Temporal and Semantic Evaluation Metrics for Foundation Models in
  Post-Hoc Analysis of Robotic Sub-tasks
Jonathan Salfity Selma Wanna Minkyu Choi Mitch Pryor
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)
including both Large Language Models (LLMs) and Vision Language Models (VLMs).
Our framework provides both time-based and language-based descriptions for
lower-level sub-tasks that comprise full trajectories. To rigorously evaluate
the quality of our automatic labeling framework, we contribute an algorithm
SIMILARITY to produce two novel metrics, temporal similarity and semantic
similarity. The metrics measure the temporal alignment and semantic fidelity of
language descriptions between two sub-task decompositions, namely an FM
sub-task decomposition prediction and a ground-truth sub-task decomposition. We
present scores for temporal similarity and semantic similarity above 90%,
compared to 30% of a randomized baseline, for multiple robotic environments,
demonstrating the effectiveness of our proposed framework. Our results enable
building diverse, large-scale, language-supervised datasets for improved
robotic TAMP.