SkillPlug: Unsupervised Skill Mining for Few-Shot Adaptation in Robotic Manipulation

• URL: http://arxiv.org/abs/2607.08354v1
• Date: Thu, 09 Jul 2026 11:02:35 GMT
• Title: SkillPlug: Unsupervised Skill Mining for Few-Shot Adaptation in Robotic Manipulation
• Abstract summary: SkillPlug is a plug-in framework that augments an existing visuomotor policy with a skill-conditioning module.<n>SkillPlug learns skills via self-supervised objectives that promote compact, reusable, and non-redundant behavior-level primitives.<n>We evaluate SkillPlug on two simulation benchmarks and on a real robot, and observe that the mined transferable skills consistently improve both multi-task performance and few-shot adaptation.
• Score: 18.300772557133072
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning transferable visuomotor imitation policies that generalize across diverse manipulation tasks and adapt rapidly to new tasks from only a handful of demonstrations remains challenging. Most modern policies are trained end-to-end to map observations directly to low-level actions, offering little explicit structure for reusing and recombining behaviors across tasks and making transfer data-inefficient under limited supervision. We propose SkillPlug, a plug-in framework that augments an existing visuomotor policy with a skill-conditioning module and mines a shared, transferable skill library from raw multi-task demonstrations. SkillPlug learns skills via self-supervised objectives that promote compact, reusable, and non-redundant behavior-level primitives, forming a task-shared prior for compositional control. After skill mining, we keep the learned skills fixed and specialize to unseen tasks by fine-tuning only lightweight router and action head, enabling efficient adaptation without full end-to-end retraining. We evaluate SkillPlug on two simulation benchmarks and on a real robot, and observe that the mined transferable skills consistently improve both multi-task performance and few-shot adaptation. Overall, SkillPlug offers a scalable way to mine reusable skills that improve data-efficient generalization in robotic manipulation.

Related papers

• SkillComposer: Learning to Evolve Agent Skills for Specification and Generalization [99.18305770138677]
  Agent skills are reusable strategies that guide agent reasoning and action.<n>Current skill construction methods treat the problem as one-shot extraction.<n>We introduce SkillComposer, a framework that decomposes skill construction into three learnable operations.
  arXiv  Detail & Related papers  (2026-06-04T12:16:08Z)
• Uni-Skill: Building Self-Evolving Skill Repository for Generalizable Robotic Manipulation [32.86306309089796]
  Uni-Skill is a skill-centric framework that supports skill-aware planning.<n>Uni-Skill requests for new skill implementations when existing ones are insufficient.<n>Skill is a VerbNet-inspired repository derived from large-scale unstructured robotic videos.
  arXiv  Detail & Related papers  (2026-03-03T05:49:37Z)
• SkillBlender: Towards Versatile Humanoid Whole-Body Loco-Manipulation via Skill Blending [79.83865372778273]
  We introduce SkillBlender, a novel hierarchical reinforcement learning framework for versatile humanoid loco-manipulation.<n>SkillBlender first pretrains goal-conditioned task-agnostic primitive skills, and then dynamically blends these skills to accomplish complex loco-manipulation tasks.<n>We also introduce SkillBench, a parallel, cross-embodiment, and diverse simulated benchmark containing three embodiments, four primitive skills, and eight challenging loco-manipulation tasks.
  arXiv  Detail & Related papers  (2025-06-11T03:24:26Z)
• SPECI: Skill Prompts based Hierarchical Continual Imitation Learning for Robot Manipulation [3.1997825444285457]
  Real-world robot manipulation in dynamic unstructured environments requires lifelong adaptability to evolving objects, scenes and tasks.<n>Traditional imitation learning relies on static training paradigms, which are ill-suited for lifelong adaptation.<n>We propose Skill Prompts-based HiErarchical Continual Imitation Learning (SPECI), a novel end-to-end hierarchical CIL policy architecture for robot manipulation.
  arXiv  Detail & Related papers  (2025-04-22T03:30:38Z)
• Transferable Latent-to-Latent Locomotion Policy for Efficient and Versatile Motion Control of Diverse Legged Robots [9.837559106057814]
  The pretrain-and-finetune paradigm offers a promising approach for efficiently adapting to new robot entities and tasks.<n>We propose a latent training framework where a transferable latent-to-latent locomotion policy is pretrained alongside diverse task-specific observation encoders and action decoders.<n>We validate our approach through extensive simulations and real-world experiments, demonstrating that the pretrained latent-to-latent locomotion policy effectively generalizes to new robot entities and tasks with improved efficiency.
  arXiv  Detail & Related papers  (2025-03-22T03:01:25Z)
• Skill Transformer: A Monolithic Policy for Mobile Manipulation [36.18813073796717]
  We present Skill Transformer, an approach for solving long-horizon robotic tasks by combining conditional sequence modeling and skill modularity. Conditioned on egocentric and proprioceptive observations of a robot, Skill Transformer is trained end-to-end to predict both a high-level skill and a whole-body low-level action. We test Skill Transformer on an embodied benchmark and find it performs robust task planning and low-level control in new scenarios, achieving a 2.5x higher success rate than baselines in hard rearrangement problems.
  arXiv  Detail & Related papers  (2023-08-19T01:37:41Z)
• Effective Adaptation in Multi-Task Co-Training for Unified Autonomous Driving [103.745551954983]
  In this paper, we investigate the transfer performance of various types of self-supervised methods, including MoCo and SimCLR, on three downstream tasks. We find that their performances are sub-optimal or even lag far behind the single-task baseline. We propose a simple yet effective pretrain-adapt-finetune paradigm for general multi-task training.
  arXiv  Detail & Related papers  (2022-09-19T12:15:31Z)
• ASE: Large-Scale Reusable Adversarial Skill Embeddings for Physically Simulated Characters [123.88692739360457]
  General-purpose motor skills enable humans to perform complex tasks. These skills also provide powerful priors for guiding their behaviors when learning new tasks. We present a framework for learning versatile and reusable skill embeddings for physically simulated characters.
  arXiv  Detail & Related papers  (2022-05-04T06:13:28Z)
• Unsupervised Reinforcement Learning for Transferable Manipulation Skill Discovery [22.32327908453603]
  Current reinforcement learning (RL) in robotics often experiences difficulty in generalizing to new downstream tasks. We propose a framework that pre-trains the agent in a task-agnostic manner without access to the task-specific reward. We show that our approach achieves the most diverse interacting behavior and significantly improves sample efficiency in downstream tasks.
  arXiv  Detail & Related papers  (2022-04-29T06:57:46Z)
• 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)
• 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)

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.