Sampling-Based Model Predictive Control for Dexterous Manipulation on a Biomimetic Tendon-Driven Hand

Abstract

Biomimetic and compliant robotic hands offer the potential for human-like dexterity, but controlling them is challenging due to high dimensionality, complex contact interactions, and uncertainties in state estimation. Sampling-based model predictive control (MPC), using a physics simulator as the dynamics model, is a promising approach for generating contact-rich behavior. However, sampling-based MPC has yet to be evaluated on physical (non-simulated) robotic hands, particularly on compliant hands with state uncertainties. We present the first successful demonstration of in-hand manipulation on a physical biomimetic tendon-driven robot hand using sampling-based MPC. While sampling-based MPC does not require lengthy training cycles like reinforcement learning approaches, it still necessitates adapting the task-specific objective function to ensure robust behavior execution on physical hardware. To adapt the objective function, we integrate a visual language model (VLM) with a real-time optimizer (MuJoCo MPC). We provide the VLM with a high-level human language description of the task and a video of the hand's current behavior. The VLM gradually adapts the objective function, allowing for efficient behavior generation, with each iteration taking less than two minutes. We show the feasibility of ball rolling, flipping, and catching using both simulated and physical robot hands. Our results demonstrate that sampling-based MPC is a promising approach for generating dexterous manipulation skills on biomimetic hands without extensive training cycles.