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Rotational Impedance Formulation in a Unified Viewpoint of Lie Algebra

IEEE robotics AND automation LETTERS 2025年第4期10卷 3454-3461页

作者： Fu, Jian Shen, Siyuan Fu, Yuxiang Xiang, Kui Wuhan Univ Technol Sch Automat Wuhan 430070 Peoples R China Univ British Columbia Dept Elect & Comp Engn Vancouver BC V6T 2G3 Canada

Impedance control is essential in robotic manipulation tasks involving unpredictable interactions with the environment, as it ensures safety and stability during contact. While the concept of translational impedance is consistently understood, rotational impedance exhibits varied forms in scientific literature. This letter seeks to elucidate the fundamental nature of rotational impedance and presents a comprehensive, unified framework for formulating rotational impedance using Lie algebra and Noether's theorem. This approach facilitates the derivation of the various expressions of rotational impedance observed in the field. We utilized quaternions and rotation matrices to represent rotational motion within our proposed framework to ensure theoretical validity. This approach yielded a rotational impedance expression that aligns with existing literature. For empirical verification, we have conducted both simulated experiments using robots and actual trials on a UR5 robot. These assessments demonstrated the expected dynamic responses along all axes. These results showcase the efficacy and broad applicability of our approach.

关键词： Impedance Robots Manifolds Vectors Algebra Aerospace electronics Lie groups Quaternions Translation End effectors Compliance and impedance control force control formal methods in robotics and automation

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Maximum Allowable TCF Calibration Error for Robotic Pose Servoing

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IEEE robotics AND automation LETTERS 2025年第2期10卷 1744-1751页

作者： Hou, Jun Xing, Shiyu Ma, Yunkai Jing, Fengshui Tan, Min Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat Key Lab Cognit & Decis Intelligence Complex Syst Beijing 100190 Peoples R China

Robotic pose servoing aims to move the robot end-effector to the target pose. Closed-loop servo systems can tolerate a small TCF (tool control frame) calibration error and accurately reach the target pose through multiple pose measurements and pose adjustments. However, the maximum allowable TCF calibration error remains an open question. This paper demonstrates that the necessary condition for robotic pose servoing is a TCF calibration error angle of less than 60 degrees, with no limit on the translational component of the TCF calibration error. Next, an improved pose servoing method is proposed to address the conflict between the large TCF error and the limited robot workspace. This method introduces a scaling factor to limit the adjustment range within the robot workspace, ensuring greater robustness. Finally, robot-assisted cabin docking is selected as an experimental validation case. Simulation and physical experiments validate the maximum allowable TCF calibration error. Comparative experiments confirm the robustness of the improved pose servoing method, achieving cabin docking despite significant TCF calibration errors.

关键词： Calibration Robots Robot kinematics Stars Assembly Servomotors Robustness Measurement uncertainty Accuracy Visualization formal methods in robotics and automation calibration and identification assembly

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Online Resynthesis of High-Level Collaborative Tasks for Robots With Changing Capabilities

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IEEE robotics AND automation LETTERS 2025年第2期10卷 2032-2039页

作者： Fang, Amy Yin, Tenny Kress-Gazit, Hadas Cornell Univ Sibley Sch Mech & Aerosp Engn Ithaca NY 14850 USA

Given a collaborative high-level task and a team of heterogeneous robots with behaviors to satisfy it, this work focuses on the challenge of automatically adjusting the individual robot behaviors at runtime such that the task is still satisfied. We specifically address scenarios when robots encounter changes to their abilities-either failures or additional actions they can perform. We aim to minimize global teaming reassignments (and as a result, local resynthesis) when robots' capabilities change. The tasks are encoded in LTL psi, an extension of LTL introduced in our prior work. We increase the expressivity of LTL psi by including additional types of constraints on the overall teaming assignment that the user can specify, such as the minimum number of robots required for each assignment. We demonstrate the framework in a simulated warehouse scenario.

关键词： Robots Collaboration Semantics Logic Runtime Mobile robots Planning Manipulators Grippers Grammar formal methods in robotics and automation task planning multi-robot systems

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A Signal Temporal Logic approach for task-based coordination of multi-aerial systems: A wind turbine inspection case study

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robotics AND AUTONOMOUS SYSTEMS 2025年 186卷

作者： Silano, Giuseppe Caballero, Alvaro Liuzza, Davide Iannelli, Luigi Bogdan, Stjepan Saska, Martin Czech Tech Univ Fac Elect Engn Dept Cybernet Prague Czech Republic Ric Sistema Energet RSE SpA Dept Power Generat Technocal & Mat Milan Italy Univ Seville GRVC Robot Lab Seville Spain Univ Sannio Benevento Dept Engn Benevento Italy Italian Natl Agcy New Technol Energy & Sustainable Fus & Technol Nucl Safety & Secur Dept Frascati Italy Univ Zagreb Fac Elect Engn & Comp Zagreb Croatia

The paper addresses task assignment and trajectory generation for collaborative inspection missions using a fleet of multi-rotors, focusing on the wind turbine inspection scenario. The proposed solution enables safe and feasible trajectories while accommodating heterogeneous time-bound constraints and vehicle physical limits. An optimization problem is formulated to meet mission objectives and temporal requirements encoded as Signal Temporal Logic (STL) specifications. Additionally, an event-triggered replanner is introduced to address unforeseen events and compensate for lost time. Furthermore, a generalized robustness scoring method is employed to reflect user preferences and mitigate task conflicts. The effectiveness of the proposed approach is demonstrated through MATLAB and Gazebo simulations, as well as field multi-robot experiments in a mock-up scenario.

关键词： Aerial systems Applications formal methods in robotics and automation Task and motion planning Multi-robot systems

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Task-Driven Detection of Distribution Shifts With Statistical Guarantees for Robot Learning

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IEEE TRANSACTIONS ON robotics 2025年 41卷 926-945页

作者： Farid, Alec Veer, Sushant Pachisia, Divyanshu Majumdar, Anirudha Princeton Univ Princeton NJ 08540 USA Zoox Foster CA 94404 USA NVIDIA Autonomous Vehicle Res Grp Santa Clara CA 95051 USA Skydio San Mateo CA 94402 USA Princeton Univ Intelligent Robot Mot Lab Princeton NJ USA

Our goal is to perform out-of-distribution (OOD) detection, i.e., to detect when a robot is operating in environments drawn from a different distribution than the ones used to train the robot. We leverage probably approximately correct-Bayes theory to train a policy with a guaranteed bound on performance on the training distribution. Our idea for OOD detection relies on the following intuition: violation of the performance bound on test environments provides evidence that the robot is operating OOD. We formalize this via statistical techniques based on $p$-values and concentration inequalities. The approach provides guaranteed confidence bounds on OOD detection including bounds on both the false-positive and false-negative rates of the detector and is task-driven and only sensitive to changes that impact the robot's performance. We demonstrate our approach in simulation and hardware for a grasping task using objects with unfamiliar shapes or poses and a drone performing vision-based obstacle avoidance in environments with wind disturbances and varied obstacle densities. Our examples demonstrate that we can perform task-driven OOD detection within just a handful of trials.

关键词： Deep learning in robotics and automation failure detection and recovery formal methods in robotics and automation PAC-Bayes Deep learning in robotics and automation failure detection and recovery formal methods in robotics and automation PAC-Bayes

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Reinforcement learning with soft temporal logic constraints using limit-deterministic generalized Büchi automaton

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Journal of automation and Intelligence 2025年第1期4卷 39-51页

作者： Mingyu Cai Zhangli Zhou Lin Li Shaoping Xiao Zhen Kan University of California RiversideCA92521USA University of Science and Technology of China Hefei230026China The University of Iowa IA52246USA

This paper investigates control synthesis for motion planning under conditions of uncertainty,specifically in robot motion and environmental properties,which are modeled using a probabilistic labeled Markov decision process(PL-MDP).To address this,a model-free reinforcement learning(RL)approach is designed to produce a finite-memory control policy that meets complex tasks specified by linear temporal logic(LTL)*** the presence of uncertainties and potentially conflicting objectives,this study centers on addressing infeasible LTL specifications.A relaxed LTL constraint enables the agent to adapt its motion plan,allowing for partial satisfaction by accounting for necessary task ***,a new automaton structure is introduced to increase the density of accepting rewards,facilitating deterministic policy *** proposed RL framework is rigorously analyzed and prioritizes two key objectives:(1)satisfying the acceptance condition of the relaxed product MDP,and(2)minimizing long-term violation *** and experimental results are presented to demonstrate the framework’s effectiveness and robustness.

关键词： formal methods in robotics and automation Linear temporal logic Motion planning Optimal control

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Toward a Transform Method From Lighthill Fish Swimming Model to Biomimetic Robot Fish

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IEEE robotics AND automation LETTERS 2018年第3期3卷 2632-2639页

作者： Zhong, Yong Song, Jialei Yu, Haoyong Du, Ruxu Natl Univ Singapore Dept Biomed Engn Singapore 119077 Singapore Chinese Univ Hong Kong Dept Mech & Automat Engn Hong Kong Hong Kong Peoples R China

How to build a biomimetic robot fish has always been a divergent question. Most of the existing robot fishes suffer structural complexity and propulsive inefficiency. In this paper, we proposed a method that transfers the natural actuation properties of fish to the biomimetic robot counterpart. The natural properties were analyzed based on the Lighthill fish swimming model and the actuation function of fish muscles. The analyses shown that the compliant part is feasible in fish propulsion, which means nonactuated/compliant part is also feasible to achieve an effective propulsion on robot fish, and an example of this type of robot fish and its general kinematic model are presented. This transform method not only can demonstrate the actuation features of fish, but also can reduce the motors in building robot fish. The method has also been extended to apply on different swimming modes, offering a general guidance to build efficient biomimetic robot fishes with fewer motors.

关键词： Biologically inspired robots formal methods in robotics and automation kinematics

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Fast Decomposition of Temporal Logic Specifications for Heterogeneous Teams

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IEEE robotics AND automation LETTERS 2022年第2期7卷 2297-2304页

作者： Leahy, Kevin Jones, Austin Vasile, Cristian-Ioan MIT Lincoln Lab Lexington MA 02421 USA Lehigh Univ Dept Mech Engn & Mech Bethlehem PA 18015 USA

We focus on decomposing large multi-agent path planning problems with global temporal logic goals (common to all agents) into smaller sub-problems that can be solved and executed independently. Crucially, the sub-problems' solutions must jointly satisfy the common global mission specification. The agents' missions are given as Capability Temporal Logic (CaTL) formulas, a fragment of Signal Temporal Logic (STL) that can express properties over tasks involving multiple agent capabilities (i.e., different combinations of sensors, effectors, and dynamics) under strict timing constraints. We jointly decompose both the temporal logic specification and the team of agents, using a satisfiability modulo theories (SMT) approach and heuristics for handling temporal operators. The output of the SMT is then distributed to subteams and leads to a significant speed up in planning time compared to planning for the entire team and specification. We include computational results to evaluate the efficiency of our solution, as well as the trade-offs introduced by the conservative nature of the SMT encoding and heuristics.

关键词： formal methods in robotics and automation multi-robot systems

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Runtime Monitoring of Time Window Temporal Logic

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IEEE robotics AND automation LETTERS 2022年第3期7卷 5888-5895页

作者： Bonnah, Ernest Hoque, Khaza Anuarul Univ Missouri Dept Elect & Comp Engn Columbia MO 65211 USA

Temporal logic is becoming increasingly popular for its application in the analysis and control of dynamic systems. Time window temporal logic (TWTL) is a rich expressive language for specifying time-bounded serial tasks in a compact manner that is common in many control applications, such as robotics. Typically, TWTL specifications are verified using an automata-based model checking algorithm. However, verification of important properties of a given system using model checking in design time does not guarantee that the system will behave as expected during the runtime operation which falls under the scope of runtime verification. In this letter, we present a rewriting-based algorithm for runtime monitoring of safety requirements expressed in TWTL. The feasibility and efficiency of our proposed approach is demonstrated using two case studies related to unmanned aerial vehicle (UAV) surveillance and industrial robotics for manufacturing.

关键词： formal methods in robotics and automation task and motion planning time window temporal logic runtime verification

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Point-Based Policy Synthesis for POMDPs With Boolean and Quantitative Objectives

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IEEE robotics AND automation LETTERS 2019年第2期4卷 1860-1867页

作者： Wang, Yue Chaudhuri, Swarat Kavraki, Lydia E. Rice Univ Dept Comp Sci Houston TX 77005 USA

Effectively planning robust executions under uncertainty is critical for building autonomous robots. Partially observable Markov decision processes (POMDPs) provide a standard framework for modeling many robot applications under uncertainty. We study POMDPs with two kinds of objectives: (1) Boolean objectives for a correctness guarantee of accomplishing tasks and (2) quantitative objectives for optimal behaviors. For robotic domains that require both correctness and optimality, POMDPs with Boolean and quantitative objectives are natural formulations. We present a practical policy synthesis approach for POMDPs with Boolean and quantitative objectives by combining policy iteration and policy synthesis for POMDPs with only Boolean objectives. To improve efficiency, our approach produces approximate policies by performing the point-based backup on a small set of representative beliefs. Despite being approximate, our approach maintains validity (satisfying Boolean objectives) and guarantees improved policies at each iteration before termination. Moreover, the error due to approximation is bounded. We evaluate our approach in several robotic domains. The results show that our approach produces good approximate policies that guarantee task completion.

关键词： formal methods in robotics and automation Task Planning Motion and Path Planning

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