# Merging Position and Orientation Motion Primitives

arXiv: https://arxiv.org/abs/2003.11507 · HTML: https://mlsanity.com/paper/2003.11507/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2020.

## Built on by (most cited first, 12 of 12)

- [Continual Learning from Demonstration of Robotics Skills](https://mlsanity.com/paper/2202.06843/) (2022)
- [Orientation Probabilistic Movement Primitives on Riemannian Manifolds](https://mlsanity.com/paper/2110.15036/) (2021)
- [Hierarchical Policy Blending as Inference for Reactive Robot Control](https://mlsanity.com/paper/2210.07890/) (2022)
- [Scalable and Efficient Continual Learning from Demonstration via a Hypernetwork-generated Stable Dynamics Model](https://mlsanity.com/paper/2311.03600/) (2023)
- [Hierarchical Policy Blending As Optimal Transport](https://mlsanity.com/paper/2212.01938/) (2022)
- [Fusion Dynamical Systems with Machine Learning in Imitation Learning: A Comprehensive Overview](https://mlsanity.com/paper/2403.19916/) (2024)
- [Periodic DMP formulation for Quaternion Trajectories](https://mlsanity.com/paper/2110.10510/) (2021)
- [Logic Learning from Demonstrations for Multi-step Manipulation Tasks in Dynamic Environments](https://mlsanity.com/paper/2404.16138/) (2024)
- [A Non-parametric Skill Representation with Soft Null Space Projectors for Fast Generalization](https://mlsanity.com/paper/2209.08522/) (2022)
- [Non-parametric regression for robot learning on manifolds](https://mlsanity.com/paper/2310.19561/) (2023)
- [Imitation Learning-based Direct Visual Servoing using the Large Projection Formulation](https://mlsanity.com/paper/2406.09120/) (2024)
- [Screw Geometry Meets Bandits: Incremental Acquisition of Demonstrations to Generate Manipulation Plans](https://mlsanity.com/paper/2410.18275/) (2024)

## Abstract (arXiv)

In this paper, we focus on generating complex robotic trajectories by merging sequential motion primitives. A robotic trajectory is a time series of positions and orientations ending at a desired target. Hence, we first discuss the generation of converging pose trajectories via dynamical systems, providing a rigorous stability analysis. Then, we present approaches to merge motion primitives which represent both the position and the orientation part of the motion. Developed approaches preserve the shape of each learned movement and allow for continuous transitions among succeeding motion primitives. Presented methodologies are theoretically described and experimentally evaluated, showing that it is possible to generate a smooth pose trajectory out of multiple motion primitives.