When Pre-trained Visual Representations Fall Short: Limitations in Visuo-Motor Robot Learning

• URL: http://arxiv.org/abs/2502.03270v1
• Date: Wed, 05 Feb 2025 15:25:46 GMT
• Title: When Pre-trained Visual Representations Fall Short: Limitations in Visuo-Motor Robot Learning
• Authors: Nikolaos Tsagkas, Andreas Sochopoulos, Duolikun Danier, Chris Xiaoxuan Lu, Oisin Mac Aodha,
• Abstract summary: integration of pre-trained visual representations into visuo-motor robot learning has emerged as a promising alternative to training visual encoders from scratch.<n>PVRs face critical challenges in the context of policy learning, including temporal entanglement and an inability to generalise even in the presence of minor scene perturbations.<n>This work identifies these shortcomings and proposes solutions to address them. First, we augment PVR features with temporal perception and a sense of task completion, effectively disangling them in time.<n>Second, we introduce a module that learns to selectively attend to task-relevant local features, enhancing robustness when evaluated on out-of-distribution
• Score: 19.394253159981577
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The integration of pre-trained visual representations (PVRs) into visuo-motor robot learning has emerged as a promising alternative to training visual encoders from scratch. However, PVRs face critical challenges in the context of policy learning, including temporal entanglement and an inability to generalise even in the presence of minor scene perturbations. These limitations hinder performance in tasks requiring temporal awareness and robustness to scene changes. This work identifies these shortcomings and proposes solutions to address them. First, we augment PVR features with temporal perception and a sense of task completion, effectively disentangling them in time. Second, we introduce a module that learns to selectively attend to task-relevant local features, enhancing robustness when evaluated on out-of-distribution scenes. Our experiments demonstrate significant performance improvements, particularly in PVRs trained with masking objectives, and validate the effectiveness of our enhancements in addressing PVR-specific limitations.

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