Augmentation for Learning From Demonstration with Environmental Constraints
This addresses the challenge of robust and generalizable Learning from Demonstration for complex robotic manipulation, though it appears incremental in its approach.
The paper tackles the problem of generalizing a policy from a single human demonstration for contact-rich manipulation tasks with articulated mechanisms, achieving reliable task completion in changing environments through autonomous augmentation.
We introduce a Learning from Demonstration (LfD) approach for contact-rich manipulation tasks with articulated mechanisms. The extracted policy from a single human demonstration generalizes to different mechanisms of the same type and is robust against environmental variations. The key to achieving such generalization and robustness from a single human demonstration is to autonomously augment the initial demonstration to gather additional information through purposefully interacting with the environment. Our real-world experiments on complex mechanisms with multi-DOF demonstrate that our approach can reliably accomplish the task in a changing environment. Videos are available at the: https://sites.google.com/view/rbosalfdec/home