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davidlinjiahao/
lerobot_so101_base_sim_pickplace

Name: lerobot_so101_base_sim_pickplace
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SO-101 Pick & Place - 50K Checkpoint

This is a 50,000-step checkpoint of an ACT (Action Chunking Transformer) policy trained for pick-and-place tasks using the SO-101 robot arm in MuJoCo simulation.

📊 Training Details

Training Steps: 50,000
Loss: 0.089 (down from 4.4 at start)
Dataset: 105 human demonstrations (teleoperation)
Policy Type: ACT (Action Chunking Transformer)
Framework: LeRobot
Simulator: MuJoCo
Device: Apple Silicon (MPS)

🎯 Performance

Evaluation Results (3 episodes):

Success Rate: 66.7% (2/3 episodes)
Average Steps to Success: ~360 steps
Task: Pick cube from table and place in bin

Successful Episodes:

Episode 1: ✅ Success in 300 steps
Episode 2: ❌ Timeout at 500 steps
Episode 3: ✅ Success in 424 steps

🤖 Model Architecture

Backbone: ResNet18 for visual encoding
Input:
- Robot state (12D): joint positions (6), cube position (3), cube-to-bin vector (3)
- Front camera image (480x640 RGB)
Output: 6D joint position commands
Parameters: ~52M

📦 Dataset

Trained on davidlinjiahao/lerobot_batch_001:

105 teleoperation demonstrations
Recorded using SO-101 leader arm
Task: Pick and place cube into bin
Video + state observations at 30 Hz

🚀 Usage

With LeRobot

python

from lerobot.policies.act.modeling_act import ACTPolicy
import torch

# Load checkpoint
policy = ACTPolicy.from_pretrained("davidlinjiahao/so101_50k_checkpoint")
policy.eval()
policy.to("cuda")  # or "mps" for Apple Silicon

# Get action
observation = {
    "observation.state": state_tensor,
    "observation.images.front": image_tensor
}

with torch.no_grad():
    action = policy.select_action(observation)

Evaluation in MuJoCo

bash

mjpython scripts/deploy.py \
    --policy-path davidlinjiahao/so101_50k_checkpoint \
    --episodes 10 \
    --render \
    --hz 10

📈 Training Progress

This is a mid-training checkpoint. Training continues to 200K steps with checkpoints every 1,000 steps:

✅ 50K: Current checkpoint (66.7% success)
🎯 100K: Target evaluation point
🏁 200K: Final checkpoint (expected >90% success)

🔗 Related

Dataset: davidlinjiahao/lerobot_batch_001
Final Model: davidlinjiahao/so101_overnight (training in progress)
Framework: LeRobot

📝 Citation

bibtex

@misc{so101_50k_checkpoint,
  author = {David Lin},
  title = {SO-101 Pick & Place - 50K Checkpoint},
  year = {2025},
  publisher = {HuggingFace Hub},
  howpublished = {\url{https://huggingface.co/davidlinjiahao/so101_50k_checkpoint}}
}

📄 License

Apache 2.0

davidlinjiahao/lerobot_so101_base_sim_pickplace