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arXiv · 2504.11493

Toward Aligning Human and Robot Actions via Multi-Modal Demonstration Learning

Azizul Zahid·Jie Fan·Farong Wang·Ashton Dy·Sai Swaminathan·Fei Liu
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Abstract

Understanding action correspondence between humans and robots is essential for evaluating alignment in decision-making, particularly in human-robot collaboration and imitation learning within unstructured environments. We propose a multimodal demonstration learning framework that explicitly models human demonstrations from RGB video with robot demonstrations in voxelized RGB-D space. Focusing on the "pick and place" task from the RH20T dataset, we utilize data from 5 users across 10 diverse scenes. Our approach combines ResNet-based visual encoding for human intention modeling and a Perceiver Transformer for voxel-based robot action prediction. After 2000 training epochs, the human model reaches 71.67% accuracy, and the robot model achieves 71.8% accuracy, demonstrating the framework's potential for aligning complex, multimodal human and robot behaviors in manipulation tasks.

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Azizul Zahid, Jie Fan, Farong Wang, Ashton Dy, Sai Swaminathan, Fei Liu. 2025-04-14. Toward Aligning Human and Robot Actions via Multi-Modal Demonstration Learning. https://arxiv.org/abs/2504.11493

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