Announcement
VAP-TAMP wins best paper at IROS workshop on full-shift robot co-workers
A contributor says it combines large language models with task and motion planning to handle failures and out-of-distribution scenarios.
TLDR
A contributor to VAP-TAMP says the work won best paper at an IROS workshop on full-shift robot co-workers. It combines task and motion planning with large language models, an approach the contributor says helps handle failures and out-of-distribution scenarios. They argue that handling edge cases autonomously will be crucial to reliable, long-horizon robot deployment.
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