TronCamp Mani ACT Checkpoints
T1-T4 ACT inference checkpoints for the 2026 TronCamp Mani track on the Tron2 dual-arm robot. The models were trained on TronCamp Mani Expert Demonstrations using the code in the GitHub repository.
The models use three RGB cameras (cam_high, cam_right_wrist, cam_left_wrist) and 16-D qpos; they do not use depth, point clouds, end-effector poses, or privileged object state.
Model Summary
| Track | Task | Epoch | Online result |
|---|---|---|---|
| T1 | adjust_bottle | best in 6000-epoch run | 0.59 SR |
| T2 | grab_roller | 2000 | 0.89 SR |
| T3 | stack_bowls_two | 4000 | 0.56 SR |
| T4 | stack_bowls_three | 8000 | 73.0 /100 |
Each task directory contains a normalized policy_last.ckpt and its matching dataset_stats.pkl.
Final deployment uses temporal_agg: false. See manifest.json for the exact dataset and code mapping.
Architecture and Training
All four checkpoints use the same ACT configuration:
| Parameter | Value |
|---|---|
| Visual backbone | ResNet-18 |
| Cameras | 3 RGB views |
| State/action dimension | 16 / 16 |
| Chunk size | 50 |
| Hidden/feed-forward dimension | 512 / 3200 |
| Encoder/decoder layers | 4 / 7 |
| Attention heads | 8 |
| Batch size | 8 |
| Learning rate | 1e-5 |
| Weight decay | 1e-4 |
| KL weight | 10 |
The checkpoints were trained with the unmodified official ACT action/time alignment loader. Final deployment uses temporal_agg: false.
Repository Layout
checkpoints/
├── T1-adjust_bottle/{policy_last.ckpt,dataset_stats.pkl}
├── T2-grab_roller/{policy_last.ckpt,dataset_stats.pkl}
├── T3-stack_bowls_two/{policy_last.ckpt,dataset_stats.pkl}
└── T4-stack_bowls_three/{policy_last.ckpt,dataset_stats.pkl}
deploy_policy.yml
manifest.json
checksums.sha256
policy_last.ckpt is a normalized distribution filename. It does not imply that the selected checkpoint was the last epoch of the source run. manifest.json records the source task, dataset setting, selected epoch, SHA256, deployment setting, and online result.
Checkpoint Selection
- T1: original-loader
policy_best.ckptfrom the 6000-epoch run. - T2: epoch 2000 trained on 993 curated demonstrations.
- T3: epoch 4000 trained on the first 1000-episode symmetric-contact dataset.
- T4: epoch 8000 from the from-scratch run. The lowest validation-loss checkpoint near epoch 6514 and the epoch 9000/10000 checkpoints did not improve the 73.0 online score.
Each checkpoint must be used with the dataset_stats.pkl in the same directory. Statistics from another task or data version are incompatible.
Evaluation
Download the repository and point the local evaluator to each task directory:
hf download han-xudong/troncamp-mani-act \
--local-dir troncamp-mani-act
cd troncamp-mani-act
sha256sum -c checksums.sha256
cd /path/to/troncamp-mani
python starter/eval_local.py --track T1 --ckpt-dir /path/to/troncamp-mani-act/checkpoints/T1-adjust_bottle
python starter/eval_local.py --track T2 --ckpt-dir /path/to/troncamp-mani-act/checkpoints/T2-grab_roller
python starter/eval_local.py --track T3 --ckpt-dir /path/to/troncamp-mani-act/checkpoints/T3-stack_bowls_two
python starter/eval_local.py --track T4 --ckpt-dir /path/to/troncamp-mani-act/checkpoints/T4-stack_bowls_three
The public local seeds and private online seeds follow the same task distribution but are not identical. Exact leaderboard scores are therefore not guaranteed on a local rerun.
Intended Use and Limitations
These models are intended for competition reproduction and research in the matching RoboTwin/Tron2 simulation. They have not been validated on physical hardware, under different camera calibration, or with modified task assets. Long-horizon bowl stacking remains sensitive to compounding action error and wrist-camera occlusion.
Data, Code, and Integrity
Dataset: https://huggingface.co/datasets/han-xudong/troncamp-mani-dataset
Source code: https://github.com/han-xudong/troncamp-mani
checksums.sha256 covers all published inference weights and matching dataset statistics. manifest.json binds them to the Dataset revision and GitHub release commit.
License and Attribution
The top-level source license does not relicense bundled third-party components or proprietary Tron2 assets. Review the source repository LICENSE and NOTICE before redistribution. The checkpoints are simulation-trained research artifacts and carry no real-robot safety warranty.