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aic-rlt-cable-insertion
siyulw2025/aic-rlt-cable-insertion
aic-rlt-cable-insertion is a machine learning model from siyulw2025. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
RLT (RL Token) checkpoint for the AIC cable insertion challenge.
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Updated Apr 12, 2026
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.pt234 MB · 100%
At a glance
- Access
- Public
- Created
- Apr 12, 2026
- Updated
- Apr 12, 2026
- SHA
- 75db95a9
Model card
From the Hugging Face model README
AIC RLT — SFP Cable Insertion
RLT (RL Token) checkpoint for the AIC cable insertion challenge.
What's inside
| File | Description |
|---|---|
phase1_rl_token.pt | Phase 1: RL token encoder pretrained on XVLA (lerobot/xvla-base) embeddings extracted from 100 synthetic demo episodes. Loss: 0.65 → 0.024 over 50 epochs. |
phase2_offline.pt | Phase 2: Actor + Critic trained offline via TD3+BC on 13,100 demo transitions with synthetic distance-to-goal rewards. 5,100 gradient steps. |
Architecture
- RL token encoder: Perceiver-style cross-attention, compresses (115, 1024) XVLA embeddings → 2048-dim z_rl
- Actor: 3-layer MLP (256→256→action), outputs action chunks (C=10, 7-dim TCP)
- Critic: Twin Q-networks for TD3-style pessimistic value estimation
Training data
- 100 synthetic episodes of scripted cable insertion (
generate_synthetic_data.py) - XVLA embeddings extracted from Florence-2 encoder (lerobot/xvla-base)
Usage (ROS 2 simulation)
pixi run ros2 run aic_model aic_model \
--ros-args \
-p use_sim_time:=true \
-p policy:=aic_example_policies.ros.RunRLT \
-p policy_args.checkpoint_path:=$(hf_hub_download siyulw2025/aic-rlt-cable-insertion phase2_offline.pt)
See aic_example_policies/ros/RunRLT.py for the full inference policy.
- Created
- Apr 12, 2026
- Updated
- Apr 12, 2026