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

• URL: http://arxiv.org/abs/2504.11493v1
• Date: Mon, 14 Apr 2025 21:14:51 GMT
• Title: Toward Aligning Human and Robot Actions via Multi-Modal Demonstration Learning
• Authors: Azizul Zahid, Jie Fan, Farong Wang, Ashton Dy, Sai Swaminathan, Fei Liu,
• Abstract summary: We propose a framework that explicitly models human demonstrations from RGB video with robot demonstrations in voxelized RGB-D space.<n>Our approach combines ResNet-based visual encoding for human intention modeling and a Perceiver Transformer for voxel-based robot action prediction.
• Score: 3.9738951919572827
• License: http://creativecommons.org/licenses/by/4.0/
• 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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