Meta-Learning-Based Optimal Control for Soft Robotic Manipulators to Interact with Unknown Environments

Safe and efficient robot-environment interaction is a critical but challenging problem as robots are being increas- ingly employed to operate in unstructured and unpredictable environments. Soft robots are inherently compliant to safely interact with environments but their high nonlinearity exacer- bates control difficulties. Meta-learning provides a powerful tool for fast online model adaptation because it can learn an efficient model from data across different environments. Thus, this work applies the idea of meta-learning for the control of soft robotics. In particular, a target-oriented proactive search strategy is firstly performed to collect environment- specific data efficiently when a new interaction environment occurs. Then meta-learning exploits past experience to train a data-driven probabilistic model prior, and the model prior is online updated to be fast adapted to the new environment. Lastly, a model-based optimal control policy is utilized to drive the robot to desired performance. Our approach controls a soft robotic manipulator to achieve the desired position and contact force simultaneously when interacting with unknown changing environments. Overall, this work provides a viable control approach for soft robots to interact with unknown environments.