A Safety-Aware Shared Autonomy Framework with BarrierIK Using Control Barrier Functions
Technical University of Darmstadt（德累斯顿技术大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；Robotics Institute Germany (RIG)（德国机器人研究所）；Centre for Cognitive Science（认知科学中心）；Honda Research Institute Europe GmbH（本田欧洲研究机构）
Berk Guler, Kay Pompetzki, Yuanzheng Sun, Simon Manschitz, Jan Peters
Shared autonomy blends operator intent with autonomous assistance. In cluttered environments, linear blending can produce unsafe commands even when each source is individually collision-free. Many existing approaches model obstacle avoidance through potentials or cost terms, which only enforce safety as a soft constraint. In contrast, safety-critical control requires hard guarantees. We investigate the use of control barrier functions (CBFs) at the inverse kinematics (IK) layer of shared autonomy, targeting post-blend safety while preserving task performance. Our approach is evaluated in simulation on representative cluttered environments and in a VR teleoperation study comparing pure teleoperation with shared autonomy. Across conditions, employing CBFs at the IK layer reduces violation time and increases minimum clearance while maintaining task performance. In the user study, participants reported higher perceived safety and trust, lower interference, and an overall preference for shared autonomy with our safety filter. Additional materials available at https://berkguler.github.io/barrierik.
With the increasing emphasis on the safe autonomy for robots, model-based safe control approaches such as Control Barrier Functions have been extensively studied to ensure guaranteed safety during inter-robot interactions. In this paper, we introduce the Parametric Control Barrier Function (Parametric-CBF), a novel variant of the traditional Control Barrier Function to extend its expressivity in describing different safe behaviors among heterogeneous robots. Instead of assuming cooperative and homogeneous robots using the same safe controllers, the ego robot is able to model the neighboring ro…
Shared autonomy provides a framework where a human and an automated system, such as a robot, jointly control the system's behavior, enabling an effective solution for various applications, including human-robot interaction. However, a challenging problem in shared autonomy is safety because the human input may be unknown and unpredictable, which affects the robot's safety constraints. If the human input is a force applied through physical contact with the robot, it also alters the robot's behavior to maintain safety. We address the safety issue of shared autonomy in real-time applications by p…
Control Barrier Corridors: From Safety Functions to Safe Sets
Department of Mechanical Engineering, Eindhoven University of Technology（埃因霍温理工大学机械工程系）；Department of Electrical and Computer Engineering, University of California San Diego（加州大学圣地亚哥分校电气与计算机工程系）
AssistDLO: Assistive Teleoperation for Deformable Linear Object Manipulation
TU Darmstadt, Institute for Intelligent Autonomous Systems, Computer Science Department（德累斯顿技术大学智能自主系统研究所，计算机科学系）；Honda Research Institute Europe GmbH（本田欧洲研究院）；German Research Center for AI (DFKI), Research Department: Systems AI for Robot Learning（德国人工智能研究中心（DFKI），研究部门：机器人学习的系统AI；黑森AI；德国机器人研究所（RIG）；认知科学中心）；Hessian.AI；Robotics Institute Germany (RIG)；Centre for Cognitive Science
Institute for Perception, Action and Behaviour, School of Informatics, The University of Edinburgh（爱丁堡大学信息学院感知、行动与行为研究所）
The deployment of robots in industrial and civil scenarios is a viable solution to protect operators from danger and hazards. Shared autonomy is paramount to enable remote control of complex systems such as legged robots, allowing the operator to focus on the essential tasks instead of overly detailed execution. To realize this, we propose a comprehensive control framework for inspection and intervention using a legged robot and validate the integration of multiple loco-manipulation algorithms optimised for improving the remote operation. The proposed control offers 3 operation modes: fully au…
Autonomous Multiple-Trolley Collection System with Nonholonomic Robots: Design, Control, and Implementation
Southern University of Science and Technology（南方科技大学）；Jiaxing Research Institute, Southern University of Science and Technology（南方科技大学嘉兴研究院）；Chinese University of Hong Kong（香港中文大学）；University of Alberta（阿尔伯塔大学）
SSP: Safety-guaranteed Surgical Policy via Joint Optimization of Behavioral and Spatial Constraints
Global College, Shanghai Jiao Tong University（上海交通大学全球学院）；Department of Computer Science and Engineering, The Chinese University of Hong Kong（香港中文大学计算机科学与工程系）；Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology（麻省理工学院计算机科学与人工智能实验室）
SHIELD: Safety on Humanoids via CBFs In Expectation on Learned Dynamics
Mechanical and Civil Engineering, California Institute of Technology（加州理工学院机械与土木工程系）；Aerospace Engineering and Engineering Mechanics, UT Austin（德克萨斯大学奥斯汀分校航空航天工程与工程力学系）；Computer Science, Cornell University（康奈尔大学计算机科学系）
Shared autonomy teleoperation can guarantee safety, but does so by reducing the human operator's control authority, which can lead to reduced levels of human-robot agreement and user satisfaction. This paper presents a novel haptic shared autonomy teleoperation paradigm that uses haptic feedback to inform the user about the inner state of a shared autonomy paradigm, while still guaranteeing safety. This differs from haptic shared control, which uses haptic feedback to inform the user's actions, but gives the human operator full control over the robot's actions. We conducted a user study in whi…
Policy Library CBF: Finite-Horizon Safety at Runtime via Parallel Rollouts
Department of Robotics（机器人学系）；Department of Aerospace Engineering（航空航天工程系）；University of Michigan（密歇根大学）；Toyota Motor North America, Research & Development（丰田美国北美洲研发部门）
Safety with Agency: Human-Centered Safety Filter with Application to AI-Assisted Motorsports
Department of Electrical and Computer Engineering, Princeton University（普林斯顿大学电气与计算机工程系）；Department of Mechanical and Aerospace Engineering, Princeton University（普林斯顿大学机械与航空航天工程系）；Toyota Research Institute（丰田研究院）
We present Learned Risk Metric Maps (LRMM) for real-time estimation of coherent risk metrics of high dimensional dynamical systems operating in unstructured, partially observed environments. LRMM models are simple to design and train -- requiring only procedural generation of obstacle sets, state and control sampling, and supervised training of a function approximator -- which makes them broadly applicable to arbitrary system dynamics and obstacle sets. In a parallel autonomy setting, we demonstrate the model's ability to rapidly infer collision probabilities of a fast-moving car-like robot dr…
An Open-source Hardware/Software Architecture and Supporting Simulation Environment to Perform Human FPV Flight Demonstrations for Unmanned Aerial Vehicle Autonomy
A Shared-Autonomy Construction Robotic System for Overhead Works
Intelligent Robotics Research Center, Korea Electronics Technology Institute (KETI)（韩国电子技术研究所智能机器人研究中心）；Dept. of Electrical and Computer Engineering, University of California, San Diego (UCSD)（加州大学圣地亚哥分校电气与计算机工程系）；ITONE Inc.（ITONE公司）
Breaking the Latency Barrier: Synergistic Perception and Control for High-Frequency 3D Ultrasound Servoing
Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China（香港中文大学电子工程系）；Department of Biomedical Engineering, Tsinghua University, Beijing, China（清华大学生物医学工程系）；Great Bay University, Dongguan, China（东莞大邦大学）
SharedAssembly: A Data Collection Approach via Shared Tele-Assembly
Technical University of Munich（慕尼黑工业大学）；Munich Institute of Robotics and Machine Intelligence（慕尼黑机器人与机器智能研究所）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
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）
The Robotarium, a remotely accessible swarm-robotics testbed, has provided free, open access to robotics and controls research for hundreds of users in thousands of experiments. This high level of usage requires autonomy in the system, which mainly corresponds to constraint satisfaction in the context of users' submissions. In other words, in case that the users' inputs to the robots may lead to collisions, these inputs must be altered to avoid these collisions automatically. However, these alterations must be minimal so as to preserve the users' objective in the experiment. Toward this end, t…
HI-GVF: Shared Control based on Human-Influenced Guiding Vector Fields for Human-multi-robot Cooperation
College of Intelligence Science and Technology, National University of Defense Technology（国防科技大学智能科学学院）；School of Robotics, Hunan University（湖南大学机器人学院）
Georgia Institute of Technology（佐治亚理工学院）；Optimus Ride
Applications that require multi-robot systems to operate independently for extended periods of time in unknown or unstructured environments face a broad set of challenges, such as hardware degradation, changing weather patterns, or unfamiliar terrain. To operate effectively under these changing conditions, algorithms developed for long-term autonomy applications require a stronger focus on robustness. Consequently, this work considers the ability to satisfy the operation-critical constraints of a disturbed system in a modular fashion, which means compatibility with different system objectives …
