Temporal and Semantic Evaluation Metrics for Foundation Models in Post-Hoc Analysis of Robotic Sub-tasks

arXiv:2403.17238v1 Announce Type: cross
Abstract: Recent works in Task and Motion Planning (TAMP) show that training control policies on language-supervised robot trajectories with quality labeled data markedly improves agent task success rates. However, the scarcity of such data presents a significant hurdle to extending these methods to general use cases. To address this concern, we present an automated framework to decompose trajectory data into temporally bounded and natural language-based descriptive sub-tasks by leveraging recent prompting strategies for Foundation Models (FMs) …

abstract agent analysis arxiv control cs.lg cs.ro data evaluation evaluation metrics foundation however language metrics motion planning planning policies quality robot robotic semantic show success tasks temporal training type