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yjsm1203/
roco_model_act_2

Name: roco_model_act_2
Availability: InStock

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ACT Policy for Gearbox Assembly (Filtered Demos)

This model is an Action Chunking with Transformers (ACT) policy trained to perform gearbox assembly tasks. It was trained using behavior cloning on a combination of rocochallenge2025 and additional collected datasets.

Model Details

Model Type: ACT (Action Chunking with Transformers)
Policy Class: ACT
Backbone: ResNet-18
Training Dataset: Integrated dataset (rocochallenge2025 + temp_new_dataset) containing 241 episodes.
Episode Length: Fixed to 12,600 steps (padded/truncated) to handle variable length recordings.

Training Configuration

Task Name: sim_gearbox_assembly_demos_filtered
Batch Size: 32
Chunk Size (Action Horizon): 100
KL Weight: 10
Hidden Dimension: 512
Feedforward Dimension: 3200
Learning Rate: 1e-5
Num Epochs: ~9500 (Early stopped/Interrupted)
Seed: 0

Inputs and Outputs

Observations:
- head_rgb (240x320)
- left_hand_rgb (240x320)
- right_hand_rgb (240x320)
- qpos (14-dim joint positions)
Actions:
- 14-dim combined action vector (7-dim left arm + 7-dim right arm)

Usages

This model can be loaded using the ACTPolicy class. Ensure dataset_stats.pkl is loaded to normalize/unnormalize observations and actions correctly.

python

from policy import ACTPolicy
import pickle

# Load stats
with open('dataset_stats.pkl', 'rb') as f:
    stats = pickle.load(f)

# Load policy
policy = ACTPolicy(config)
policy.load_state_dict(torch.load('policy_best.ckpt'))
policy.cuda()
policy.eval()

yjsm1203/roco_model_act_2