Integrating DeepRL with Robust Low-Level Control in Robotic Manipulators for Non-Repetitive Reaching Tasks
This work addresses safety and interpretability issues in learning-based robotics for manipulation tasks, though it appears incremental as it combines existing methods like DRL and CSO.
The paper tackles the problem of ensuring stability and safety in robotic manipulators for non-repetitive reaching tasks by integrating a deep reinforcement learning-based trajectory planner with an auto-tuning low-level control strategy, achieving robust and uniformly exponential convergence in simulations despite uncertainties and disturbances.
In robotics, contemporary strategies are learning-based, characterized by a complex black-box nature and a lack of interpretability, which may pose challenges in ensuring stability and safety. To address these issues, we propose integrating a collision-free trajectory planner based on deep reinforcement learning (DRL) with a novel auto-tuning low-level control strategy, all while actively engaging in the learning phase through interactions with the environment. This approach circumvents the control performance and complexities associated with computations while addressing nonrepetitive reaching tasks in the presence of obstacles. First, a model-free DRL agent is employed to plan velocity-bounded motion for a manipulator with 'n' degrees of freedom (DoF), ensuring collision avoidance for the end-effector through joint-level reasoning. The generated reference motion is then input into a robust subsystem-based adaptive controller, which produces the necessary torques, while the cuckoo search optimization (CSO) algorithm enhances control gains to minimize the stabilization and tracking error in the steady state. This approach guarantees robustness and uniform exponential convergence in an unfamiliar environment, despite the presence of uncertainties and disturbances. Theoretical assertions are validated through the presentation of simulation outcomes.