# Constrained Dynamic Movement Primitives for Safe Learning of Motor Skills

arXiv: https://arxiv.org/abs/2209.14461 · HTML: https://mlsanity.com/paper/2209.14461/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 30 references that are in the map.
- First posted on arXiv 28 Sep 2022.

## Builds on (most cited first, 9 of 9)

- [Control Barrier Functions: Theory and Applications](https://mlsanity.com/paper/1903.11199/) (2019)
- [Control Barrier Function Based Quadratic Programs for Safety Critical Systems](https://mlsanity.com/paper/1609.06408/) (2016)
- [Dynamic Movement Primitives in Robotics: A Tutorial Survey](https://mlsanity.com/paper/2102.03861/) (2021)
- [PYROBOCOP : Python-based Robotic Control & Optimization Package for Manipulation and Collision Avoidance](https://mlsanity.com/paper/2106.03220/) (2021)
- [Imitation and Supervised Learning of Compliance for Robotic Assembly](https://mlsanity.com/paper/2111.10488/) (2021)
- [Learning Semantic Embedding Spaces for Slicing Vegetables](https://mlsanity.com/paper/1904.00303/) (2019)
- [Design of Adaptive Compliance Controllers for Safe Robotic Assembly](https://mlsanity.com/paper/2204.10447/) (2022)
- [Constraint Learning for Control Tasks with Limited Duration Barrier Functions](https://mlsanity.com/paper/1908.09506/) (2019)
- [Safety of Dynamical Systems with Multiple Non-Convex Unsafe Sets Using Control Barrier Functions](https://mlsanity.com/paper/2106.06330/) (2021)

## Built on by (most cited first, 2 of 2)

- [Style-transfer based Speech and Audio-visual Scene Understanding for Robot Action Sequence Acquisition from Videos](https://mlsanity.com/paper/2306.15644/) (2023)
- [Generalizable Human-Robot Collaborative Assembly Using Imitation Learning and Force Control](https://mlsanity.com/paper/2212.01434/) (2022)

## Abstract (arXiv)

Dynamic movement primitives are widely used for learning skills which can be demonstrated to a robot by a skilled human or controller. While their generalization capabilities and simple formulation make them very appealing to use, they possess no strong guarantees to satisfy operational safety constraints for a task. In this paper, we present constrained dynamic movement primitives (CDMP) which can allow for constraint satisfaction in the robot workspace. We present a formulation of a non-linear optimization to perturb the DMP forcing weights regressed by locally-weighted regression to admit a Zeroing Barrier Function (ZBF), which certifies workspace constraint satisfaction. We demonstrate the proposed CDMP under different constraints on the end-effector movement such as obstacle avoidance and workspace constraints on a physical robot. A video showing the implementation of the proposed algorithm using different manipulators in different environments could be found here https://youtu.be/hJegJJkJfys.