Adaptive Neural Control for Efficient Rhythmic Movement Generation and Online Frequency Adaptation of a Compliant Robot Arm

In this paper, we propose an adaptive and simple neural control approach for a robot arm with soft/compliant materials, called GummiArm. The control approach is based on a minimal two-neuron oscillator network (acting as a central pattern generator) and an error-based dual integral learning (DIL) method for efficient rhythmic movement generation and frequency adaptation, respectively. By using this approach, we can precisely generate rhythmic motion for GummiArm and allow it to quickly adapt its motion to handle physical and environmental changes as well as interacting with a human safely. Experimental results for GummiArm in different scenarios (e.g., dealing with different joint stiffnesses, working against elastic loads, and interacting with a human) are provided to illustrate the effectiveness of the proposed adaptive neural control approach.