Primitive-Informed Sampling-Based MPC for Multi-Fingered Dexterous Manipulation

2026-09-13

AI Summary

本文提出了一种基于原语信息的采样模型预测控制框架，用于解决多指灵巧操作问题，通过使用低维操作原语和关节级残差优化提高采样效率。

Abstract

We present a primitive-informed sampling-based model predictive control (MPC) framework for multi-fingered dexterous manipulation. Sampling-based MPC avoids the need for gradients through complex contact dynamics, but direct exploration of the high-dimensional joint space is inefficient and makes performance strongly dependent on the sampling distribution. Our framework biases sampling using low-dimensional manipulation primitives that encode coordinated finger motions, while simultaneously optimizing joint-level residuals to adapt these motions to the current hand-object configuration. Task-related rollout constraints reject infeasible trajectories during forward simulation, improving the effective use of the sampling budget. We evaluate the approach on a physical Allegro hand using a synchronized MuJoCo digital twin. Ablations show that both the primitive and residual are necessary for reliable continuous in-hand rotation, that increasing the sampling budget alone does not recover this coordination, and that rollout constraints substantially improve success rate. A primitive extracted from a single object remains effective across object sizes and under model mismatch, and the framework further supports grasping, object reorientation, and coordinated arm-hand reach-grasp-transport, using primitives extracted from both a simulation-trained policy and human hand-motion data.

Problem

• sampling-based MPC
• dexterous manipulation
• high-dimensional joint space
• manipulation primitives
• rollout constraints

Innovation

• primitive-informed sampling
• model predictive control (MPC)
• dexterous manipulation
• rollout constraints
• manipulation primitives