Kitchen¶
These datasets were generated with the FrankaKitchen-v1 environment, originally hosted in the D4RL[1] and relay-policy-learning[2] repository. The goal of the FrankaKitchen environment is to interact with the various objects in order to reach a desired state configuration. The objects you can interact with include the position of the kettle, flipping the light switch, opening and closing the microwave and cabinet doors, or sliding the other cabinet door. The desired goal configuration for all datasets is to complete 4 subtasks: open the microwave, move the kettle, flip the light switch, and slide open the cabinet door.
References¶
[1] Fu, Justin, et al. ‘D4RL: Datasets for Deep Data-Driven Reinforcement Learning’. CoRR, vol. abs/2004.07219, 2020, https://arxiv.org/abs/2004.07219.
[2] Gupta, A., Kumar, V., Lynch, C., Levine, S., & Hausman, K. (2019). Relay policy learning: Solving long-horizon tasks via imitation and reinforcement learning. arXiv preprint arXiv:1910.11956.
Content¶
ID |
Description |
|---|---|
The partial dataset includes other tasks being performed, but there are subtrajectories where the 4 target subtasks are completed in sequence. |
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The complete dataset includes demonstrations of all 4 target subtasks being completed, in order. |
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The mixed dataset contains various subtasks being performed, but the 4 target subtasks are never completed in sequence together. |