mlsanityData to 7 Oct 2026

Task-Parameterized Imitation Learning with Time-Sensitive Constraints

arXiv 2312.03506 ↗First posted 6 Dec 2023cs.ROcited by 0

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

Abstract (arXiv)

Programming a robot manipulator should be as intuitive as possible. To achieve that, the paradigm of teaching motion skills by providing few demonstrations has become widely popular in recent years. Probabilistic versions thereof take into account the uncertainty given by the distribution of the training data. However, precise execution of start-, via-, and end-poses at given times can not always be guaranteed. This limits the technology transfer to industrial application. To address this problem, we propose a novel constrained formulation of the Expectation Maximization algorithm for learning Gaussian Mixture Models (GMM) on Riemannian Manifolds. Our approach applies to probabilistic imitation learning and extends also to the well-established TP-GMM framework with Task-Parameterization. It allows to prescribe end-effector poses at defined execution times, for instance for precise pick & place scenarios. The probabilistic approach is compared with state-of-the-art learning-from-demonstration methods using the KUKA LBR iiwa robot. The reader is encouraged to watch the accompanying video available at https://youtu.be/JMI1YxtN9C0

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 4 of its 34 references that are in the map. First posted on arXiv 6 Dec 2023.

Builds on

4 of 34 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Learning from Few Demonstrations with Frame-Weighted Motion Generation24 Mar

Other references · 3

  1. 2017Kernelized Movement Primitivescited by 37
  2. 2019Memory of Motion for Warm-starting Trajectory Optimizationcited by 10
  3. 2022Learning Stable Robotic Skills on Riemannian Manifoldscited by 10

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  1. 2026Fast Non-Parametric Heteroscedastic Imitation Learning With Geometric Priors
  2. 2018Generalizing Robot Imitation Learning with Invariant Hidden Semi-Markov Models
  3. 2025Generalizing Robot Trajectories from Single-Context Human Demonstrations: A Probabilistic Approach
  4. 2019Uncertainty-Aware Imitation Learning using Kernelized Movement Primitives
  5. 2021EKMP: Generalized Imitation Learning with Adaptation, Nonlinear Hard Constraints and Obstacle Avoidance
  6. 2020Active Improvement of Control Policies with Bayesian Gaussian Mixture Model
  7. 2019Improving Task-Parameterised Movement Learning Generalisation with Frame-Weighted Trajectory Generation
  8. 2024Bayesian Optimization for Sample-Efficient Policy Improvement in Robotic Manipulation ↗