Policy Adaptation via Language Optimization: Decomposing Tasks for Few-Shot Imitation
<|reference_start|>Policy Adaptation via Language Optimization: Decomposing Tasks for Few-Shot Imitation: Learned language-conditioned robot policies often struggle to effectively adapt to new real-world tasks even when pre-trained across a diverse set of instructions. We propose a novel approach for few-shot adaptation to unseen tasks that exploits the semantic understanding of task decomposition provided by vision-language models (VLMs). Our method, Policy Adaptation via Language Optimization (PALO), combines a handful of demonstrations of a task with proposed language decompositions sampled from a VLM to quickly enable rapid nonparametric adaptation, avoiding the need for a larger fine-tuning dataset. We evaluate PALO on extensive real-world experiments consisting of challenging unseen, long-horizon robot manipulation tasks. We find that PALO is able of consistently complete long-horizon, multi-tier tasks in the real world, outperforming state of the art pre-trained generalist policies, and methods that have access to the same demonstrations.<|reference_end|>
@article{myers2024policy,
    title={Policy Adaptation via Language Optimization: Decomposing Tasks for
  Few-Shot Imitation},
    author={Vivek Myers, Bill Chunyuan Zheng, Oier Mees, Sergey Levine, Kuan Fang},
    journal={arXiv preprint arXiv:2408.16228},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.16228},
    primaryClass={cs.RO cs.LG}
}