mlsanityData to 14 Oct 2026

A Framework for Learning and Reusing Robotic Skills

arXiv 2404.18383 ↗First posted 29 Apr 2024cs.ROcited by 1

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Abstract (arXiv)

In this paper, we present our work in progress towards creating a library of motion primitives. This library facilitates easier and more intuitive learning and reusing of robotic skills. Users can teach robots complex skills through Learning from Demonstration, which is automatically segmented into primitives and stored in clusters of similar skills. We propose a novel multimodal segmentation method as well as a novel trajectory clustering method. Then, when needed for reuse, we transform primitives into new environments using trajectory editing. We present simulated results for our framework with demonstrations taken on real-world robots.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). First posted on arXiv 29 Apr 2024.

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  1. 2018Auto-conditioned Recurrent Mixture Density Networks for Learning Generalizable Robot Skills
  2. 2022A Unified Formulation of Geometry-aware Dynamic Movement Primitives
  3. 2021Similarity-Aware Skill Reproduction based on Multi-Representational Learning from Demonstration
  4. 2023RH20T: A Comprehensive Robotic Dataset for Learning Diverse Skills in One-Shot
  5. 2026SparTa: Sparse Graphical Task Models from a Handful of Demonstrations
  6. 2026Uni-Skill: Building Self-Evolving Skill Repository for Generalizable Robotic Manipulation
  7. 2022Continual Learning from Demonstration of Robotics Skills
  8. 2025An Alignment-Based Approach to Learning Motions from Demonstrations