Vision-language-action (VLA) models using discrete action tokens have proven effective for controling robotic arms on manipulation tasks. For a humanoid, however, the whole-body action space -- legs, torso, arms, and hands -- is far higher-dimensional and heterogeneous, raising tokenization, training, and real-time inference challenges that the previous VLA models do not address. We present Holo-M, to our knowledge the first discrete VLA model for humanoid loco-manipulation that intrinsically exploits the language model by extending its vocabulary with action tokens. In this model, we devise a unified action tokenizer that decomposes the humanoid action space into four body-part-specific tokenizers -- end-effector, body, hand, and kinematics -- enabling training across drastically different embodiments and data sources, including humanoid teleoperation, ego-centric human video, and simulation. By extending the language model's vocabulary with these action tokens, we avoid the knowledge-insulation problem inherent to the models that use separate continuous action experts. To meet real-time control requirements, we decode each body part's action tokens through grouped discrete diffusion decoding, rather than using autoregression on the action tokens. We have conducted extensive experiments on the SIMPLE humanoid loco-manipulation benchmark, in which Holo-M achieves the highest success rates in both the generalist and specialist evaluations, leading the second best by significant margins. We will release all the code and model weights.
Figures & tables
Figure 1: Overview of the proposed Holo-M framework. A unified action tokenizer, pre-trained across heterogeneous data (teleoperated, human-centric, simulated), decomposes the humanoid action space into four body-part tokenizers (EEF, body, hand, kinematics) sharing one discrete vocabulary. At inference, the VLM backbone encodes the image, language, and proprioceptive state into this vocabulary and decodes action tokens via grouped discrete diffusion – parallel within a body part, autoregressive across parts – which are then detokenized into whole-body controller commands driving the humanoid.
Group
Representation
Dim.
Tokens
End effector
Wrist and fingertip poses
48
100
Body
Body joint targets
29
62
Hand
Hand joint targets
14
32
Kinematics
Base-motion commands
5
14
Full action
All groups
96
208
Table 1: Canonical action-space decomposition used by Holo-M.
Figure 2: Progressive training of Holo-M through cross-embodiment autoregressive pre-training, humanoid post-training, grouped discrete diffusion fine-tuning, and task-specific adaptation.
Pretraining Data
Open-loop MAE ↓
Closed-loop Success Rate ↑
No Pretraining
0.022
53/180
HE
0.020
119/180
EgoDex + HE
0.019
138/180
Table 5: Effect of scaling pre-training data on Holo-M AR.
Figure 3: Our fixed schedule along the tick axis using 8 de-masking steps as example: observation is taken every 500ms, which the model consumes to infer 1-second action chunk. During model inference, the action steps from the previous chunk are executed.
De-masking steps
Inference latency, mean ± std (ms) ↓
2
175.8±11.5
4
275.7±9.3
8
476.8±16.9
Table 6: Real-world inference latency per 30-timestep chunk.
Task
Success Rate
Tabletop Grasp
10/10
Move Pick
8/10
Table 7: Real-robot success rate over 10 rollouts per task.
Whole-body humanoid loco-manipulation requires coordinating the robot's entire kinematic chain. However, most existing systems typically decouple the upper and lower bodies into separate controllers, limiting such coordination and yielding behaviors similar to those of a wheeled dual-arm platform. In this paper, we ask what it takes to build a whole-body native vision-language-action (VLA) model that maps language and pixels directly to all of the humanoid's degrees of freedom. We conduct a systematic empirical study organized as a roadmap of one-variable-at-a-time experiments across three phases: whole-body teleoperation, VLA model design, and heterogeneous co-training. Our study yields several intriguing findings: a joint-based whole-body teleoperation interface outperforms alternatives that only partially expose the humanoid's degrees of freedom; a VLA pretrained on static and wheeled dual-arm platforms transfers surprisingly well to a humanoid's full action space; and co-training with HuMI, the humanoid analog of UMI, extends the policy to new objects and instructions without additional whole-body teleoperation on those targets. Following this roadmap yields OpenHLM, an open-source recipe for whole-body humanoid loco-manipulation. In a challenging long-horizon task that spans a wide vertical range of the humanoid, OpenHLM outperforms two state-of-the-art humanoid VLA baselines (GR00T N1.6 and Ψ0) using less than half the total demonstration time. Our code, training data, and model checkpoints are available at [https://openhlm-project.github.io/].
Yingdong Hu, Haodong Zhu, Boyuan Zheng +6
Tsinghua University · Shanghai Qi Zhi Institute · Spirit AI
Vision-language-action policies are a promising foundation for general robot control, but long-horizon humanoid loco-manipulation requires the robot to treat task objects as persistent physical entities across movement, contact, occlusion, and recovery. We study this problem as object-state divergence: the object state used to condition a whole-body action can differ from the state used to decide whether the action achieved the intended physical relation. We propose \emph{Persistent Object Tokenization} (POT), which maintains role-indexed 3D object records from RGB-D observations and converts them into object tokens for a whole-body action expert. Instantiated as \emph{POT-VLA}, the same object records condition action generation and support geometric predicate checks, yielding a closed-loop execution system in which object state is both actionable and verifiable. On a Unitree G1, POT-VLA improves a matched direct GR00T-N1.7 baseline from 39/80 to 71/80 successes over eight real-world task families. In an external Being-0-aligned reference, POT-VLA achieves 44/50 successes on aligned service tasks, compared with the 37/50 success reported by the Being-0 paper. The largest gains occur on tasks requiring maintained 3D relations, suggesting that persistent object-centered state is a useful abstraction for verifiable humanoid VLA execution.
Discrete action tokenization provides a compact interface for autoregressive VLA policies, but accurately recovering continuous robot actions from discrete codes remains challenging. Existing tokenizers typically map each discrete code to a fixed continuous action prototype, ignoring the robot's current proprioceptive state. This limitation is particularly pronounced in manipulation, where the same action token may require different continuous controls under different joint configurations, object poses, and contact conditions. We therefore propose SA-VLA, a state-aware action tokenizer that conditions action decoding on robot state. We study two state-injection mechanisms for VQ-based action tokenization: cross-attention between state and action features, and a lightweight state adapter that predicts action-wise modulation factors for state-conditioned action modulation and reconstruction. The adapter formulation expands the effective support of a finite codebook by allowing each discrete token to represent a family of state-dependent continuous actions, while preserving the efficiency and compatibility of discrete action modeling. Integrated into an LLM-based VLA policy, SA-VLA supports both autoregressive and parallel action-token decoding with minimal changes to the model interface. On 12 RoboTwin manipulation tasks, SA-VLA improves the average success rate from 0.29 to 0.56 over the strongest tokenizer baseline. In zero-shot sim-to-real experiments on three real-world tasks, it further improves average success from 0.15 to 0.33 over the strongest tokenizer baseline. These results demonstrate that state-conditioned action decoding is a simple and effective mechanism for reducing the compression gap in discrete VLA policies.
Tengyue Jiang, Chunpu Xu, Jiayue Kang +1
2East China University of Science and Technology · 3Hong Kong Polytechnic University · 4Xi’an University of Electronic Science and Technology +1