Sequential Discrete Action Selection via Blocking Conditions and Resolutions
<|reference_start|>Sequential Discrete Action Selection via Blocking Conditions and Resolutions: In this work, we introduce a strategy that frames the sequential action selection problem for robots in terms of resolving \textit{blocking conditions}, i.e., situations that impede progress on an action en route to a goal. This strategy allows a robot to make one-at-a-time decisions that take in pertinent contextual information and swiftly adapt and react to current situations. We present a first instantiation of this strategy that combines a state-transition graph and a zero-shot Large Language Model (LLM). The state-transition graph tracks which previously attempted actions are currently blocked and which candidate actions may resolve existing blocking conditions. This information from the state-transition graph is used to automatically generate a prompt for the LLM, which then uses the given context and set of possible actions to select a single action to try next. This selection process is iterative, with each chosen and executed action further refining the state-transition graph, continuing until the agent either fulfills the goal or encounters a termination condition. We demonstrate the effectiveness of our approach by comparing it to various LLM and traditional task-planning methods in a testbed of simulation experiments. We discuss the implications of our work based on our results.<|reference_end|>
@article{hoffmeister2024sequential,
    title={Sequential Discrete Action Selection via Blocking Conditions and
  Resolutions},
    author={Liam Merz Hoffmeister, Brian Scassellati, Daniel Rakita},
    journal={arXiv preprint arXiv:2409.08410},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.08410},
    primaryClass={cs.RO}
}