Neural Dynamic Movement Primitives -- a survey
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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.
Key facts. Cited by 1 paper 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
3 of 40 references.
References · 3
- 2019Benchmark for Skill Learning from Demonstration: Impact of User Experience, Task Complexity, and Start Configuration on Performancecited by 7
- 2020Reward Conditioned Neural Movement Primitives for Population Based Variational Policy Optimizationcited by 2
- 2016Kick Motions for the NAO Robot using Dynamic Movement Primitivescited by 1
Built on by
Citing papers per year: 2022: 1.
1 paper.