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rlogh/
climbing-holds-rig-with-taxonomy

Name: climbing-holds-rig-with-taxonomy
Availability: InStock

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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 — 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(1024×3) → 256-d + State MLP → 128-d + GraspType MLP → 64-d → Fuse → 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)

bash

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	Δ	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 (180°, toward robot base) after policy convergence. See repository README for full protocol.

Citation

Repository: github.com/rumilog/rock-climb