Mitigating the Human-Robot Domain Discrepancy in Visual Pre-training for Robotic Manipulation

• URL: http://arxiv.org/abs/2406.14235v3
• Date: Sun, 06 Apr 2025 11:46:32 GMT
• Title: Mitigating the Human-Robot Domain Discrepancy in Visual Pre-training for Robotic Manipulation
• Authors: Jiaming Zhou, Teli Ma, Kun-Yu Lin, Zifan Wang, Ronghe Qiu, Junwei Liang,
• Abstract summary: We propose a novel adaptation paradigm that leverages readily available paired human-robot video data to bridge the domain gap.<n>Our method employs a human-robot contrastive alignment loss to align the semantics of human and robot videos, adapting pre-trained models to the robot domain in a parameter-efficient manner.
• Score: 16.809190349155525
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning generalizable visual representations across different embodied environments is essential for effective robotic manipulation in real-world scenarios. However, the limited scale and diversity of robot demonstration data pose a significant challenge. Recent research has explored leveraging large-scale human activity data for pre-training, but the substantial morphological differences between humans and robots introduce a significant human-robot domain discrepancy, hindering the generalization of these models to downstream manipulation tasks. To overcome this, we propose a novel adaptation paradigm that leverages readily available paired human-robot video data to bridge the domain gap. Our method employs a human-robot contrastive alignment loss to align the semantics of human and robot videos, adapting pre-trained models to the robot domain in a parameter-efficient manner. Experiments on 20 simulated tasks across two different benchmarks and five real-world tasks demonstrate significant improvements. These results span both single-task and language-conditioned multi-task settings, evaluated using two different pre-trained models. Compared to existing pre-trained models, our adaptation method improves the average success rate by over 7% across multiple tasks on both simulated benchmarks and real-world evaluations.

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