While robots execute many tasks where physical interaction with the environment is required, it is still challenging to control a robot that deliberately makes contact at a non-zero velocity, especially with multiple contact points that are impacted simultaneously.
When there is a mismatch between planned and actual impact time, the robot typically does not respond as desired.
In this paper, we demonstrate that an Impact-Aware Learning from Demonstration (IA-LfD) framework, that is based on Reference Spreading, can be developed and validated by physical experiments on real robots.
The proposed IA-LfD framework is based on the following key aspects:
(a) generating suitable ante-impact and post-impact tracking references from demonstrations;
(b) development and validation of an impact detection mechanism to identify the contact transition, typically consisting of multiple impacts.
The validation of the approach shows in particular the advantage of using an intermediate phase controller in reducing peak contact forces and oscillations during the dynamic contact transition, when compared to baseline approaches not using this controller.
In addition, the validation highlights the role played by active/physical contact damping during the contact transition to improve execution performance.