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

• URL: http://arxiv.org/abs/2503.09018v1
• Date: Wed, 12 Mar 2025 03:14:04 GMT
• Title: Feasibility-aware Imitation Learning from Observations through a Hand-mounted Demonstration Interface
• Authors: Kei Takahashi, Hikaru Sasaki, Takamitsu Matsubara,
• Abstract summary: We propose feasibility-aware behavior cloning from observation (FABCO)<n>In the FABCO framework, the feasibility of each demonstration is assessed using the robot's pre-trained forward and inverse dynamics models.<n>We experimentally validated FABCO's effectiveness by applying it to a pipette insertion task involving a pipette and a vial.
• Score: 10.808201018448274
• License: http://creativecommons.org/licenses/by/4.0/
• 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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