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Check out the documentation for more information.

climbing-holds-rig-with-taxonomy

DP3-style point-cloud diffusion policy for grasping climbing holds with a Franka arm + LEAP hand, trained on the spring-testbed-era rig dataset (rlogh/climbing-holds-rig).

This is the taxonomy-conditioned model. The point cloud + robot state are fused with a 64-d grasp-type embedding (crimp / sloper / pinch / jug, one-hot) before being passed into the diffusion U-Net. At inference time the operator provides the grasp-type label.

The unconditioned counterpart (ablation) lives at rlogh/climbing-holds-rig-no-taxonomy.

Files

File Size Purpose
best.pt 415 MB EMA weights + optimizer + config dict (best training loss, epoch 2939)
norm_stats.json 2 KB Min-max normalization stats โ€” required by evaluate.py
training_status.md <1 KB Final loss + recent-epoch table
train.log ~170 KB Full epoch-by-epoch loss + LR log

Training summary

  • Dataset: 200 episodes (50 / grasp type), 24,621 valid samples
  • Epochs: 3000 (cosine LR decay after 500-step warmup)
  • Batch size: 128, AMP enabled
  • Best loss: 0.001670 (final 0.001760)
  • Wall time: 8.9 h on RTX 2080 Ti
  • Conditioning: PointNet(1024ร—3) โ†’ 256-d + State MLP โ†’ 128-d + GraspType MLP โ†’ 64-d โ†’ Fuse โ†’ 512-d
  • U-Net dims: (256, 512, 1024), 100-step cosine DDPM
  • State / action dim: 23 (Franka 7 + LEAP 16)
  • --no-grasp-conditioning flag: OFF (taxonomy conditioning ENABLED)

Usage (robot machine)

mkdir -p checkpoints/pc_with_taxonomy_rig
hf download rlogh/climbing-holds-rig-with-taxonomy \
    --local-dir checkpoints/pc_with_taxonomy_rig

python3 data_collection/evaluate.py \
    --checkpoint checkpoints/pc_with_taxonomy_rig/best.pt \
    --pull-dist 0.130 \
    --hold 1 --grasp-type crimp

evaluate.py autodetects the encoder type and conditioning state from the embedded config dict. --grasp-type is required and is fed into the model's grasp-type embedding branch.

Paired-evaluation result (spring testbed, 80 paired trials, 2026-05-15)

Grasp WITH median WITHOUT median ฮ” Wilcoxon p Cohen's d
Crimp 15.7 N 9.2 N +6.5 N 0.0007 1.05 ***
Jug 15.7 N 14.4 N +1.3 N 0.0278 0.55 *
Sloper 7.9 N 5.2 N +2.6 N 0.0012 1.08 **
Pinch 15.7 N 9.2 N +6.5 N <0.001 1.82 ***
Overall 13.1 N 7.9 N +5.2 N <0.001 0.95 ***

Primary metric: slip force from a parallel-spring linear ratchet testbed. Pull is a 13 cm impedance-controlled displacement (180ยฐ, toward robot base) after policy convergence. See repository README for full protocol.

Citation

Repository: github.com/rumilog/rock-climb

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