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TriFinger RL Datasets
TriFinger RL Datasets
May 19, 2023
Gürtler, Nico; Blaes, Sebastian; Kolev, Pavel; Widmaier, Felix; Wuthrich, Manuel; Bauer, Stefan; Schölkopf, Bernhard; Martius, Georg, 2023, "TriFinger RL Datasets", https://doi.org/10.17617/3.DXZ7TL, Edmond, V1
Offline reinforcement learning datasets collected on real TriFinger robots and with a simulated version of the robot. Trajectories for two dexterous manipulation tasks were collected: Pushing a cube to a goal position on the ground and Lifting it to a goal position and orientatio...
Data of long-term experiment using a robot actuated by pneumatic muscles
Apr 27, 2023
Simon Guist; Jan Schneider; Hao Ma; Vincent Berenz; Julian Martus; Felix Grüninger; Michael Mühlebach; Jonathan Fiene; Bernhard Schölkopf; Dieter Büchler, 2023, "Data of long-term experiment using a robot actuated by pneumatic muscles", https://doi.org/10.17617/3.OMM0JP, Edmond, V1
This dataset offers a compilation of long-term dynamic motion data gathered over approximately 3.5 weeks from a newly designed 4-DoF tendon-driven robotic arm powered by pneumatic artificial muscles (PAMs). The data encompasses movements generated by random multisine signals of t...
A Real-Robot Dataset for Assessing Transferability of Learned Dynamics Models
Jul 27, 2022
Agudelo-España, Diego; Zadaianchuk, Andrii; Wenk, Philipp; Garg, Aditya; Akpo, Joel Bessekon; Grimminger, Felix; Viereck, Julian; Naveau, Maximilien; Righetti, Ludovic ‬; Martius, Georg; Krause, Andreas; Schölkopf, Bernhard; Bauer, Stefan; Wüthrich, Manuel, 2022, "A Real-Robot Dataset for Assessing Transferability of Learned Dynamics Models", https://doi.org/10.17617/3.ZT6K7P, Edmond, V1
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. The purpose of these learned models is to synthesize new control policies. An important open question is how robust current...
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