Collaborative Robot Learning from Demonstrations using Hidden Markov Model State Distribution
On the map: Offline RL and imitation learning from data → Robot learning from demonstration
Abstract (arXiv)
In robotics, there is need of an interactive and expedite learning method as experience is expensive. Robot Learning from Demonstration (RLfD) enables a robot to learn a policy from demonstrations performed by teacher. RLfD enables a human user to add new capabilities to a robot in an intuitive manner, without explicitly reprogramming it. In this work, we present a novel interactive framework, where a collaborative robot learns skills for trajectory based tasks from demonstrations performed by a human teacher. The robot extracts features from each demonstration called as key-points and learns a model of the demonstrated skill using Hidden Markov Model (HMM). Our experimental results show that the learned model can be used to produce a generalized trajectory based skill.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Robot learning from demonstration, one of 13 directions in Offline RL and imitation learning from data. Builds on 0 of its 16 references that are in the map. First posted on arXiv 27 Sep 2018.
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- 2024Learning from Demonstration Framework for Multi-Robot Systems Using Interaction Keypoints and Soft Actor-Critic Methods
- 2021Learning from Successful and Failed Demonstrations via Optimization
- 2018Learning Generalizable Robot Skills from Demonstrations in Cluttered Environments
- 2026Human-to-Robot Interaction: Learning from Video Demonstration for Robot Imitation
- 2023Learning from Few Demonstrations with Frame-Weighted Motion Generation