Deformable linear objects (DLOs), such as rods, cables, and ropes, play important roles in daily life. However, manipulation of DLOs is challenging as large geometrically nonlinear deformations may occur during the manipulation process. This problem is made even more difficult as the different deformation modes (e.g., stretching, bending, and twisting) may result in elastic instabilities during manipulation. In this paper, we formulate a physics-guided data-driven method to solve a challenging manipulation task -- accurately deploying a DLO (an elastic rod) onto a rigid substrate along various prescribed patterns. Our framework combines machine learning, scaling analysis, and physical simulations to develop a physics-based neural controller for deployment. We explore the complex interplay between the gravitational and elastic energies of the manipulated DLO and obtain a control method for DLO deployment that is robust against friction and material properties. Out of the numerous geometrical and material properties of the rod and substrate, we show that only three non-dimensional parameters are needed to describe the deployment process with physical analysis. Therefore, the essence of the controlling law for the manipulation task can be constructed with a low-dimensional model, drastically increasing the computation speed. The effectiveness of our optimal control scheme is shown through a comprehensive robotic case study comparing against a heuristic control method for deploying rods for a wide variety of patterns. In addition to this, we also showcase the practicality of our control scheme by having a robot accomplish challenging high-level tasks such as mimicking human handwriting, cable placement, and tying knots.
Self-supervised Physics-Informed Manipulation of Deformable Linear Objects with Non-negligible Dynamics
Human-Robot Interfaces and Interaction Lab, Istituto Italiano di Tecnologia, Genova, Italy（人类-机器人交互实验室，意大利技术研究院，热那亚，意大利）；Ph.D. program of national interest in Robotics and Intelligent Machines (DRIM) and Universita di Genova, Genoa, Italy（机器人与智能机械国家级博士项目（DRIM）和热那亚大学，热那亚，意大利）
Challenges and Outlook in Robotic Manipulation of Deformable Objects
Cognitive Robotics, TU Delft（代尔夫特理工大学认知机器人）；Honda Research Institute, Europe（本田研究院欧洲）；LIRMM - Université de Montpellier CNRS（蒙彼利埃大学 CNRS LIRMM）；COSMER laboratory EA 7398, Universié de Toulon（土伦大学 COSMER 实验室 EA 7398）；The Hong Kong Polytechnic University（香港理工大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of Michigan（密歇根大学）；Università degli Studi di Napoli Federico II（那不勒斯费德里科二世大学）；Osaka University（大阪大学）；National Institute of AIST（产业技术综合研究所）；Tsinghua University（清华大学）；The University of Hong Kong（香港大学）；Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）
Reinforcement Learning Compensated Model Predictive Control for Off-road Driving on Unknown Deformable Terrain
Department of Automotive Engineering, Clemson University（汽车工程系，克莱姆森大学）；Ground Vehicle Systems Center, U.S. Army Combat Capabilities Development Command（地面车辆系统中心，美国陆军作战能力发展指挥部）
Enhanced Robotic Navigation in Deformable Environments using Learning from Demonstration and Dynamic Modulation
Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich (TUM), Germany（慕尼黑机器人与机器智能研究所（MIRMI），慕尼黑技术大学（TUM））；Mohamed Bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE（穆罕默德·本·扎耶德人工智能大学）
Shape-Space Deformer: Unified Visuo-Tactile Representations for Robotic Manipulation of Deformable Objects
CSIRO Robotics, Data61, CSIRO（CSIRO机器人部、Data61、CSIRO）；School of Electrical Engineering and Computer Science (EECS), The University of Queensland（电气工程与计算机科学学院（EECS）、昆士兰大学）
DiagGen: Agentic Generation of Deformable Assets with Sim-based Diagnostics for Robotic Simulation
University of British Columbia（不列颠哥伦比亚大学）；National University of Singapore（新加坡国立大学）；University of Cambridge（剑桥大学）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）
Context-Aware Force Estimation for Deformable Tool Manipulation in Robotic Environmental Swabbing via Few-Shot Continual Adaptation
Department of Biological and Agricultural Engineering, University of Arkansas（阿肯色大学，生物与农业工程系）；Department of Food Science, University of Arkansas（阿肯色大学，食品科学系）
Actor-Critic Cooperative Compensation to Model Predictive Control for Off-Road Autonomous Vehicles Under Unknown Dynamics
Department of Automotive Engineering, Clemson University（汽车工程系，克莱姆森大学）；Ground Vehicle Systems Center, U.S. Army Combat Capabilities Development Command（地面车辆系统中心，美国陆军作战能力发展指挥部）
Coordinated Manipulation of Hybrid Deformable-Rigid Objects in Constrained Environments
Department of Mechanical and Nuclear Engineering, Khalifa University（卡利法大学机械与核工程系）；Khalifa University Center for Autonomous Robotic Systems (KUCARS)（卡利法大学自主机器人系统中心）；Department of Computer Science, Khalifa University（卡利法大学计算机科学系）
SoftVTBench: A Safety-Aware Visuo-Tactile Benchmark for Physically Constrained Robotic Manipulation of Deformable Objects (Early Version)
Tuojing Intelligence（拓景智能）；Tsinghua University（清华大学）；King’s College London（伦敦国王学院）；Southeast University（东南大学）；Stevens Institute of Technology（史蒂文斯理工学院）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；University of Manchester（曼彻斯特大学）；Simple AI（简单人工智能公司）；Imperial College London（伦敦帝国学院）；Carnegie Mellon University（卡内基梅隆大学）；Zhejiang University（浙江大学）；Beihang University（北京航空航天大学）；The University of Hong Kong（香港大学）
