aiqbench analysis
This research introduces a primitive-informed sampling-based Model Predictive Control (MPC) framework specifically designed to solve the high-dimensional control challenges of multi-fingered robotic hands. By biasing the search space with low-dimensional manipulation primitives and optimizing joint residuals, the method achieves reliable dexterous tasks like in-hand rotation without relying on complex gradient calculations through contact dynamics. The approach demonstrates robustness across varying object sizes and model mismatches, validating its utility for advancing autonomous robotic manipulation systems.
Primitive-Informed Sampling-Based MPC for Multi-Fingered Dexterous Manipulation
arXiv:2609.14868v1 Announce Type: new 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.
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.
Robotics (cs.RO)
arXiv:2609.14868 [cs.RO]
(or arXiv:2609.14868v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.14868 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 270 words
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