  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/
