Pith Number

pith:YN5B5WVA

pith:2024:YN5B5WVAMMGQXJPXQUYQ6VVC5W

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3D Diffuser Actor: Policy Diffusion with 3D Scene Representations

Katerina Fragkiadaki, Nikolaos Gkanatsios, Tsung-Wei Ke

A diffusion policy that denoises 3D robot pose trajectories from tokenized scene features, language, and proprioception sets new performance records on standard robot benchmarks.

arxiv:2402.10885 v3 · 2024-02-16 · cs.RO · cs.AI · cs.CV · cs.LG

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4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

3D Diffuser Actor sets a new state-of-the-art on RLBench with an absolute performance gain of 18.1% over the current SOTA on a multi-view setup and an absolute gain of 13.1% on a single-view setup. On the CALVIN benchmark, it improves over the current SOTA by a 9% relative increase.

C2weakest assumption

That the 3D scene features aggregated from depth images remain sufficiently accurate and viewpoint-invariant when camera placement or lighting changes in ways not seen during training.

C3one line summary

3D Diffuser Actor unifies diffusion policies with 3D scene features to set new state-of-the-art results on RLBench and CALVIN robot benchmarks.

References

120 extracted · 120 resolved · 16 Pith anchors

[1] Generative adversarial imitation learning 2016 · arXiv:1606.03476

[2] Y . Tsurumine and T. Matsubara. Goal-aware generative adversarial imitation learning from imperfect demonstration for robotic cloth manipulation, 2022 2022

[4] Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets · arXiv:1705.10479

[5] N. M. M. Shafiullah, Z. J. Cui, A. Altanzaya, and L. Pinto. Behavior transformers: Cloning k modes with one stone, 2022 2022

[6] T. Pearce, T. Rashid, A. Kanervisto, D. Bignell, M. Sun, R. Georgescu, S. V . Macua, S. Z. Tan, I. Momennejad, K. Hofmann, and S. Devlin. Imitating human behaviour with diffusion models, 2023 2023

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Cited by

26 papers in Pith

GAF: Gaussian Action Field as a 4D Representation for Dynamic World Modeling in Robotic Manipulation

A Survey on Vision-Language-Action Models for Embodied AI

FUNCanon: Learning Pose-Aware Action Primitives via Functional Object Canonicalization for Generalizable Robotic Manipulation

DISC: Decoupling Instruction from State-Conditioned Control via Policy Generation

Learning Structural Latent Points for Efficient Visual Representations in Robotic Manipulation

Receipt and verification

First computed	2026-05-17T23:38:12.919758Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

c37a1edaa0630d0ba5f785310f56a2edacfb47d547c81c6bb6bb843d8a586954

Aliases

arxiv: 2402.10885 · arxiv_version: 2402.10885v3 · doi: 10.48550/arxiv.2402.10885 · pith_short_12: YN5B5WVAMMGQ · pith_short_16: YN5B5WVAMMGQXJPX · pith_short_8: YN5B5WVA

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/YN5B5WVAMMGQXJPXQUYQ6VVC5W \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: c37a1edaa0630d0ba5f785310f56a2edacfb47d547c81c6bb6bb843d8a586954

Canonical record JSON

{
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    "cross_cats_sorted": [
      "cs.AI",
      "cs.CV",
      "cs.LG"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
    "primary_cat": "cs.RO",
    "submitted_at": "2024-02-16T18:43:02Z",
    "title_canon_sha256": "af756eca498b5c4efcbd0b56e1ba0bd8267b043217f346ce988806667ee0678d"
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  "source": {
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    "kind": "arxiv",
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  }
}