# Neural Dynamic Movement Primitives -- a survey

arXiv: https://arxiv.org/abs/2208.01903 · HTML: https://mlsanity.com/paper/2208.01903/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 40 references that are in the map.
- First posted on arXiv 3 Aug 2022.

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

- [Benchmark for Skill Learning from Demonstration: Impact of User Experience, Task Complexity, and Start Configuration on Performance](https://mlsanity.com/paper/1911.02725/) (2019)
- [Reward Conditioned Neural Movement Primitives for Population Based Variational Policy Optimization](https://mlsanity.com/paper/2011.04282/) (2020)
- [Kick Motions for the NAO Robot using Dynamic Movement Primitives](https://mlsanity.com/paper/1606.00600/) (2016)

## Built on by (most cited first, 1 of 1)

- [Bimanual rope manipulation skill synthesis through context dependent correction policy learning from human demonstration](https://mlsanity.com/paper/2209.13850/) (2022)

## Abstract (arXiv)

One of the most important challenges in robotics is producing accurate trajectories and controlling their dynamic parameters so that the robots can perform different tasks. The ability to provide such motion control is closely related to how such movements are encoded. Advances on deep learning have had a strong repercussion in the development of novel approaches for Dynamic Movement Primitives. In this work, we survey scientific literature related to Neural Dynamic Movement Primitives, to complement existing surveys on Dynamic Movement Primitives.