# Learning Task-Parameterized Skills from Few Demonstrations

arXiv: https://arxiv.org/abs/2201.09975 · HTML: https://mlsanity.com/paper/2201.09975/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2022.

## Built on by (most cited first, 8 of 8)

- [Task Generalization with Stability Guarantees via Elastic Dynamical System Motion Policies](https://mlsanity.com/paper/2309.01884/) (2023)
- [Wasserstein Gradient Flows for Optimizing Gaussian Mixture Policies](https://mlsanity.com/paper/2305.10411/) (2023)
- [Interactive incremental learning of generalizable skills with local trajectory modulation](https://mlsanity.com/paper/2409.05655/) (2024)
- [Robot Deformable Object Manipulation via NMPC-generated Demonstrations in Deep Reinforcement Learning](https://mlsanity.com/paper/2502.11375/) (2025)
- [Task-agnostic Lifelong Robot Learning with Retrieval-based Weighted Local Adaptation](https://mlsanity.com/paper/2410.02995/) (2024)
- [TReF-6: Inferring Task-Relevant Frames from a Single Demonstration for One-Shot Skill Generalization](https://mlsanity.com/paper/2509.00310/) (2025)
- [CLASP: Language-Driven Robot Skill Selection and Composition using Task-Parameterized Learning](https://mlsanity.com/paper/2606.08169/) (2026)
- [Dressing in Motion: A Human Motion-Aware Diffusion Policy for Robot-Assisted Dressing](https://mlsanity.com/paper/2609.04759/) (2026)

## Abstract (arXiv)

Moving away from repetitive tasks, robots nowadays demand versatile skills that adapt to different situations. Task-parameterized learning improves the generalization of motion policies by encoding relevant contextual information in the task parameters, hence enabling flexible task executions. However, training such a policy often requires collecting multiple demonstrations in different situations. To comprehensively create different situations is non-trivial thus renders the method less applicable to real-world problems. Therefore, training with fewer demonstrations/situations is desirable. This paper presents a novel concept to augment the original training dataset with synthetic data for policy improvements, thus allows learning task-parameterized skills with few demonstrations.