🤖 AI Summary
This work addresses the inherent conflict between high-precision trajectory tracking and compliant contact in dexterous hands under fixed-gain control. The authors propose a drive-agnostic impedance model predictive control (MPC) framework that algebraically reduces tendon-driven systems to a constant-coefficient double integrator via feedforward linearization. An encoder-only augmented Kalman disturbance estimator is embedded to eliminate steady-state errors. For the first time, a constant-\(A_d\) unbiased MPC architecture is applied to dexterous manipulation, achieving high-speed real-time optimization under hard constraints while ensuring stability and recursive feasibility with strong disturbance rejection. Experiments demonstrate that a hydraulic finger attains 0.5 mrad RMS tracking error and 0.1 mrad steady-state error, with only 6.6 mrad peak deflection under contact disturbances. On a 16-DOF LEAP hand, a 2.5 N grasping disturbance is rejected within 0.7 seconds, yielding performance improvements of tens to thousands of times over classical methods.
📝 Abstract
Dexterous hands must simultaneously track precise finger trajectories and maintain safe, compliant contact -- objectives in tension for any fixed-gain controller. We present an actuator-agnostic Impedance Model Predictive Control (Impedance MPC) framework for dexterous fingers, instantiating the constant-$A_d$ offset-free architecture established for physical human-robot interaction (pHRI); its stability, recursive-feasibility, and input-to-state-stability guarantees are inherited by preserving the architectural assumptions. An algebraic feedforward reduces the tendon transmission -- hydraulic, cable, pneumatic, twisted-string, or series-elastic -- to a constant-coefficient double integrator, so the QP cost inverse is precomputed offline and a 10-step receding-horizon quadratic program runs at 500\,Hz while enforcing hard constraints on contact force (ISO/TS 15066), actuation limits, and jerk. An encoder-only augmented-Kalman disturbance state drives steady-state error to zero under any constant contact load. On a hydraulically actuated finger -- the worked example platform, adding pressure and cavitation constraints -- the 500\,Hz Kalman MPC attains 0.5\,mrad RMS, 0.1\,mrad steady-state, and 6.6\,mrad peak deflection under 1.5\,Nm contact: 183$\times$, 1500$\times$, and 23$\times$ better than classical impedance. The realized first-move stiffness (18$\to$323\,Nm/rad with update rate) is independently verified. The architecture scales to a 16-DOF LEAP Hand MuJoCo simulation, recovering from 2.5\,N grasp-load disturbances within 0.7\,s.