Learning robot anomaly recovery skills from multiple time-driven demonstrations - Shuai Li (Neurocomputing, 2021)
Robots are prone to making anomalies when performing manipulation tasks in unstructured environments, it is often desirable to rapidly adapt the robotic behavior to avoid environmental changes by learning from experts’ demonstrations. We propose a framework for learning robot anomaly recovery skills from time-driven demonstrations based on a Gaussian process regression with prior mean derived by Gaussian mixture regression, named as mean-prior GPR (MP-GPR), which allows an end-user to adjust the anomalous trajectory intuitively by simultaneously considering the variability of the demonstrations and the adaptation of recovery skills. Evaluations are divided into two phases, a benchmarking dataset with robot reaching, pushing, writing, and pressing tasks are first used to verify the path accuracy and variability, and then a real-time robot bin-picking task for evaluating the adaptation of the framework. Our method has a fair comparison with probabilistic-based methods in the field of robot learning from demonstrations, including Gaussian mixture regression, probabilistic movement primitives, and kernelized movement primitives. The results indicate that our proposed method can efficiently encode the variability from multiple demonstrations and rapidly anomaly recovery skills learning by modulating a learned trajectory to safe via-points.