The Mini Wheelbot Dataset: High-Fidelity Data for Robot Learning
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908770788966400 |
|---|---|
| author | Hose, Henrik Brunzema, Paul Subhasish, Devdutt Trimpe, Sebastian |
| author_facet | Hose, Henrik Brunzema, Paul Subhasish, Devdutt Trimpe, Sebastian |
| contents | The development of robust learning-based control algorithms for unstable systems requires high-quality, real-world data, yet access to specialized robotic hardware remains a significant barrier for many researchers. This paper introduces a comprehensive dynamics dataset for the Mini Wheelbot, an open-source, quasi-symmetric balancing reaction wheel unicycle. The dataset provides 1 kHz synchronized data encompassing all onboard sensor readings, state estimates, ground-truth poses from a motion capture system, and third-person video logs. To ensure data diversity, we include experiments across multiple hardware instances and surfaces using various control paradigms, including pseudo-random binary excitation, nonlinear model predictive control, and reinforcement learning agents. We include several example applications in dynamics model learning, state estimation, and time-series classification to illustrate common robotics algorithms that can be benchmarked on our dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_11394 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | The Mini Wheelbot Dataset: High-Fidelity Data for Robot Learning Hose, Henrik Brunzema, Paul Subhasish, Devdutt Trimpe, Sebastian Robotics Systems and Control The development of robust learning-based control algorithms for unstable systems requires high-quality, real-world data, yet access to specialized robotic hardware remains a significant barrier for many researchers. This paper introduces a comprehensive dynamics dataset for the Mini Wheelbot, an open-source, quasi-symmetric balancing reaction wheel unicycle. The dataset provides 1 kHz synchronized data encompassing all onboard sensor readings, state estimates, ground-truth poses from a motion capture system, and third-person video logs. To ensure data diversity, we include experiments across multiple hardware instances and surfaces using various control paradigms, including pseudo-random binary excitation, nonlinear model predictive control, and reinforcement learning agents. We include several example applications in dynamics model learning, state estimation, and time-series classification to illustrate common robotics algorithms that can be benchmarked on our dataset. |
| title | The Mini Wheelbot Dataset: High-Fidelity Data for Robot Learning |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2601.11394 |