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pith:TPPT6AL5

pith:2025:TPPT6AL5A2DBU6JEE7XKTHLMYF

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BeyondMimic: From Motion Tracking to Versatile Humanoid Control via Guided Diffusion

C. Karen Liu, Guy Tevet, Koushil Sreenath, Qiayuan Liao, Takara E. Truong, Xiaoyu Huang, Yuman Gao

A compact motion-tracking setup plus classifier-guided latent diffusion lets one humanoid policy master diverse agile skills and solve unseen tasks zero-shot.

arxiv:2508.08241 v4 · 2025-08-11 · cs.RO

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Claims

C1strongest claim

A compact motion-tracking formulation enables mastering a wide range of radically agile behaviors, including aerial cartwheels, spin-kicks, flip-kicks, and sprinting, with a single setup and shared hyperparameters, while a unified latent diffusion model with classifier guidance solves downstream tasks never encountered during training and transfers zero-shot to real hardware.

C2weakest assumption

That classifier guidance during diffusion sampling can reliably steer toward novel objectives (motion inpainting, teleoperation, obstacle avoidance) while preserving motion naturalness and stability without task-specific retraining or post-hoc tuning that would undermine the single-setup claim.

C3one line summary

BeyondMimic combines compact motion tracking with a unified guided latent diffusion model to master diverse agile behaviors from human demos and solve unseen downstream tasks via test-time classifier guidance.

References

94 extracted · 94 resolved · 3 Pith anchors

[1] Kuindersma,et al., Optimization-based locomotion planning, estimation, and control design for the atlas humanoid robot.Autonomous robots40(3), 429–455 (2016) 2016

[2] P. M. Wensing,et al., Optimization-based control for dynamic legged robots.IEEE Transactions on Robotics40, 43–63 (2023) 2023

[3] Kajita,et al., Biped walking pattern generation by using preview control of zero- moment point, in2003 IEEE international conference on robotics and automation (Cat 2003

[4] J. Pratt, J. Carff, S. Drakunov, A. Goswami, Capture point: A step toward humanoid push recovery, in2006 6th IEEE-RAS international conference on humanoid robots(Ieee) (2006), pp. 200–207 2006

[5] R. Deits, R. Tedrake, Footstep planning on uneven terrain with mixed-integer convex opti- mization, in2014 IEEE-RAS international conference on humanoid robots(IEEE) (2014), pp. 279–286 2014

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Cited by

28 papers in Pith

Imagine2Real: Towards Zero-shot Humanoid-Object Interaction via Video Generative Priors

SONIC: Supersizing Motion Tracking for Natural Humanoid Whole-Body Control

HUSKY: Humanoid Skateboarding System via Physics-Aware Whole-Body Control

HEX: Humanoid-Aligned Experts for Cross-Embodiment Whole-Body Manipulation

NaP-Control: Navigating Diffusion Prior for Versatile and Fast Character Control

Receipt and verification

First computed	2026-05-17T23:38:49.825057Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

9bdf3f017d06861a792427eea99d6cc15b233fa797458d3770b70b71ecef86d9

Aliases

arxiv: 2508.08241 · arxiv_version: 2508.08241v4 · doi: 10.48550/arxiv.2508.08241 · pith_short_12: TPPT6AL5A2DB · pith_short_16: TPPT6AL5A2DBU6JE · pith_short_8: TPPT6AL5

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/TPPT6AL5A2DBU6JEE7XKTHLMYF \
  | 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: 9bdf3f017d06861a792427eea99d6cc15b233fa797458d3770b70b71ecef86d9

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "54665c33a379a25e9e298cbc2ff0f76341ba3d92229ff79f30706d0b57985440",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.RO",
    "submitted_at": "2025-08-11T17:55:26Z",
    "title_canon_sha256": "63a78279064fd2c1e1986f6a8723c1a1c68c03fe5dfe135871379a33e6f07a53"
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}