Harmonic potentials provide globally convergent potential fields that are provably free of local minima. Due to its analytical format, it is particularly suitable for generating safe and reliable robot navigation policies. However, for complex environments that consist of a large number of overlapping non-sphere obstacles, the computation of associated transformation functions can be tedious. This becomes more apparent when: (i) the workspace is initially unknown and the underlying potential fields are updated constantly as the robot explores it; (ii) the high-level mission consists of sequential navigation tasks among numerous regions, requiring the robot to switch between different potentials. Thus, this work proposes an efficient and automated scheme to construct harmonic potentials incrementally online as guided by the task automaton. A novel two-layer harmonic tree (HT) structure is introduced that facilitates the hybrid combination of oriented search algorithms for task planning and harmonic-based navigation controllers for non-holonomic robots. Both layers are adapted efficiently and jointly during online execution to reflect the actual feasibility and cost of navigation within the updated workspace. Global safety and convergence are ensured both for the high-level task plan and the low-level robot trajectory. Known issues such as oscillation or long-detours for purely potential-based methods and sharp-turns or high computation complexity for purely search-based methods are prevented. Extensive numerical simulation and hardware experiments are conducted against several strong baselines.
FSUNav: A Cerebrum-Cerebellum Architecture for Fast, Safe, and Universal Zero-Shot Goal-Oriented Navigation
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（东方理工学院信息科学与技术学院）
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）
One of the standing challenges in multi-robot systems is the ability to reliably coordinate motions of multiple robots in environments where the robots are subject to disturbances. We consider disturbances that force the robot to temporarily stop and delay its advancement along its planned trajectory which can be used to model, e.g., passing-by humans for whom the robots have to yield. Although reactive collision-avoidance methods are often used in this context, they may lead to deadlocks between robots. We design a multi-robot control strategy for executing coordinated trajectories computed b…
Technical University of Munich（慕尼黑工业大学）；University of South Florida（南佛罗里达大学）
Following work on joint object-action representations, the functional object-oriented network (FOON) was introduced as a knowledge graph representation for robots. Taking the form of a bipartite graph, a FOON contains symbolic or high-level information that would be pertinent to a robot's understanding of its environment and tasks in a way that mirrors human understanding of actions. In this work, we outline a road-map for future development of FOON and its application in robotic systems for task planning as well as knowledge acquisition from demonstration. We propose preliminary ideas to show…
Safe Execution of Learned Orientation Skills with Conic Control Barrier Functions
Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich（慕尼黑机器人与机器智能研究所（慕尼黑工业大学））；Department of Industrial Engineering (DII), University of Trento（特伦托大学工业工程系）；Electronic and Informatics Department, Faculty of Engineering, Mondragon Unibertsitatea（蒙德拉贡大学工程学院电子与信息系）
Safe, Fluent and Acceptable Motion Generation and Execution for Human--Robot Interaction in Manufacturing Environments
GIPSA Lab（GIPSA实验室）；Grenoble Institute of Technology（格勒诺布尔理工学院）；Inria（法国国家信息与自动化研究所）；LIP/PC2S（LIP/PC2S实验室）；Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；Univ. Savoie Mont Blanc（萨瓦大学）
Brain-SAD: A Brain-Inspired Safe Autonomous Driving Control Framework with Dynamic Fear-Oriented Constraint on Dual-Policy
Nanjing University of Information Science & Technology（南京信息工程大学）；University of Electronic Science and Technology of China（电子科技大学）；Jiangsu Ocean University（江苏海洋大学）
