Bootstrap Your Own Skills: Learning to Solve New Tasks with Large Language Model Guidance

• URL: http://arxiv.org/abs/2310.10021v2
• Date: Tue, 17 Oct 2023 12:01:17 GMT
• Title: Bootstrap Your Own Skills: Learning to Solve New Tasks with Large Language Model Guidance
• Authors: Jesse Zhang, Jiahui Zhang, Karl Pertsch, Ziyi Liu, Xiang Ren, Minsuk Chang, Shao-Hua Sun, Joseph J. Lim
• Abstract summary: BOSS learns to accomplish new tasks by performing "skill bootstrapping" We demonstrate through experiments in realistic household environments that agents trained with our LLM-guided bootstrapping procedure outperform those trained with naive bootstrapping.
• Score: 66.615355754712
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose BOSS, an approach that automatically learns to solve new long-horizon, complex, and meaningful tasks by growing a learned skill library with minimal supervision. Prior work in reinforcement learning require expert supervision, in the form of demonstrations or rich reward functions, to learn long-horizon tasks. Instead, our approach BOSS (BOotStrapping your own Skills) learns to accomplish new tasks by performing "skill bootstrapping," where an agent with a set of primitive skills interacts with the environment to practice new skills without receiving reward feedback for tasks outside of the initial skill set. This bootstrapping phase is guided by large language models (LLMs) that inform the agent of meaningful skills to chain together. Through this process, BOSS builds a wide range of complex and useful behaviors from a basic set of primitive skills. We demonstrate through experiments in realistic household environments that agents trained with our LLM-guided bootstrapping procedure outperform those trained with naive bootstrapping as well as prior unsupervised skill acquisition methods on zero-shot execution of unseen, long-horizon tasks in new environments. Website at clvrai.com/boss.

Related papers

• Agentic Skill Discovery [19.5703917813767]
  Language-conditioned robotic skills make it possible to apply the high-level reasoning of Large Language Models (LLMs) to low-level robotic control. A remaining challenge is to acquire a diverse set of fundamental skills. We introduce a novel framework for skill discovery that is entirely driven by LLMs.
  arXiv  Detail & Related papers  (2024-05-23T19:44:03Z)
• LOTUS: Continual Imitation Learning for Robot Manipulation Through Unsupervised Skill Discovery [29.774700960178624]
  We introduce LOTUS, a continual imitation learning algorithm that empowers a physical robot to continuously and efficiently learn to solve new manipulation tasks. Continual skill discovery updates existing skills to avoid forgetting previous tasks and adds new skills to solve novel tasks. Our comprehensive experiments show that LOTUS outperforms state-of-the-art baselines by over 11% in success rate.
  arXiv  Detail & Related papers  (2023-11-03T17:38:35Z)
• Learning Options via Compression [62.55893046218824]
  We propose a new objective that combines the maximum likelihood objective with a penalty on the description length of the skills. Our objective learns skills that solve downstream tasks in fewer samples compared to skills learned from only maximizing likelihood.
  arXiv  Detail & Related papers  (2022-12-08T22:34:59Z)
• Residual Skill Policies: Learning an Adaptable Skill-based Action Space for Reinforcement Learning for Robotics [18.546688182454236]
  Skill-based reinforcement learning (RL) has emerged as a promising strategy to leverage prior knowledge for accelerated robot learning. We propose accelerating exploration in the skill space using state-conditioned generative models. We validate our approach across four challenging manipulation tasks, demonstrating our ability to learn across task variations.
  arXiv  Detail & Related papers  (2022-11-04T02:42:17Z)
• Transferring Knowledge for Reinforcement Learning in Contact-Rich Manipulation [10.219833196479142]
  We address the challenge of transferring knowledge within a family of similar tasks by leveraging multiple skill priors. Our method learns a latent action space representing the skill embedding from demonstrated trajectories for each prior task. We have evaluated our method on a set of peg-in-hole insertion tasks and demonstrate better generalization to new tasks that have never been encountered during training.
  arXiv  Detail & Related papers  (2022-09-19T10:31:13Z)
• Skill-based Meta-Reinforcement Learning [65.31995608339962]
  We devise a method that enables meta-learning on long-horizon, sparse-reward tasks. Our core idea is to leverage prior experience extracted from offline datasets during meta-learning.
  arXiv  Detail & Related papers  (2022-04-25T17:58:19Z)
• Hierarchical Skills for Efficient Exploration [70.62309286348057]
  In reinforcement learning, pre-trained low-level skills have the potential to greatly facilitate exploration. Prior knowledge of the downstream task is required to strike the right balance between generality (fine-grained control) and specificity (faster learning) in skill design. We propose a hierarchical skill learning framework that acquires skills of varying complexity in an unsupervised manner.
  arXiv  Detail & Related papers  (2021-10-20T22:29:32Z)
• Example-Driven Model-Based Reinforcement Learning for Solving Long-Horizon Visuomotor Tasks [85.56153200251713]
  We introduce EMBR, a model-based RL method for learning primitive skills that are suitable for completing long-horizon visuomotor tasks. On a Franka Emika robot arm, we find that EMBR enables the robot to complete three long-horizon visuomotor tasks at 85% success rate.
  arXiv  Detail & Related papers  (2021-09-21T16:48:07Z)
• Hierarchical Few-Shot Imitation with Skill Transition Models [66.81252581083199]
  Few-shot Imitation with Skill Transition Models (FIST) is an algorithm that extracts skills from offline data and utilizes them to generalize to unseen tasks. We show that FIST is capable of generalizing to new tasks and substantially outperforms prior baselines in navigation experiments.
  arXiv  Detail & Related papers  (2021-07-19T15:56:01Z)
• ELSIM: End-to-end learning of reusable skills through intrinsic motivation [0.0]
  We present a novel reinforcement learning architecture which hierarchically learns and represents self-generated skills in an end-to-end way. With this architecture, an agent focuses only on task-rewarded skills while keeping the learning process of skills bottom-up.
  arXiv  Detail & Related papers  (2020-06-23T11:20:46Z)

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.