# Use the Force, Bot! -- Force-Aware ProDMP with Event-Based Replanning

arXiv: https://arxiv.org/abs/2409.11144 · HTML: https://mlsanity.com/paper/2409.11144/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 35 references that are in the map.
- First posted on arXiv 17 Sep 2024.

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

- [Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments](https://mlsanity.com/paper/2301.04195/) (2023)
- [FurnitureBench: Reproducible Real-World Benchmark for Long-Horizon Complex Manipulation](https://mlsanity.com/paper/2305.12821/) (2023)
- [Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapes](https://mlsanity.com/paper/2110.06192/) (2021)
- [Movement Primitive Diffusion: Learning Gentle Robotic Manipulation of Deformable Objects](https://mlsanity.com/paper/2312.10008/) (2023)
- [ProDMPs: A Unified Perspective on Dynamic and Probabilistic Movement Primitives](https://mlsanity.com/paper/2210.01531/) (2022)
- [Deep Black-Box Reinforcement Learning with Movement Primitives](https://mlsanity.com/paper/2210.09622/) (2022)
- [Interactive Imitation Learning of Bimanual Movement Primitives](https://mlsanity.com/paper/2210.16220/) (2022)
- [Robotic Imitation of Human Assembly Skills Using Hybrid Trajectory and Force Learning](https://mlsanity.com/paper/2103.05912/) (2021)
- [Curriculum-Based Imitation of Versatile Skills](https://mlsanity.com/paper/2304.05171/) (2023)

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

- [A Survey on Imitation Learning for Contact-Rich Tasks in Robotics](https://mlsanity.com/paper/2506.13498/) (2025)
- [DAM-VLA: Decoupled Asynchronous Multimodal Vision Language Action model](https://mlsanity.com/paper/2606.12105/) (2026)

## Abstract (arXiv)

Movement Primitives (MPs) are a well-established method for representing and generating modular robot trajectories. This work presents FA-ProDMP, a new approach which introduces force awareness to Probabilistic Dynamic Movement Primitives (ProDMP). FA-ProDMP adapts the trajectory during runtime to account for measured and desired forces. It offers smooth trajectories and captures position and force correlations over multiple trajectories, e.g. a set of human demonstrations. FA-ProDMP supports multiple axes of force and is thus agnostic to cartesian or joint space control. This makes FA-ProDMP a valuable tool for learning contact rich manipulation tasks such as polishing, cutting or industrial assembly from demonstration. In order to reliably evaluate FA-ProDMP, this work additionally introduces a modular, 3D printed task suite called POEMPEL, inspired by the popular Lego Technic pins. POEMPEL mimics industrial peg-in-hole assembly tasks with force requirements. It offers multiple parameters of adjustment, such as position, orientation and plug stiffness level, thus varying the direction and amount of required forces. Our experiments show that FA-ProDMP outperforms other MP formulations on the POEMPEL setup and a electrical power plug insertion task, due to its replanning capabilities based on the measured forces. These findings highlight how FA-ProDMP enhances the performance of robotic systems in contact-rich manipulation tasks.