# Residual Robot Learning for Object-Centric Probabilistic Movement Primitives

arXiv: https://arxiv.org/abs/2203.03918 · HTML: https://mlsanity.com/paper/2203.03918/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2022.

## Built on by (most cited first, 5 of 5)

- [ASAP: Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body Skills](https://mlsanity.com/paper/2502.01143/) (2025)
- [From Imitation to Refinement -- Residual RL for Precise Assembly](https://mlsanity.com/paper/2407.16677/) (2024)
- [RFS: Reinforcement Learning with Residual Flow Steering for Dexterous Manipulation](https://mlsanity.com/paper/2602.01789/) (2026)
- [Efficient Skill Acquisition for Complex Manipulation Tasks in Obstructed Environments](https://mlsanity.com/paper/2303.03365/) (2023)
- [AnchorRefine: Synergy-Manipulation Based on Trajectory Anchor and Residual Refinement for Vision-Language-Action Models](https://mlsanity.com/paper/2604.17787/) (2026)

## Abstract (arXiv)

It is desirable for future robots to quickly learn new tasks and adapt learned skills to constantly changing environments. To this end, Probabilistic Movement Primitives (ProMPs) have shown to be a promising framework to learn generalizable trajectory generators from distributions over demonstrated trajectories. However, in practical applications that require high precision in the manipulation of objects, the accuracy of ProMPs is often insufficient, in particular when they are learned in cartesian space from external observations and executed with limited controller gains. Therefore, we propose to combine ProMPs with recently introduced Residual Reinforcement Learning (RRL), to account for both, corrections in position and orientation during task execution. In particular, we learn a residual on top of a nominal ProMP trajectory with Soft-Actor Critic and incorporate the variability in the demonstrations as a decision variable to reduce the search space for RRL. As a proof of concept, we evaluate our proposed method on a 3D block insertion task with a 7-DoF Franka Emika Panda robot. Experimental results show that the robot successfully learns to complete the insertion which was not possible before with using basic ProMPs.