Abstract-Bionic soft robots offer exciting perspectives for more flexible and safe physical interaction with the world and humans. Unfortunately, their hardware design often prevents analytical modeling, which in turn is a prerequisite to apply classical automatic control approaches. On the other hand, also modeling by means of learning is hardly feasible due to many degrees of freedom, high-dimensional state spaces and the softness properties like e.g. mechanical elasticity, which cause limited repeatability and complex dynamics. Nevertheless, the realization of basic control modes is important to leverage the potential of soft robots for applications. We therefore propose a hybrid approach combining classical and learning elements for the realization of an interactive control mode for an elastic bionic robot. It superimposes a low-gain feedback control with a feed-forward control based on a learned simplified model of the inverse dynamics which considers only equilibria of the robot's dynamics. We demonstrate on the Bionic Handling Assistant how a respective inverse equilibrium model can be learned and effectively exploited for quick and agile control. In a second step, the control scheme is extended to an active compliant control mode. It implements a kind of gravitation compensation to allow for kinesthetic teaching of the robot based on the implicit knowledge of gravitational and mechanical forces that are encoded in the learned equilibrium model. We finally discuss that this control scheme may be implemented also on other soft robots to provide the avenue towards their applications in general manipulation tasks.
For the realization of an interactive control without the knowledge of a dynamic model, a hybrid approach was introduced in REF that combines classical control modes, like PID control, and learning elements.
An active compliant control mode for interaction with a pneumatic soft robot
2014 IEEE/RSJ International Conference on Intelligent Robots and Systems
2014 IEEE/RSJ International Conference on Intelligent Robots and Systems
Computer Science