Learning-Based Approach for a Soft Assistive Robotic Arm to Achieve Simultaneous Position and Force Control

Soft robotics have demonstrated great advantages in assisting elderly/disabled people during daily tasks, owing to their highly dexterous motions and safe human-robot interactions. However, simultaneously controlling the position and force of soft robots is still a challenging task due to soft actuators' nonlinearity, system uncertainty, and high-dimensional control space. Classical control methods are usually based on first-principle/analytical models that are difficult to derive for soft robots without making significant simplifications. To overcome such control challenges, the central concept of this work is to introduce a learning-based data-driven approach. The approach employs a probabilistic model to explicitly capture system nonlinearity and uncertainty. Besides, nonparametric local learning methods are investigated to deal with redundant high-dimensional control space. The approach is applied in a soft robotic arm interacting with a manikin to simulate the bathing task. Experimental results demonstrate that the soft robotic arm could be well controlled to track the desired position and force simultaneously (maximum error of position and force is 6 mm and 0.037 N). Meanwhile, our method outperforms another typical data-driven approach (maximum error of position and force is 10 mm and 0.058 N). The results indicate that our approach is helpful for soft robots because of the physical interactions needed in assistive tasks.