# Gaussian-Process-based Robot Learning from Demonstration

arXiv: https://arxiv.org/abs/2002.09979 · HTML: https://mlsanity.com/paper/2002.09979/

## Key facts

- Cited by 5 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 8 of its 60 references that are in the map.
- First posted on arXiv 23 Feb 2020.

## 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, 8 of 8)

- [When Gaussian Process Meets Big Data: A Review of Scalable GPs](https://mlsanity.com/paper/1807.01065/) (2018)
- [Distributed Gaussian Processes](https://mlsanity.com/paper/1502.02843/) (2015)
- [Kernelized Movement Primitives](https://mlsanity.com/paper/1708.08638/) (2017)
- [Towards Orientation Learning and Adaptation in Cartesian Space](https://mlsanity.com/paper/1907.03918/) (2019)
- [Uncertainty-Aware Imitation Learning using Kernelized Movement Primitives](https://mlsanity.com/paper/1903.02114/) (2019)
- [Bayesian Gaussian mixture model for robotic policy imitation](https://mlsanity.com/paper/1904.10716/) (2019)
- [Probabilistic Learning of Torque Controllers from Kinematic and Force Constraints](https://mlsanity.com/paper/1712.07249/) (2017)
- [Learning from demonstration with model-based Gaussian process](https://mlsanity.com/paper/1910.05005/) (2019)

## Built on by (most cited first, 5 of 5)

- [PUMA: Deep Metric Imitation Learning for Stable Motion Primitives](https://mlsanity.com/paper/2310.12831/) (2023)
- [Conditional Neural Expert Processes for Learning Movement Primitives from Demonstration](https://mlsanity.com/paper/2402.08424/) (2024)
- [The Unreasonable Effectiveness of Discrete-Time Gaussian Process Mixtures for Robot Policy Learning](https://mlsanity.com/paper/2505.03296/) (2025)
- [Task-Adaptive Robot Learning from Demonstration with Gaussian Process Models under Replication](https://mlsanity.com/paper/2010.07795/) (2020)
- [Learning from Demonstration via Spatiotemporal Tubes for Unknown Euler-Lagrange Systems](https://mlsanity.com/paper/2607.00534/) (2026)

## Abstract (arXiv)

Endowed with higher levels of autonomy, robots are required to perform increasingly complex manipulation tasks. Learning from demonstration is arising as a promising paradigm for transferring skills to robots. It allows to implicitly learn task constraints from observing the motion executed by a human teacher, which can enable adaptive behavior. We present a novel Gaussian-Process-based learning from demonstration approach. This probabilistic representation allows to generalize over multiple demonstrations, and encode variability along the different phases of the task. In this paper, we address how Gaussian Processes can be used to effectively learn a policy from trajectories in task space. We also present a method to efficiently adapt the policy to fulfill new requirements, and to modulate the robot behavior as a function of task variability. This approach is illustrated through a real-world application using the TIAGo robot.