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arXiv · 2503.09018

Feasibility-aware Imitation Learning from Observations through a Hand-mounted Demonstration Interface

Kei Takahashi·Hikaru Sasaki·Takamitsu Matsubara
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Abstract

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.

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Kei Takahashi, Hikaru Sasaki, Takamitsu Matsubara. 2025-03-12. Feasibility-aware Imitation Learning from Observations through a Hand-mounted Demonstration Interface. https://arxiv.org/abs/2503.09018

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