Instead of a hand-derived dynamics model, we use a dataset of state-control trajectories to train a translation-equivariant nonlinear Hamiltonian model represented as a neural ordinary d…
