Abstract
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 this https URL
Abstract (translated)
大语言模型（LLMs）已经被证明在长时间的机器人任务中具有执行高级计划的能力。然而，现有的方法需要访问预定义的技能库（例如抓取、放置、拖动、推开、导航）。然而，LLM计划并没有解决如何设计或学习这些行为，这使得在长时间设置中解决这个问题变得更加具有挑战性。此外，对于许多感兴趣的任务，机器人需要能够以细粒度的方式调整其行为，要求代理具备修改低级控制动作的能力。我们可以 instead 使用LLM在高级策略上进行知识表示，指导强化学习（RL）策略有效地解决机器人控制任务，而无需预先确定一组技能？在本文中，我们提出了Plan-Seq-Learn（PSL）：一种模块化方法，使用运动规划来桥接抽象语言和学习的低级控制，以从零开始解决长时间的机器人任务。我们证明了PSL在超过25个具有挑战性的机器人任务上取得了最先进的成果，其中包括10个阶段。PSL通过从成功的视觉输入中解决长期机器人任务，其成功率超过85%，超过了基于语言的传统方法、基于任务的端到端方法和基于知识的方法。视频结果和代码在此处：https://url
URL
https://arxiv.org/abs/2405.01534
