Manipulation via Force Distribution at Contact

2026-02-03

AI Summary

Existing point contact models struggle to accurately capture the frictional and torque dynamics inherent in rich contact interactions, thereby limiting the realization of human-like dexterous manipulation. This work proposes a Force-Distributed Line Contact (FDLC) model that, for the first time, incorporates non-uniform force distributions along line contacts into trajectory optimization. A bilevel optimization framework is developed: the lower level optimizes the contact force distribution, while the upper level employs iterative Linear Quadratic Regulator (iLQR) for trajectory optimization. By transcending the representational limitations of point contact models in complex contact scenarios, the proposed approach generates highly efficient and robust trajectories—demonstrated in a box-rotation task—achieving lower control effort and reduced robot motion compared to conventional methods.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Efficient and robust trajectories play a crucial role in contact-rich manipulation, which demands accurate mod- eling of object-robot interactions. Many existing approaches rely on point contact models due to their computational effi- ciency. Simple contact models are computationally efficient but inherently limited for achieving human-like, contact-rich ma- nipulation, as they fail to capture key frictional dynamics and torque generation observed in human manipulation. This study introduces a Force-Distributed Line Contact (FDLC) model in contact-rich manipulation and compares it against conventional point contact models. A bi-level optimization framework is constructed, in which the lower-level solves an optimization problem for contact force computation, and the upper-level optimization applies iLQR for trajectory optimization. Through this framework, the limitations of point contact are demon- strated, and the benefits of the FDLC in generating efficient and robust trajectories are established. The effectiveness of the proposed approach is validated by a box rotating task, demonstrating that FDLC enables trajectories generated via non-uniform force distributions along the contact line, while requiring lower control effort and less motion of the robot.

Problem

• contact-rich manipulation
• point contact model
• force distribution
• frictional dynamics
• torque generation

Innovation

• Force-Distributed Line Contact
• contact-rich manipulation
• bi-level optimization
• trajectory optimization
• frictional dynamics