robotic-vt-grasp-project/Single-Step-Grasp-Refinement

Single-Step Grasp Refinement

This repository stages the public checkpoint for the single-step visual-tactile grasp refinement policy. The default release contains one evaluation-ready Full SGA-GSN model trained with the PPCT/SGA-GSN perception backbone.

License: MIT. The single-step RL code and staged model checkpoint are released under the same permissive license family as the AdaPoinTr-derived SGA-GSN code. Dataset, perception weights, and simulation assets remain separate dependencies with their own license terms.

Contents

checkpoints/full_sga_gsn_seed8_best.pt
configs/full_sga_gsn_seed8/configs/
metadata/
normalization/
ablations/
backbones/

checkpoints/full_sga_gsn_seed8_best.pt is an evaluation checkpoint derived from:

/rl-grasp-refine/outputs/exp_debug/seed8_rwd-grp-b_stb-rwd-5x_128-epi_paper-spec_latefus/checkpoints/best.pt

The public checkpoint keeps actor_critic, calibrator, experiment_cfg, object_split, and best-metric metadata. It omits optimizer and full training history, so it is intended for rollout/evaluation rather than exact training resume.

Default Model

• Release name: full_sga_gsn_seed8
• Best validation metric: validation/outcome/success_lift_vs_dataset = 0.109375
• Best iteration: 465
• Completed iterations: 466
• Split seed: 7
• Train objects: 000 to 074
• Validation objects: 078, 082, 085, 087
• Test objects: 075, 076, 077, 079, 080, 081, 083, 084, 086

The formal unseen-test summary used for this staging pass reports macro_success_lift_mean = 0.0969230769 for full-sga-gsn-seed8. See metadata/evaluation_metrics.json for the full table.

Required External Assets

This model repo does not include the perception weights, dataset, or simulator assets. A working rollout environment must provide:

• SGA-GSN perception code and weights:
  • ap_ps55.pth
  • PPCT/SGA-GSN ckpt-best.pth
  • matching PPCT/SGA-GSN config
• 3DA-VTG dataset restored to the path expected by the RL config.
• VT-Grasp simulation assets:
  • GraspNet VHACD object models
  • GSmini TACTO config/background
  • GSmini Panda hand assets
• The RL code and Docker environment that define scripts/evaluate_best_checkpoints.py, PyBullet, TACTO, and the environment wrappers.

The copied config snapshot preserves the original runtime paths used by the current container, including /AdaPoinTr and /Datasets/GraspNet-1billion/tactile-extended. If the public code release uses different mount points, update the config copy or provide path-compatible mounts.

Config Snapshot

The config snapshot is nested as:

configs/full_sga_gsn_seed8/configs/{experiment,env,perception,calibration,rl,model}/

This preserves compatibility with the RL loader, which resolves paths such as configs/env/grasp_refine_env_stb5x.yaml relative to a directory named configs.

For evaluation, copy or symlink this inner configs/ directory next to checkpoints/best.pt in an experiment directory:

my_eval_exp/
├── checkpoints/
│   └── best.pt
└── configs/
    ├── experiment/
    ├── env/
    ├── perception/
    ├── calibration/
    ├── rl/
    └── model/

Normalization

No separate observation normalization file was found in the current RL code or selected experiment. Action scaling is encoded in configs/full_sga_gsn_seed8/configs/env/grasp_refine_env_stb5x.yaml:

• translation_bound: [0.01, 0.01, 0.01]
• rotation_bound: [0.1, 0.1, 0.1]

Ablations

The minimal public release does not include ablation checkpoints. Learned ablation candidates are documented in ablations/README.md. no-action and rand-action baselines do not require model weights.

Checksums

Run checksum verification from the repository root:

sha256sum -c checksums.sha256