Search is not available for this dataset
action list | observation.state list | timestamp float32 0 24.9 ⌀ | frame_index int64 0 746 ⌀ | episode_index int64 0 9 ⌀ | index int64 0 4.64k ⌀ | task_index int64 0 0 ⌀ |
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End of preview. Expand in Data Studio
ACT V5: SO-101 Precision Pick-and-Place Baseline
Task
Pick up a red cube and put it in a box.
Hardware
- SO-101 leader-follower robot arm
- External RGB camera
- NVIDIA RTX 5060 Laptop GPU
Training data
20 high-quality teleoperated demonstrations. The cube starts at a fixed reference location A.
Training configuration
- Policy: ACT
- Training steps: 20,000
- Batch size: 8
- Input: RGB image and robot joint state
- Output: SO-101 joint action chunk
Evaluation
The policy was evaluated in 10 independent autonomous real-robot trials.
| Metric | Result |
|---|---|
| Successes | 8 / 10 |
| Success rate | 80% |
Result
This is the initial closed-loop imitation-learning baseline. The next stage adds a wrist camera, multiple cube positions, language instructions, and SmolVLA fine-tuning.
Limitations
The evaluation uses a fixed object position and a single external RGB camera. It does not yet test position, lighting, or language generalization.
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