Think Small, Act Big: Primitive Prompt Learning for Lifelong Robot Manipulation

• URL: http://arxiv.org/abs/2504.00420v1
• Date: Tue, 01 Apr 2025 04:55:34 GMT
• Title: Think Small, Act Big: Primitive Prompt Learning for Lifelong Robot Manipulation
• Authors: Yuanqi Yao, Siao Liu, Haoming Song, Delin Qu, Qizhi Chen, Yan Ding, Bin Zhao, Zhigang Wang, Xuelong Li, Dong Wang,
• Abstract summary: We propose Primitive Prompt Learning to achieve lifelong robot manipulation via reusable and primitives.<n>We first learn a set of primitive prompts to represent shared primitives through multi-skills pre-training stage.<n>New prompts are appended and optimized with frozen pretrained prompts, boosting the learning via knowledge transfer from old skills to new ones.
• Score: 43.350196832118215
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Building a lifelong robot that can effectively leverage prior knowledge for continuous skill acquisition remains significantly challenging. Despite the success of experience replay and parameter-efficient methods in alleviating catastrophic forgetting problem, naively applying these methods causes a failure to leverage the shared primitives between skills. To tackle these issues, we propose Primitive Prompt Learning (PPL), to achieve lifelong robot manipulation via reusable and extensible primitives. Within our two stage learning scheme, we first learn a set of primitive prompts to represent shared primitives through multi-skills pre-training stage, where motion-aware prompts are learned to capture semantic and motion shared primitives across different skills. Secondly, when acquiring new skills in lifelong span, new prompts are appended and optimized with frozen pretrained prompts, boosting the learning via knowledge transfer from old skills to new ones. For evaluation, we construct a large-scale skill dataset and conduct extensive experiments in both simulation and real-world tasks, demonstrating PPL's superior performance over state-of-the-art methods.

Related papers

• Pretrained Bayesian Non-parametric Knowledge Prior in Robotic Long-Horizon Reinforcement Learning [10.598207472087578]
  Reinforcement learning (RL) methods typically learn new tasks from scratch, often disregarding prior knowledge that could accelerate the learning process.<n>This work introduces a method that models potential primitive skill motions as having non-parametric properties with an unknown number of underlying features.<n>We utilize a non-parametric model, specifically Dirichlet Process Mixtures, enhanced with birth and merge, to pre-train a skill prior that effectively captures the diverse nature of skills.
  arXiv  Detail & Related papers  (2025-03-27T20:43:36Z)
• SPIRE: Synergistic Planning, Imitation, and Reinforcement Learning for Long-Horizon Manipulation [58.14969377419633]
  We propose spire, a system that decomposes tasks into smaller learning subproblems and second combines imitation and reinforcement learning to maximize their strengths. We find that spire outperforms prior approaches that integrate imitation learning, reinforcement learning, and planning by 35% to 50% in average task performance.
  arXiv  Detail & Related papers  (2024-10-23T17:42:07Z)
• Unsupervised Skill Discovery for Robotic Manipulation through Automatic Task Generation [17.222197596599685]
  We propose a Skill Learning approach that discovers composable behaviors by solving a large number of autonomously generated tasks. Our method learns skills allowing the robot to consistently and robustly interact with objects in its environment. The learned skills can be used to solve a set of unseen manipulation tasks, in simulation as well as on a real robotic platform.
  arXiv  Detail & Related papers  (2024-10-07T09:19:13Z)
• Task-free Lifelong Robot Learning with Retrieval-based Weighted Local Adaptation [17.215730187681952]
  We store a subset of data from previous tasks and utilize it in two manners: leveraging experience replay to retain learned skills and applying a novel Retrieval-based Local Adaptation technique to restore relevant knowledge.<n>We also incorporate a selective weighting mechanism to focus on the most "forgotten" skill segment, ensuring effective knowledge restoration.
  arXiv  Detail & Related papers  (2024-10-03T21:11:42Z)
• Learning and Retrieval from Prior Data for Skill-based Imitation Learning [47.59794569496233]
  We develop a skill-based imitation learning framework that extracts temporally extended sensorimotor skills from prior data. We identify several key design choices that significantly improve performance on novel tasks.
  arXiv  Detail & Related papers  (2022-10-20T17:34:59Z)
• Bottom-Up Skill Discovery from Unsegmented Demonstrations for Long-Horizon Robot Manipulation [55.31301153979621]
  We tackle real-world long-horizon robot manipulation tasks through skill discovery. We present a bottom-up approach to learning a library of reusable skills from unsegmented demonstrations. Our method has shown superior performance over state-of-the-art imitation learning methods in multi-stage manipulation tasks.
  arXiv  Detail & Related papers  (2021-09-28T16:18:54Z)
• Lifelong Robotic Reinforcement Learning by Retaining Experiences [61.79346922421323]
  Many multi-task reinforcement learning efforts assume the robot can collect data from all tasks at all times. In this work, we study a practical sequential multi-task RL problem motivated by the practical constraints of physical robotic systems. We derive an approach that effectively leverages the data and policies learned for previous tasks to cumulatively grow the robot's skill-set.
  arXiv  Detail & Related papers  (2021-09-19T18:00:51Z)
• Scalable Multi-Task Imitation Learning with Autonomous Improvement [159.9406205002599]
  We build an imitation learning system that can continuously improve through autonomous data collection. We leverage the robot's own trials as demonstrations for tasks other than the one that the robot actually attempted. In contrast to prior imitation learning approaches, our method can autonomously collect data with sparse supervision for continuous improvement.
  arXiv  Detail & Related papers  (2020-02-25T18:56:42Z)

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.