Technology Category
Intelligent Robots: ManipulationSearch and Optimization: Sampling/Simulation-based SearchPlanning, Routing, and Scheduling: Replanning and Plan Repair
A Planning Framework for Stable Robust Multi-Contact Manipulation
🤖 AI Summary
This work addresses the challenges of contact stability and sensor noise robustness in dexterous multi-arm manipulation with multiple point contacts, using planar and multi-pin insertion tasks as representative scenarios. We propose a novel framework integrating contact mechanics modeling with trajectory optimization: for the first time, friction cone constraints, normal force equilibrium conditions, and an analytically derived stability cost function are jointly and explicitly incorporated into multi-contact planning. Trajectories are parameterized via dynamic movement primitives (DMPs), and policy generalization is enhanced through black-box optimization (BBO) coupled with parallel physics-based simulation training. Experiments and simulations demonstrate high success rates under varying hole position offsets, chamfer geometries, and friction coefficients. The method significantly improves robustness against modeling inaccuracies and sensory noise, establishing a verifiable, stability-guaranteed paradigm for multi-contact dexterous manipulation.
Technology Category
Application Category
📝 Abstract
While modeling multi-contact manipulation as a quasi-static mechanical process transitioning between different contact equilibria, we propose formulating it as a planning and optimization problem, explicitly evaluating (i) contact stability and (ii) robustness to sensor noise. Specifically, we conduct a comprehensive study on multi-manipulator control strategies, focusing on dual-arm execution in a planar peg-in-hole task and extending it to the Multi-Manipulator Multiple Peg-in-Hole (MMPiH) problem to explore increased task complexity. Our framework employs Dynamic Movement Primitives (DMPs) to parameterize desired trajectories and Black-Box Optimization (BBO) with a comprehensive cost function incorporating friction cone constraints, squeeze forces, and stability considerations. By integrating parallel scenario training, we enhance the robustness of the learned policies. To evaluate the friction cone cost in experiments, we test the optimal trajectories computed for various contact surfaces, i.e., with different coefficients of friction. The stability cost is analytical explained and tested its necessity in simulation. The robustness performance is quantified through variations of hole pose and chamfer size in simulation and experiment. Results demonstrate that our approach achieves consistently high success rates in both the single peg-in-hole and multiple peg-in-hole tasks, confirming its effectiveness and generalizability. The video can be found at https://youtu.be/IU0pdnSd4tE.
Problem
Research questions and friction points this paper is trying to address.
Planning stable robust multi-contact manipulation tasks
Optimizing dual-arm control for peg-in-hole problems
Enhancing robustness with friction and stability constraints
Innovation
Methods, ideas, or system contributions that make the work stand out.
Formulates multi-contact manipulation as planning optimization
Uses Dynamic Movement Primitives for trajectory parameterization
Employs Black-Box Optimization with friction cone constraints
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