Online learning for behavior switching in a soft robotic arm
Tao Li, Kohei Nakajima, Rolf Pfeifer · 2013
Soft robots possess several potential advantages over traditional articulated ones and have attracted significant interest in recent years. However, to control this new type of robots using conventional model-based robotic control approaches is generally ineffective. In this paper, we investigate the challenge to embed and switch among multiple behaviors for an octopus-inspired soft robotic arm. An online learning method for reservoir computing is exploited for this task. This online learning method does not require a separate teaching data collection phase; thus, it has the potential to achieve autonomy in soft robots. Our result shows the feasibility of this approach.