# Uncertainty-Aware Imitation Learning using Kernelized Movement Primitives

arXiv: https://arxiv.org/abs/1903.02114 · HTML: https://mlsanity.com/paper/1903.02114/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Placed in Robot learning from demonstration, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 3 of its 26 references that are in the map.
- First posted on arXiv 5 Mar 2019.

## Placed in

- Offline RL and imitation learning from data / [Robot learning from demonstration](https://mlsanity.com/offlinerl/robot-learning-from-demonstration/)

## Builds on (most cited first, 3 of 3)

- [Kernelized Movement Primitives](https://mlsanity.com/paper/1708.08638/) (2017)
- [Uncertainty-Aware Learning from Demonstration using Mixture Density Networks with Sampling-Free Variance Modeling](https://mlsanity.com/paper/1709.02249/) (2017)
- [Probabilistic Learning of Torque Controllers from Kinematic and Force Constraints](https://mlsanity.com/paper/1712.07249/) (2017)

## Built on by (most cited first, 11 of 11)

- [HYDRA: Hybrid Robot Actions for Imitation Learning](https://mlsanity.com/paper/2306.17237/) (2023)
- [Gaussian-Process-based Robot Learning from Demonstration](https://mlsanity.com/paper/2002.09979/) (2020)
- [Interactive incremental learning of generalizable skills with local trajectory modulation](https://mlsanity.com/paper/2409.05655/) (2024)
- [Get Back Here: Robust Imitation by Return-to-Distribution Planning](https://mlsanity.com/paper/2305.01400/) (2023)
- [A Linearly Constrained Nonparametric Framework for Imitation Learning](https://mlsanity.com/paper/1909.07374/) (2019)
- [EKMP: Generalized Imitation Learning with Adaptation, Nonlinear Hard Constraints and Obstacle Avoidance](https://mlsanity.com/paper/2103.00452/) (2021)
- [COIN: Chance-Constrained Imitation Learning for Uncertainty-aware Adaptive Resource Oversubscription Policy](https://mlsanity.com/paper/2401.07051/) (2024)
- [A Non-parametric Skill Representation with Soft Null Space Projectors for Fast Generalization](https://mlsanity.com/paper/2209.08522/) (2022)
- [Act, Think or Abstain: Complexity-Aware Adaptive Inference for Vision-Language-Action Models](https://mlsanity.com/paper/2603.05147/) (2026)
- [CLASP: Language-Driven Robot Skill Selection and Composition using Task-Parameterized Learning](https://mlsanity.com/paper/2606.08169/) (2026)
- [Learning from Demonstration via Spatiotemporal Tubes for Unknown Euler-Lagrange Systems](https://mlsanity.com/paper/2607.00534/) (2026)

## Abstract (arXiv)

During the past few years, probabilistic approaches to imitation learning have earned a relevant place in the literature. One of their most prominent features, in addition to extracting a mean trajectory from task demonstrations, is that they provide a variance estimation. The intuitive meaning of this variance, however, changes across different techniques, indicating either variability or uncertainty. In this paper we leverage kernelized movement primitives (KMP) to provide a new perspective on imitation learning by predicting variability, correlations and uncertainty about robot actions. This rich set of information is used in combination with optimal controller fusion to learn actions from data, with two main advantages: i) robots become safe when uncertain about their actions and ii) they are able to leverage partial demonstrations, given as elementary sub-tasks, to optimally perform a higher level, more complex task. We showcase our approach in a painting task, where a human user and a KUKA robot collaborate to paint a wooden board. The task is divided into two sub-tasks and we show that using our approach the robot becomes compliant (hence safe) outside the training regions and executes the two sub-tasks with optimal gains.