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

• URL: http://arxiv.org/abs/2609.14868v1
• Date: Mon, 14 Sep 2026 00:36:17 GMT
• Title: Primitive-Informed Sampling-Based MPC for Multi-Fingered Dexterous Manipulation
• Abstract summary: We present a primitive-informed sampling-based model predictive control (MPC) framework for dexterous manipulation.<n>Our framework biases sampling using low-dimensional manipulation primitives that encode coordinated finger motions.<n>We evaluate the approach on a physical Allegro hand using a synchronized MuJoCo digital twin.
• Score: 3.693403461852542
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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