CurricuLLM: Automatic Task Curricula Design for Learning Complex Robot Skills using Large Language Models

• URL: http://arxiv.org/abs/2409.18382v1
• Date: Fri, 27 Sep 2024 01:48:16 GMT
• Title: CurricuLLM: Automatic Task Curricula Design for Learning Complex Robot Skills using Large Language Models
• Authors: Kanghyun Ryu, Qiayuan Liao, Zhongyu Li, Koushil Sreenath, Negar Mehr,
• Abstract summary: CurricuLLM is a curriculum learning tool for complex robot control tasks. It generates subtasks that aid target task learning in natural language form. It also translates natural language description of subtasks into executable code. CurricuLLM can aid learning complex robot control tasks.
• Score: 19.73329768987112
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Curriculum learning is a training mechanism in reinforcement learning (RL) that facilitates the achievement of complex policies by progressively increasing the task difficulty during training. However, designing effective curricula for a specific task often requires extensive domain knowledge and human intervention, which limits its applicability across various domains. Our core idea is that large language models (LLMs), with their extensive training on diverse language data and ability to encapsulate world knowledge, present significant potential for efficiently breaking down tasks and decomposing skills across various robotics environments. Additionally, the demonstrated success of LLMs in translating natural language into executable code for RL agents strengthens their role in generating task curricula. In this work, we propose CurricuLLM, which leverages the high-level planning and programming capabilities of LLMs for curriculum design, thereby enhancing the efficient learning of complex target tasks. CurricuLLM consists of: (Step 1) Generating sequence of subtasks that aid target task learning in natural language form, (Step 2) Translating natural language description of subtasks in executable task code, including the reward code and goal distribution code, and (Step 3) Evaluating trained policies based on trajectory rollout and subtask description. We evaluate CurricuLLM in various robotics simulation environments, ranging from manipulation, navigation, and locomotion, to show that CurricuLLM can aid learning complex robot control tasks. In addition, we validate humanoid locomotion policy learned through CurricuLLM in real-world. The code is provided in https://github.com/labicon/CurricuLLM

Related papers

• Plan-Seq-Learn: Language Model Guided RL for Solving Long Horizon Robotics Tasks [50.27313829438866]
  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.
  arXiv  Detail & Related papers  (2024-05-02T17:59:31Z)
• 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)
• Natural Language as Policies: Reasoning for Coordinate-Level Embodied Control with LLMs [7.746160514029531]
  We demonstrate experimental results with LLMs that address robotics task planning problems. Our approach acquires text descriptions of the task and scene objects, then formulates task planning through natural language reasoning. Our approach is evaluated on a multi-modal prompt simulation benchmark.
  arXiv  Detail & Related papers  (2024-03-20T17:58:12Z)
• Interactive Planning Using Large Language Models for Partially Observable Robotics Tasks [54.60571399091711]
  Large Language Models (LLMs) have achieved impressive results in creating robotic agents for performing open vocabulary tasks. We present an interactive planning technique for partially observable tasks using LLMs.
  arXiv  Detail & Related papers  (2023-12-11T22:54:44Z)
• TaskBench: Benchmarking Large Language Models for Task Automation [82.2932794189585]
  We introduce TaskBench, a framework to evaluate the capability of large language models (LLMs) in task automation. Specifically, task decomposition, tool selection, and parameter prediction are assessed. Our approach combines automated construction with rigorous human verification, ensuring high consistency with human evaluation.
  arXiv  Detail & Related papers  (2023-11-30T18:02:44Z)
• LMRL Gym: Benchmarks for Multi-Turn Reinforcement Learning with Language Models [56.25156596019168]
  This paper introduces the LMRL-Gym benchmark for evaluating multi-turn RL for large language models (LLMs) Our benchmark consists of 8 different language tasks, which require multiple rounds of language interaction and cover a range of tasks in open-ended dialogue and text games.
  arXiv  Detail & Related papers  (2023-11-30T03:59:31Z)
• LARG, Language-based Automatic Reward and Goal Generation [8.404316955848602]
  We develop an approach that converts a text-based task description into its corresponding reward and goal-generation functions. We evaluate our approach for robotic manipulation and demonstrate its ability to train and execute policies in a scalable manner.
  arXiv  Detail & Related papers  (2023-06-19T14:52:39Z)
• AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkers [20.857692296678632]
  For effective human-robot interaction, robots need to understand, plan, and execute complex, long-horizon tasks. Recent advances in large language models have shown promise for translating natural language into robot action sequences. We show that our approach outperforms several methods using LLMs as planners in complex task domains.
  arXiv  Detail & Related papers  (2023-06-10T21:58:29Z)
• Instruct2Act: Mapping Multi-modality Instructions to Robotic Actions with Large Language Model [63.66204449776262]
  Instruct2Act is a framework that maps multi-modal instructions to sequential actions for robotic manipulation tasks. Our approach is adjustable and flexible in accommodating various instruction modalities and input types. Our zero-shot method outperformed many state-of-the-art learning-based policies in several tasks.
  arXiv  Detail & Related papers  (2023-05-18T17:59:49Z)
• Grounding Language with Visual Affordances over Unstructured Data [26.92329260907805]
  We propose a novel approach to efficiently learn language-conditioned robot skills from unstructured, offline and reset-free data. We exploit a self-supervised visuo-lingual affordance model, which requires as little as 1% of the total data with language. We find that our method is capable of completing long-horizon, multi-tier tasks in the real world, while requiring an order of magnitude less data than previous approaches.
  arXiv  Detail & Related papers  (2022-10-04T21:16:48Z)
• ProgPrompt: Generating Situated Robot Task Plans using Large Language Models [68.57918965060787]
  Large language models (LLMs) can be used to score potential next actions during task planning. We present a programmatic LLM prompt structure that enables plan generation functional across situated environments.
  arXiv  Detail & Related papers  (2022-09-22T20:29:49Z)

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.