ParticleSplat checkpoints

Checkpoints for ParticleSplat: Self-supervised Object-centric Latent Particle Splatting (project page, code). ParticleSplat decomposes a scene into a small set of latent particles from a few posed RGB-D views and renders them with feedforward 3D Gaussian splatting; the particles also serve as tokens for imitation learning.

Representation models

One model per benchmark. Every directory is a complete training run: config.yaml (merged configuration), config.json, train.log and checkpoints/model.pth.

Directory Setting Training config in the code repository
rlbench_multiview_particlesplat/ RLBench, 2 × RGB-D context views, all ten tasks configs/experiments/rlbench_multiview_particlesplat.yaml
rlbench_singleview_particlesplat/ RLBench, 1 × RGB-D context view (front camera), all ten tasks configs/experiments/rlbench_singleview_particlesplat.yaml
mimicgen_particlesplat/ MimicGen, 2 × RGB-D policy cameras, all twelve tasks configs/experiments/mimicgen_particlesplat.yaml
huggingface-cli download lyuxinghe/particlesplat --include "rlbench_multiview_particlesplat/*" --local-dir checkpoints

Policies

One EC-Diffuser policy per task (200k steps) on frozen ParticleSplat tokens, under policy/rlbench/<task>/ and policy/mimicgen/<task>/. Each directory holds ckpt/state_399_step200000.pt, args.json, the EC-Diffuser *_config.pkl files and diff.txt. Training and rollout evaluation are documented on the policy-learning branch of the code repository.

huggingface-cli download lyuxinghe/particlesplat --include "policy/rlbench/close_jar/*" --local-dir checkpoints

Data

The code is released under the MIT license.

Downloads last month

-

Downloads are not tracked for this model. How to track
Video Preview
loading