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Pretrained whole-body tracking models transfer across humanoid embodiments using 1 percent of normal training resources.

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T0 review · grok-4.3

2026-06-30 16:15 UTC pith:PO4F2ZR5

load-bearing objection Any2Any gets decent transfer results on humanoid WBT with low resources, but the kinematic alignment step lacks the ablation needed to show it actually enables policy reuse. the 1 major comments →

arxiv 2605.23733 v3 pith:PO4F2ZR5 submitted 2026-05-22 cs.RO cs.AI

Any2Any: Efficient Cross-Embodiment Transfer for Humanoid Whole-Body Tracking

Ming Yang , Tao Yu , Feng Li , Hua Chen This is my paper
classification cs.RO cs.AI
keywords cross-embodiment transferhumanoid whole-body trackingpolicy transferparameter-efficient fine-tuningkinematic alignmentdynamics adaptation
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that whole-body tracking policies pretrained on one humanoid robot can be efficiently adapted to new robot bodies. It shows this by introducing a method that first aligns the kinematics of the source and target robots so the policy can be reused, then fine-tunes only selected parts with minimal data. This matters because full retraining for each new platform is expensive in data and compute. If true, it opens a way to reuse existing models rather than starting over for every humanoid variant.

Core claim

Any2Any transfers an existing whole-body tracking specialist to a new humanoid embodiment by first performing kinematic alignment to align input and output spaces, allowing reuse of the pretrained policy, and then applying lightweight parameter-efficient fine-tuning to dynamics-sensitive modules. This achieves competitive tracking performance while using only a small fraction of the data and compute required for training from scratch, as demonstrated by transferring models from Unitree G1 to LimX Oli and LimX Luna with 1% of the resources.

What carries the argument

The Any2Any two-stage process of kinematic alignment followed by targeted parameter-efficient fine-tuning on dynamics modules, which reuses behavioral priors from the source policy.

Load-bearing premise

That performing kinematic alignment between source and target humanoids sufficiently aligns their input and output spaces so the pretrained source policy can be meaningfully reused before dynamics adaptation begins.

What would settle it

If applying Any2Any to transfer a model resulted in tracking performance no better than a randomly initialized policy even after the dynamics adaptation stage, the efficiency claim would fail.

Watch this falsifier — get emailed when new claim-graph text bears on it.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

1 major / 0 minor

Summary. The paper proposes Any2Any, a paradigm for efficient cross-embodiment transfer of pretrained whole-body tracking (WBT) models across humanoid robots. It first performs kinematic alignment to map source (e.g., Unitree G1) and target (e.g., LimX Oli, Luna) input/output spaces, enabling reuse of the source policy, then applies lightweight PEFT modules to dynamics-sensitive components for adaptation. Experiments claim that this achieves competitive or superior tracking performance using only 1% of the compute and data required for training from scratch.

Significance. If validated, the result would be significant for humanoid robotics by demonstrating that WBT specialists can be reused across embodiments with minimal resources, lowering barriers to deployment on new platforms and providing a scalable alternative to per-robot full retraining.

major comments (1)
  1. [Abstract] Abstract: The central efficiency claim (1% compute/data transfer) rests on the assertion that kinematic alignment 'aligns their input and output spaces so that the pretrained source policy can be meaningfully reused' before PEFT begins. No ablation, zero-shot metrics, or early-adaptation performance comparison (with vs. without the alignment step) is described to isolate this contribution from the PEFT modules alone.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the detailed feedback. We address the concern regarding isolation of the kinematic alignment contribution below and will revise the manuscript to strengthen this aspect of the presentation.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central efficiency claim (1% compute/data transfer) rests on the assertion that kinematic alignment 'aligns their input and output spaces so that the pretrained source policy can be meaningfully reused' before PEFT begins. No ablation, zero-shot metrics, or early-adaptation performance comparison (with vs. without the alignment step) is described to isolate this contribution from the PEFT modules alone.

    Authors: We agree that an explicit ablation isolating the kinematic alignment step would strengthen the paper. The current experiments demonstrate overall efficiency gains from the full Any2Any pipeline, but we will add the requested analysis in revision: zero-shot tracking metrics immediately after kinematic alignment (prior to any PEFT), learning curves for early adaptation stages, and direct comparisons of PEFT performance with versus without the alignment preprocessing. This will quantify how alignment enables reuse of the source policy as a meaningful initialization. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical transfer results with no self-referential derivations

full rationale

The paper proposes Any2Any as a practical transfer method (kinematic alignment + PEFT) and supports its efficiency claims solely through experimental outcomes on G1-to-LimX transfers. No equations, fitted parameters renamed as predictions, or load-bearing self-citations appear in the provided text. The derivation chain consists of method description followed by independent empirical benchmarks rather than any reduction of outputs to inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract-only review provides no explicit free parameters, axioms, or invented entities; the central claim rests on unstated assumptions about the sufficiency of kinematic alignment and the effectiveness of PEFT on dynamics modules.

axioms (1)
  • domain assumption Kinematic alignment between source and target humanoids aligns input and output spaces sufficiently for the pretrained source policy to be reused.
    Described as the first step of Any2Any in the abstract.

pith-pipeline@v0.9.1-grok · 5805 in / 1181 out tokens · 37587 ms · 2026-06-30T16:15:29.976443+00:00 · methodology

0 comments
read the original abstract

Whole-body tracking (WBT) models have become a key foundation for humanoid robots, enabling them to imitate diverse motions with high fidelity. Training such models from scratch requires large-scale data and computation, making rapid deployment on new humanoid platforms costly. This raises a natural question: Can pretrained WBT models transfer across embodiments with minimal adaptation? To answer this question, we propose Any2Any, a paradigm that efficiently transfers an existing WBT specialist to a new humanoid embodiment with only a small amount of data and compute. Any2Any first performs kinematic alignment between source and target humanoids, aligning their input and output spaces so that the pretrained source policy can be meaningfully reused on the target embodiment.Any2Any then performs dynamics adaptation by applying lightweight parameter-efficient fine-tuning (PEFT) components to selected dynamics-sensitive modules, preserving useful behavioral priors while enabling targeted adaptation to the target robot. Extensive experiments on multiple humanoid platforms and pretrained backbones show that Any2Any substantially accelerates convergence and reduces training cost compared with training from scratch, while achieving competitive or superior tracking performance. Notably, using only 1% of the compute and data required for full training, Any2Any successfully transfers Sonic models pre-trained on Unitree G1 to LimX Oli and LimX Luna. These results suggest that pretrained WBT specialists can be efficiently reused across embodiments, providing a scalable path toward deploying humanoid whole-body control on new robots. More results and videos are available on our project page: https://any2any.top/.

Figures

Figures reproduced from arXiv: 2605.23733 by Feng Li, Hua Chen, Ming Yang, Tao Yu.

Figure 1
Figure 1. Figure 1: Illustration of ANY2ANY. A pretrained whole-body tracker (WBT) learned on spe￾cific humanoid can be efficiently transferred to another humanoid platform through the proposed ANY2ANY. For example, GEAR-SONIC [1], a large-scale pretrained WBT, can be adapted to a target robot LimX Oli using only a small fraction of the original training compute and data. Abstract: Whole-body tracking (WBT) models have become… view at source ↗
Figure 2
Figure 2. Figure 2: Architecture of ANY2ANY. The proposed framework adapts a pretrained whole-body tracker to arbitrary humanoid embodiments by combining Kinematic Alignment, which maps ob￾servations and actions across different robot morphologies, with Dynamics Adaptation, which ef￾ficiently fine-tunes lightweight modules to account for target-specific dynamics. fine-tuning instead freezes most pretrained weights and updates… view at source ↗
Figure 3
Figure 3. Figure 3: ANY2ANY transfer from Sonic to LimX humanoids, including SONIC2OLI and SONIC2LUNA. The curves compare ANY2ANY with the baseline, and the snapshots show sta￾ble rollout motions after adaptation. ther demonstrate faster convergence: ANY2ANY rapidly reaches high tracking rewards in the early training stage and obtains higher or comparable final rewards. The sim-to-sim snapshots verify that the adapted policie… view at source ↗
Figure 4
Figure 4. Figure 4: ANY2ANY transfer from the Oli-pretrained WBT policy to three target humanoids: OLI￾WBT2LUNA, OLIWBT2G1, and OLIWBT2H1. ANY2ANY is compared with the baseline trained from scratch. (a) Tracking-error radar plots. (b) Training curves of normalized tracking reward. (c) Sim-to-sim rollouts on diverse motions. scratch, but its convergence and final reward are still limited. This suggests that the pretrained WBT … view at source ↗
Figure 5
Figure 5. Figure 5: Ablation of ANY2ANY architectural components on OLIWBT2LUNA. The top table compares aligned full fine-tuning and ANY2ANY with LoRA. (a) Kinematic alignment ablation. (b) PEFT method ablation under kinematic alignment. ANY2ANY-LoRA achieves comparable rewards to full fine-tuning while using fewer trainable parameters and lower training cost. Setting Actor Critic Backbone Ref. In. Prop. In. Out. Backbone In.… view at source ↗
Figure 6
Figure 6. Figure 6: Ablation of LoRA injection scopes on OLI￾WBT2LUNA. The table summarizes the component-level injection locations across actor and critic modules, while the curves show the resulting joint tracking reward and mean episode reward [PITH_FULL_IMAGE:figures/full_fig_p012_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Performance comparison under varying data scales. Left: quantitative tracking errors. [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Comparison of reward curves under dif￾ferent GPU settings [PITH_FULL_IMAGE:figures/full_fig_p013_8.png] view at source ↗

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