Feasibility-aware Imitation Learning from Observations through a Hand-mounted Demonstration Interface
This work addresses a specific challenge in robot imitation learning by enhancing demonstration quality and policy learning, but it is incremental as it builds on existing behavior cloning methods with a focus on feasibility feedback.
The paper tackles the problem of robots learning from human demonstrations that may be infeasible due to differences in movement characteristics, proposing a feasibility-aware imitation learning framework that uses robot dynamics models to assess and provide feedback on demonstration feasibility, resulting in improved data efficiency and policy robustness as validated in a pipette insertion task with four participants.
Imitation learning through a demonstration interface is expected to learn policies for robot automation from intuitive human demonstrations. However, due to the differences in human and robot movement characteristics, a human expert might unintentionally demonstrate an action that the robot cannot execute. We propose feasibility-aware behavior cloning from observation (FABCO). In the FABCO framework, the feasibility of each demonstration is assessed using the robot's pre-trained forward and inverse dynamics models. This feasibility information is provided as visual feedback to the demonstrators, encouraging them to refine their demonstrations. During policy learning, estimated feasibility serves as a weight for the demonstration data, improving both the data efficiency and the robustness of the learned policy. We experimentally validated FABCO's effectiveness by applying it to a pipette insertion task involving a pipette and a vial. Four participants assessed the impact of the feasibility feedback and the weighted policy learning in FABCO. Additionally, we used the NASA Task Load Index (NASA-TLX) to evaluate the workload induced by demonstrations with visual feedback.