Plan-Seq-Learn: Language Model Guided RL for Solving Long Horizon Robotics Tasks

• URL: http://arxiv.org/abs/2405.01534v1
• Date: Thu, 2 May 2024 17:59:31 GMT
• Title: Plan-Seq-Learn: Language Model Guided RL for Solving Long Horizon Robotics Tasks
• Authors: Murtaza Dalal, Tarun Chiruvolu, Devendra Chaplot, Ruslan Salakhutdinov,
• Abstract summary: Plan-Seq-Learn (PSL) is a modular approach that uses motion planning to bridge the gap between abstract language and learned low-level control. PSL achieves success rates of over 85%, out-performing language-based, classical, and end-to-end approaches.
• Score: 50.27313829438866
• License: http://creativecommons.org/licenses/by/4.0/
• 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 https://mihdalal.github.io/planseqlearn/

Related papers

• From LLMs to Actions: Latent Codes as Bridges in Hierarchical Robot Control [58.72492647570062]
  We introduce our method -- Learnable Latent Codes as Bridges (LCB) -- as an alternate architecture to overcome limitations. We find that methodoutperforms baselines that leverage pure language as the interface layer on tasks that require reasoning and multi-step behaviors.
  arXiv  Detail & Related papers  (2024-05-08T04:14:06Z)
• Large Language Models for Orchestrating Bimanual Robots [19.60907949776435]
  We present LAnguage-model-based Bimanual ORchestration (LABOR) to analyze task configurations and devise coordination control policies. We evaluate our method through simulated experiments involving two classes of long-horizon tasks using the NICOL humanoid robot.
  arXiv  Detail & Related papers  (2024-04-02T15:08:35Z)
• Yell At Your Robot: Improving On-the-Fly from Language Corrections [84.09578841663195]
  We show that high-level policies can be readily supervised with human feedback in the form of language corrections. This framework enables robots not only to rapidly adapt to real-time language feedback, but also incorporate this feedback into an iterative training scheme.
  arXiv  Detail & Related papers  (2024-03-19T17:08:24Z)
• Language Models as Zero-Shot Trajectory Generators [10.572264780575564]
  Large Language Models (LLMs) have recently shown promise as high-level planners for robots. It is often assumed that LLMs do not possess sufficient knowledge to be used for the low-level trajectories themselves. This work investigates if an LLM can directly predict a dense sequence of end-effector poses for manipulation tasks.
  arXiv  Detail & Related papers  (2023-10-17T21:57:36Z)
• Lifelong Robot Learning with Human Assisted Language Planners [24.66094264866298]
  We present a method for using LLM-based planners to query new skills and teach robots these skills in a data and time-efficient manner for rigid object manipulation. Our system can re-use newly acquired skills for future tasks, demonstrating the potential of open world and lifelong learning.
  arXiv  Detail & Related papers  (2023-09-25T17:45:55Z)
• Learning to Reason over Scene Graphs: A Case Study of Finetuning GPT-2 into a Robot Language Model for Grounded Task Planning [45.51792981370957]
  We investigate the applicability of a smaller class of large language models (LLMs) in robotic task planning by learning to decompose tasks into subgoal specifications for a planner to execute sequentially. Our method grounds the input of the LLM on the domain that is represented as a scene graph, enabling it to translate human requests into executable robot plans. Our findings suggest that the knowledge stored in an LLM can be effectively grounded to perform long-horizon task planning, demonstrating the promising potential for the future application of neuro-symbolic planning methods in robotics.
  arXiv  Detail & Related papers  (2023-05-12T18:14:32Z)
• Plan, Eliminate, and Track -- Language Models are Good Teachers for Embodied Agents [99.17668730578586]
  Pre-trained large language models (LLMs) capture procedural knowledge about the world. Plan, Eliminate, and Track (PET) framework translates a task description into a list of high-level sub-tasks. PET framework leads to a significant 15% improvement over SOTA for generalization to human goal specifications.
  arXiv  Detail & Related papers  (2023-05-03T20:11:22Z)
• Efficient Learning of High Level Plans from Play [57.29562823883257]
  We present Efficient Learning of High-Level Plans from Play (ELF-P), a framework for robotic learning that bridges motion planning and deep RL. We demonstrate that ELF-P has significantly better sample efficiency than relevant baselines over multiple realistic manipulation tasks.
  arXiv  Detail & Related papers  (2023-03-16T20:09:47Z)
• Hierarchies of Planning and Reinforcement Learning for Robot Navigation [22.08479169489373]
  In many navigation tasks, high-level (HL) task representations, like a rough floor plan, are available. Previous work has demonstrated efficient learning by hierarchal approaches consisting of path planning in the HL representation. This work proposes a novel hierarchical framework that utilizes a trainable planning policy for the HL representation.
  arXiv  Detail & Related papers  (2021-09-23T07:18:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.