Plan-Seq-Learn: Language Model Guided RL for Solving Long Horizon Robotics Tasks
<|reference_start|>Plan-Seq-Learn: Language Model Guided RL for Solving Long Horizon Robotics Tasks: Large Language Models (LLMs) have been shown to be capable of performing high-level planning for long-horizon robotics tasks, yet existing methods require access to a pre-defined skill library (e.g. picking, placing, pulling, pushing, navigating). However, LLM planning does not address how to design or learn those behaviors, which remains challenging particularly in long-horizon settings. Furthermore, for many tasks of interest, the robot needs to be able to adjust its behavior in a fine-grained manner, requiring the agent to be capable of modifying low-level control actions. Can we instead use the internet-scale knowledge from LLMs for high-level policies, guiding reinforcement learning (RL) policies to efficiently solve robotic control tasks online without requiring a pre-determined set of skills? In this paper, we propose Plan-Seq-Learn (PSL): a modular approach that uses motion planning to bridge the gap between abstract language and learned low-level control for solving long-horizon robotics tasks from scratch. We demonstrate that PSL achieves state-of-the-art results on over 25 challenging robotics tasks with up to 10 stages. PSL solves long-horizon tasks from raw visual input spanning four benchmarks at success rates of over 85%, out-performing language-based, classical, and end-to-end approaches. Video results and code at https://mihdalal.github.io/planseqlearn/<|reference_end|>
@article{dalal2024plan-seq-learn:,
    title={Plan-Seq-Learn: Language Model Guided RL for Solving Long Horizon
  Robotics Tasks},
    author={Murtaza Dalal, Tarun Chiruvolu, Devendra Chaplot, Ruslan Salakhutdinov},
    journal={arXiv preprint arXiv:2405.01534},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.01534},
    primaryClass={cs.LG cs.AI cs.CV cs.RO}
}