Safe Control of Quadruped in Varying Dynamics via Safety Index Adaptation
Mechanical Engineering Department, Carnegie Mellon University（卡内基梅隆大学机械工程系）；Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）；Mechanical Engineering Department, New Mexico Institute of Mining and Technology（新墨西哥州矿业与技术研究所机械工程系）
Kai S. Yun, Rui Chen, Chase Dunaway, John M. Dolan, Changliu Liu
Varying dynamics pose a fundamental difficulty when deploying safe control laws in the real world. Safety Index Synthesis (SIS) deeply relies on the system dynamics and once the dynamics change, the previously synthesized safety index becomes invalid. In this work, we show the real-time efficacy of Safety Index Adaptation (SIA) in varying dynamics. SIA enables real-time adaptation to the changing dynamics so that the adapted safe control law can still guarantee 1) forward invariance within a safe region and 2) finite time convergence to that safe region. This work employs SIA on a package-carrying quadruped robot, where the payload weight changes in real-time. SIA updates the safety index when the dynamics change, e.g., a change in payload weight, so that the quadruped can avoid obstacles while achieving its performance objectives. Numerical study provides theoretical guarantees for SIA and a series of hardware experiments demonstrate the effectiveness of SIA in real-world deployment in avoiding obstacles under varying dynamics.
Dual-Quadruped Collaborative Transportation in Narrow Environments via Safe Reinforcement Learning
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Advanced Remanufacturing & Technology Centre (ARTC)（先进再制造与技术中心）；Agency for Science, Technology and Research (A*STAR)（科技研究局）；Cheng Kar-Shun Robotics Institute, The Hong Kong University of Science and Technology（陈克舜机器人研究所，香港科技大学）
California Institute of Technology（加州理工学院）；University of Michigan（密歇根大学）；Argo AI（Argo AI公司）
Bringing dynamic robots into the wild requires a tenuous balance between performance and safety. Yet controllers designed to provide robust safety guarantees often result in conservative behavior, and tuning these controllers to find the ideal trade-off between performance and safety typically requires domain expertise or a carefully constructed reward function. This work presents a design paradigm for systematically achieving behaviors that balance performance and robust safety by integrating safety-aware Preference-Based Learning (PBL) with Control Barrier Functions (CBFs). Fusing these conc…
With the increasing prevalence of complex vision-based sensing methods for use in obstacle identification and state estimation, characterizing environment-dependent measurement errors has become a difficult and essential part of modern robotics. This paper presents a self-supervised learning approach to safety-critical control. In particular, the uncertainty associated with stereo vision is estimated, and adapted online to new visual environments, wherein this estimate is leveraged in a safety-critical controller in a robust fashion. To this end, we propose an algorithm that exploits the struc…
SAC-Loco: Safe and Adjustable Compliant Quadrupedal Locomotion
Department of Electrical and Computer Engineering, National University of Singapore（新加坡国立大学电子与计算机工程系）；School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）
Full-Body Dynamic Safety for Robot Manipulators: 3D Poisson Safety Functions for CBF-Based Safety Filters
Computing and Mathematical Sciences, California Institute of Technology（加州理工学院计算与数学科学系）；Mechanical and Civil Engineering, California Institute of Technology（加州理工学院机械与土木工程系）
Pre-Execution Safety Gate & Task Safety Contracts for LLM-Controlled Robot Systems
SMART Laboratory, Department of Computer and Information Technology, Purdue University（普渡大学计算机与信息技术系SMART实验室）；Department of Information and Telecommunication Engineering, Incheon National University（仁川国立大学信息与通信工程系）
Role-Adaptive Collaborative Formation Planning for Team of Quadruped Robots in Cluttered Environments
Robotics and Artificial Intelligence Group, Department of Computer, Electrical and Space Engineering, Lulea University of Technology（机器人与人工智能小组，计算机、电气与航天工程系，卢莱亚理工大学）
The Chinese University of Hong Kong（香港中文大学）；Multiscale Medical Robotics Center（多尺度医疗机器人中心）
Projected Inverse Dynamics Control (PIDC) is commonly used in robots subject to contact, especially in quadrupedal systems. Many methods based on such dynamics have been developed for quadrupedal locomotion tasks, and only a few works studied simple interactions between the robot and environment, such as pressing an E-stop button. To facilitate the interaction requiring exact force control for safety, we propose a novel interaction force control scheme for underactuated quadrupedal systems relying on projection techniques and Quadratic Programming (QP). This algorithm allows the robot to apply…
Department of Mechanical and Civil Engineering, Caltech（加州理工学院机械与土木工程系）；Department of Computing and Mathematical Sciences, Caltech（加州理工学院计算与数学科学系）；Department of Mechanical Engineering, Tufts University（塔夫茨大学机械工程系）
Robotic systems navigating in real-world settings require a semantic understanding of their environment to properly determine safe actions. This work aims to develop the mathematical underpinnings of such a representation -- specifically, the goal is to develop safety filters that are risk-aware. To this end, we take a two step approach: encoding an understanding of the environment via Poisson's equation, and associated risk via Laplace guidance fields. That is, we first solve a Dirichlet problem for Poisson's equation to generate a safety function that encodes system safety as its 0-superleve…
CBF-QP safety filters are pointwise minimizers of the control effort at a given state vector, i.e., myopically optimal at each time instant. But are they optimal over the entire infinite time horizon? What does it even mean for a controlled dynamic systems to be "optimally safe" as opposed to, conventionally "optimally stable"? When disturbances, deterministic and stochastic, have unknown upper bounds, how should safety be defined to allow a graceful degradation under disturbances? Can safety filters be designed to guarantee such weaker safety properties as well as the optimality of safety ove…
It is widely acknowledged that verifying the safety of autonomous driving strategies requires a substantial body of simulation testing and road testing. In recent years, the formal safety methods represented by Responsibility-Sensitive Safety (RSS) have encouraged low-cost autonomous driving safety research, benefitting from its accurate assessment of safety and clear division of responsibilities. However, how to maintain traffic efficiency while ensuring safety remains a challenge. To address this problem, this paper proposes a formulized negotiation-based lane-changing strategy that makes a …
This paper presents a safe reinforcement learning system for automated driving that benefits from multimodal future trajectory predictions. We propose a safety system that consists of two safety components: a heuristic safety and a learning-based safety. The heuristic safety module is based on common driving rules. On the other hand, the learning-based safety module is a data-driven safety rule that learns safety patterns from driving data. Specifically, it utilizes mixture density recurrent neural networks (MD-RNN) for multimodal future trajectory predictions to accelerate the learning progre…
The development of safety-critical systems requires the control of hazards that can potentially cause harm. To this end, safety engineers rely during the development phase on architectural solutions, called safety patterns, such as safety monitors, voters, and watchdogs. The goal of these patterns is to control (identified) faults that can trigger hazards. Safety patterns can control such faults by e.g., increasing the redundancy of the system. Currently, the reasoning of which pattern to use at which part of the target system to control which hazard is documented mostly in textual form or by …
