Bioinspired Simultaneous Learning and Motion-Force Hybrid Control for Robotic Manipulators Under Multiple Constraints

Inspired by the adaptive flexible motion coordination of biological systems, this study presents a bioinspired control strategy that enables robotic manipulators to achieve precise and compliant motion-force coordination for embodied intelligence and dexterous interaction in physically constrained environments. To this end, a learning-based motion-force hybrid control (LMFC) framework is proposed, which unifies learning and kinematic-level control to regulate both motion and interaction forces under incomplete or implicit kinematic information, thereby enhancing robustness and precision. The LMFC formulation recasts motion-force coordination as a time-varying quadratic programming (TVQP) problem, seamlessly incorporating multiple practical constraints-including joint limits, end-effector orientation maintenance, and obstacle avoidance-at the acceleration level, while determining control decisions at the velocity level. An RNN-based controller is further designed to integrate adaptive learning and control, enabling online estimation of uncertain kinematic parameters and mitigating joint drift. Simulation and experimental results demonstrate the effectiveness and practicality of the proposed framework, highlighting its potential for adaptive and compliant robotic control in constraint-rich environments.