Table · dataset · 2022
A Real-Robot Dataset for Assessing Transferability of Learned Dynamics Models
Listed in Edmond
In the context of model-based reinforcement learning and control, a large number of methods for learning system dynamics have been proposed in recent years.
Description
The purpose of these learned models is to synthesize new control policies. An important open question is how robust current dynamics-learning methods are to shifts in the data distribution due to changes in the control policy.
We present a real-robot dataset which allows to systematically investigate this question. This dataset contains trajectories of a 3 degrees-of-freedom (DOF) robot being controlled by a diverse set of policies. Software to reproduce our benchmark of a few widely-used dynamics-learning methods using the proposed dataset is available in our code repository
Links
Where it is published
- Dataverse dataset page edmond.mpg.de/dataset.xhtml?persistentId=doi%3A10.17617%2F3.ZT6K7P ↗
landing page · from edmond mpdl mpg de
- DOI doi.org/10.17617/3.zt6k7p ↗
DOI / persistent id · from edmond mpdl mpg de
Catalogue records · 1
- Dataverse API edmond.mpg.de/api/datasets/:persistentId/?persistentId=doi%3A10.17617%2F3.ZT… ↗
metadata API · from edmond mpdl mpg de
Topics
- Stated by source
- Computer Science, Systems and Electrical Engineering · Computer Science, Systems and Electrical Engineering
- From keywords
- Astronomy & Astrophysics · Chemistry · Computer Science & AI · Computer Science & AI · Earth & Environmental Science · Economics & Finance · Engineering · Engineering · Humanities · Life Sciences · Materials Science · Medicine & Health · Ocean & Atmospheric Science · Physics · Social Science
- Inferred from text
- Machine learning 75%
Provenance · 2 source records, 24 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Edmond | doi:10.17617/3.ZT6K7P | 13 d ago | JSON v1 |
| Edmond | doi:10.17617/3.ZT6K7P | 11 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
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