Incremental Few-Shot Adaptation for Non-Prehensile Object Manipulation using Parallelizable Physics Simulators
Few-shot adaptation is an important capability for intelligent robots that perform tasks in open-world settings such as everyday environments or flexible production. In this paper, we propose a novel approach for non-prehensile manipulation which iteratively adapts a physics-based dynamics model for model-predictive control. We adapt the parameters of the model incrementally with a few examples of robot-object interactions. This is achieved by sampling-based optimization of the parameters using a parallelizable rigid-body physics simulation as dynamic world model. In turn, the optimized dynamics model can be used for model-predictive control using efficient sampling-based optimization. We evaluate our few-shot adaptation approach in several object pushing experiments in simulation and with a real robot.
| Author(s): | Baumeister, Fabian and Mack, Lukas and Stueckler, Joerg |
| Book Title: | CoRR abs/2409.13228 |
| Year: | 2024 |
| BibTeX Type: | Technical Report (techreport) |
| Electronic Archiving: | grant_archive |
| Institution: | CoRR |
| Note: | Submitted to IEEE International Conference on Robotics and Automation (ICRA) 2025 |
| State: | Submitted |
| URL: | https://arxiv.org/abs/2409.13228 |
BibTeX
@techreport{baumeister2024mpcsim,
title = {Incremental Few-Shot Adaptation for Non-Prehensile Object Manipulation using Parallelizable Physics Simulators},
booktitle = {CoRR abs/2409.13228},
abstract = {Few-shot adaptation is an important capability for intelligent robots that perform tasks in open-world settings such as everyday environments or flexible production. In this paper, we propose a novel approach for non-prehensile manipulation which iteratively adapts a physics-based dynamics model for model-predictive control. We adapt the parameters of the model incrementally with a few examples of robot-object interactions. This is achieved by sampling-based optimization of the parameters using a parallelizable rigid-body physics simulation as dynamic world model. In turn, the optimized dynamics model can be used for model-predictive control using efficient sampling-based optimization. We evaluate our few-shot adaptation approach in several object pushing experiments in simulation and with a real robot. },
institution = {CoRR},
year = {2024},
note = {Submitted to IEEE International Conference on Robotics and Automation (ICRA) 2025},
author = {Baumeister, Fabian and Mack, Lukas and Stueckler, Joerg},
url = {https://arxiv.org/abs/2409.13228}
}