Datasets:
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action listlengths 6 6 ⌀ | observation.state listlengths 6 6 ⌀ | timestamp float32 0 15.8 ⌀ | frame_index int64 0 475 ⌀ | episode_index int64 0 152 ⌀ | index int64 0 44.6k ⌀ | task_index int64 0 12 ⌀ |
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End of preview. Expand in Data Studio
so101_eval1
Training dataset for Eval 1 of the LeMonkey project (ETH Robot Learning course, FS26): a SmolVLA policy that picks up a banana and places it in the bowl named by the prompt.
- 153 teleop episodes, 44,604 frames, 13 task strings.
- Source robot: SO-101 (6-DOF arm), wrist-mounted USB camera at 480x640 at 30 fps.
- Format: LeRobot v3 (
codebase_version: v3.0). - Aggregated from 6 per-color recording sessions:
- 118 behavior-cloning demos (blue / red / green, ~39 each)
- 35 HG-DAgger correction demos (blue / red / green) recorded against the behavior-cloning policy's failure positions
- The DAgger sources were normalized to the BC schema (rename
camera1->front, dropis_intervention) before aggregation.
Trained policy
The deployed Eval 1 policy is HBOrtiz/so101_smolvla_eval1:
SmolVLA-450M, 25k steps from lerobot/smolvla_base, image augmentation on.
Prompt templates
Put the banana in the blue colored bowl.
Put the banana in the red colored bowl.
Put the banana in the green colored bowl.
(+ paraphrases and DAgger correction prompts)
Repository
Project page and code: github.com/Ace3Z/LeMonkey
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