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What Matters, When? Diagnosing and Improving Conditional Visual Grounding in Visuomotor Imitation Policies

arXiv cs.AI / cs.LG / cs.CLby Vivek Chavan ()1 min read1 · 1 read
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Researchers diagnose conditional visual grounding failures in visuomotor imitation policies and show targeted interventions substantially improve distractor robustness.

The paper studies why ACT-based visuomotor imitation policies fail when visually similar distractor objects or receptacles are introduced, finding sensitivity depends on both distractor type and manipulation stage. Interventions including distractor augmentation, phase-dependent attention regularization, and appearance-based visual prompting improve target selection while preserving spatial control information, with gains in simulation and on a physical UR3e. The same failure pattern is confirmed in a pretrained vision-language-action policy on a state-conditioned medical instrument-handling task.

Key facts
  • Distractor sensitivity varies with visual similarity type and manipulation stage
  • Three complementary interventions improve target selection under distractors
  • Failure pattern reproduced in a pretrained vision-language-action policy
  • Robustness gains validated in simulation and on a physical UR3e robot
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From the original article

1 min read at arxiv.org

Visuomotor imitation policies can achieve high performance under in-distribution visual conditions yet fail when visually similar objects or receptacles are introduced. We study this behavior as a problem of conditional visual grounding: the visual target required for successful control changes with the manipulation phase and, in more complex tasks, with the observed task state.

arXiv cs.AI / cs.LG / cs.CL

By Vivek Chavan · arxiv.org

Read the full article at arXiv cs.AI / cs.LG / cs.CL

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