mlsanityData to 7 Oct 2026

Generalizing Robot Imitation Learning with Invariant Hidden Semi-Markov Models

arXiv 1811.07489 ↗First posted 19 Nov 2018cs.ROcited by 1

On the map: Offline RL and imitation learning from data → Robot manipulation imitation learning

Abstract (arXiv)

Generalizing manipulation skills to new situations requires extracting invariant patterns from demonstrations. For example, the robot needs to understand the demonstrations at a higher level while being invariant to the appearance of the objects, geometric aspects of objects such as its position, size, orientation and viewpoint of the observer in the demonstrations. In this paper, we propose an algorithm that learns a joint probability density function of the demonstrations with invariant formulations of hidden semi-Markov models to extract invariant segments (also termed as sub-goals or options), and smoothly follow the generated sequence of states with a linear quadratic tracking controller. The algorithm takes as input the demonstrations with respect to different coordinate systems describing virtual landmarks or objects of interest with a task-parameterized formulation, and adapt the segments according to the environmental changes in a systematic manner. We present variants of this algorithm in latent space with low-rank covariance decompositions, semi-tied covariances, and non-parametric online estimation of model parameters under small variance asymptotics; yielding considerably low sample and model complexity for acquiring new manipulation skills. The algorithm allows a Baxter robot to learn a pick-and-place task while avoiding a movable obstacle based on only 4 kinesthetic demonstrations.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Robot manipulation imitation learning, one of 13 directions in Offline RL and imitation learning from data. Builds on 9 of its 37 references that are in the map. First posted on arXiv 19 Nov 2018.

Builds on

9 of 37 references.

References · 9

  1. 2016Generative Adversarial Imitation Learningcited by 1,576
  2. 2018An Algorithmic Perspective on Imitation Learningcited by 455
  3. 2017One-Shot Imitation Learningcited by 232
  4. 2017Neural Task Programming: Learning to Generalize Across Hierarchical Taskscited by 75
  5. 2018TACO: Learning Task Decomposition via Temporal Alignment for Controlcited by 61
  6. 2017Multi-Level Discovery of Deep Optionscited by 50
  7. 2017DDCO: Discovery of Deep Continuous Options for Robot Learning from Demonstrationscited by 43
  8. 2016Small Variance Asymptotics for Non-Parametric Online Robot Learningcited by 3
  9. 2017Transform-Invariant Non-Parametric Clustering of Covariance Matrices and its Application to Unsupervised Joint Segmentation and Action Discoverycited by 1

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Citing papers per year: 2024: 1.

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  1. 2024Autonomous Vehicles Path Planning under Temporal Logic Specificationscited by 0

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  1. 2020Learning to Generalize Across Long-Horizon Tasks from Human Demonstrations
  2. 2022BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning
  3. 2025Generalization Capability for Imitation Learning
  4. 2018Auto-conditioned Recurrent Mixture Density Networks for Learning Generalizable Robot Skills
  5. 2024Learning from 10 Demos: Generalisable and Sample-Efficient Policy Learning with Oriented Affordance Frames
  6. 2021Coarse-to-Fine Imitation Learning: Robot Manipulation from a Single Demonstration
  7. 2024Offline Imitation Learning Through Graph Search and Retrieval
  8. 2024A Probabilistic Model for Skill Acquisition with Switching Latent Feedback Controllers