$Ψ_0$: An Open Foundation Model Towards Universal Humanoid Loco-Manipulation

Songlin Wei; Hongyi Jing; Boqian Li; Zhenyu Zhao; Jiageng Mao; Zhenhao Ni; Sicheng He; Jie Liu

cs.ROMar 12, 2026

$Ψ_0$ : An Open Foundation Model Towards Universal Humanoid Loco-Manipulation

Authors: Songlin Wei, Hongyi Jing, Boqian Li, Zhenyu Zhao, Jiageng Mao, Zhenhao Ni, Sicheng He, Jie Liu,

Organizations: USC Physical Superintelligence (PSI) Lab · NVIDIA · WorldEngine

Abstract

We introduce $Ψ_0$ (Psi-Zero), an open foundation model to address challenging humanoid loco-manipulation tasks. While existing approaches often attempt to address this fundamental problem by co-training on large and diverse human and humanoid data, we argue that this strategy is suboptimal due to the fundamental kinematic and motion disparities between humans and humanoid robots. Therefore, data efficiency and model performance remain unsatisfactory despite the considerable data volume. To address this challenge, \ours;decouples the learning process to maximize the utility of heterogeneous data sources. Specifically, we propose a staged training paradigm with different learning objectives: First, we autoregressively pre-train a VLM backbone on large-scale egocentric human videos to acquire generalizable visual-action representations. Then, we post-train a flow-based action expert on high-quality humanoid robot data to learn precise robot joint control. Our research further identifies a critical yet often overlooked data recipe: in contrast to approaches that scale with noisy Internet clips or heterogeneous cross-embodiment robot datasets, we demonstrate that pre-training on high-quality egocentric human manipulation data followed by post-training on domain-specific real-world humanoid trajectories yields superior performance. Extensive real-world experiments demonstrate that \ours\ achieves the best performance using only about 800 hours of human video data and 30 hours of real-world robot data, outperforming baselines pre-trained on more than 10 $\times$ as much data by over 40% in overall success rate across multiple tasks. We will open-source the entire ecosystem to the community, including a data processing and training pipeline, a humanoid foundation model, and a real-time action inference engine.

Figures & tables

Fig. 2: Model Training and Deployment : First, we pre-train the VLM on the EgoDex [ 20 ] dataset to autoregressively predict the next-action tokens in the task space. Then, we post-train the flow-based action expert using robotic data to predict action chunks in the joint space. Finally, we implement a real-time chunking mechanism that leverages the lower-body controller to achieve smooth whole-body control.

Fig. 3: MM-DiT for VLA: Comparison of MM-DiT architecture with naive DiT.

\tau

is the flow timestep and VL and A denotes hidden states of the vision-language and action respectively.

Fig. 4: Real-Time Chunking: Given that the previous action is being executed (yellow line), the next action can diverge significantly (cyan line) without RTC, which leads to control jitter. With RTC (red line), the divergence between two consecutive actions is strongly suppressed, resulting in smoother and more stable behavior.

Fig. 5: Real-Robot Teleoperation Setup: We use MANUS gloves for dexterous hand retargeting; a VR headset and wrist trackers capture upper-body poses for inverse kinematics, while waist and foot trackers provide high-level locomotion commands.

Fig. 6: Real-World Task Setup: We evaluate $\Psi_{0}$ on eight diverse long-horizon dexterous loco-manipulation tasks involving manipulation, whole-body motion, and locomotion. The task instruction is overlayed on the task images and each sub-task is denoted with marker for better visualization. Our policy rollout videos are included in the Supplementary Materials.

Fig. 7: Real-World Benchmark: Evaluation results of policies across our eight tasks, showing task-wise success rates (%) (left) and aggregated skill-level success rates (%) (right). The task descriptions are shown in Fig. 6 . Detailed results for each task including all sub-task progress are included in the Supplementary Materials .

Pre-Training		Post-Training (On HE)	Real-Time Chunking	MM-DiT Action Head	Naive DiT Action Head	Right-Arm Pick-n-Place	Left-Arm Pick-n-Place	Dual-Arm Carry	Overall Success Rate
EgoDex	HE	Post-Training (On HE)	Real-Time Chunking	MM-DiT Action Head	Naive DiT Action Head	Right-Arm Pick-n-Place	Left-Arm Pick-n-Place	Dual-Arm Carry	Overall Success Rate
✗	✗	✗	✗	✗	✓	1/10	1/10	1/10	0/10
✗	✗	✗	✗	✓	✗	9/10	2/10	3/10	2/10
✓	✗	✗	✗	✓	✗	8/10	6/10	6/10	6/10
✓	✓	✗	✗	✓	✗	8/10	8/10	9/10	8/10
✓	✓	✓	✗	✓	✗	9/10	9/10	10/10	9/10

TABLE I: Ablation Studies . We study the effects of pre-training, post-training, and real-time chunking on a dual-arm long-horizon task which consists of three steps: right-arm pick and place, left-arm pick-and-place and dual-arm lift.

	Reconstruction L1 Loss	Avg Token Length
Before	$5.83\times 1\mathrm{e}{-}4$	2.08
After	$\textbf{1.95}\times 1\mathrm{e}{-}4$	13.04

TABLE II: Fast Tokenizer. Comparison of reconstruction loss and average token length before and after training. Boldface indicates the best performance.

Fig. 8: Training-Time RTC. Diffusion timesteps and loss calculation in training.

Fig. 9: Real-Time Action Chunking System Design. The system consists of a client (observation collection and action execution) and a server (control and inference). The Control Loop (30Hz) coordinates observation updates and action dispatch, while the Inference Loop runs asynchronously to compute the next action chunk when $t\geq s_{\text{min}}$ , enabling seamless chunk transitions without inference-induced interruptions.

Fig. 10: Single Operator Teleoperation Framework. Our framework maps human upper-body motions to robot arm and hand control via retargeting and multi-target IK, while lower-body pose is generated through an RL-based policy.

Task 3 Task 2

Descriptions

Pick the bottle, turn around, and pour into cup

Spray the bowl with water, wipe clean, and fold it up

Grasp

Move

Pour

Place

Overall

Grasp

Pull

Spray

Put

Overall

Diffusion Policy

0/10

ACT

0/10

3/10

2/10

4/10

3/10

1/10

InternVLA-M1

0/10

2/10

4/10

0/10

EgoVLA

4/10

6/10

1/10

2/10

1/10

0/10

TABLE III: Real-World Benchmarking: We provide a detailed report of real-world benchmarking results, including sub-task progress. Each task consists of three to five subtasks, and a trial is counted as successful only if all subtasks are completed. Boldface indicates the best performance, while underlining denotes the second-best performance.

GR00T-N1.6	Pick the dumpling	Pick the hippo	Carry the box	Overall SR
w/o RTC	10/10	7/10	9/10	7/10
w/ RTC	6/10	7/10	10/10	6/10

TABLE IV: GR00T with RTC. We study the effect of RTC on the GR00T baseline. The task consists of three steps. It achieves comparable performance on GR00T with and without RTC.

Experiment 1	Pick the dumpling	Pick the hippo	Carry the box	Overall SR
Baseline ( $\Psi_{0}$ )	9/10	9/10	10/10	8/10
Variant (10% EgoDex)	6/10	1/10	5/10	1/10
Experiment 2	Grasp bottle	Wipe the bowl	Stack up	Overall SR
Baseline ( $\Psi_{0}$ )	10/10	9/10	7/10	7/10
Variant (10% EgoDex)	9/10	10/10	7/10	6/10

TABLE V: Ablation of Pre-Training on 10% EgoDex. We found that using 10% of EgoDex perform worse than the baseline

\Psi_{0}

, demonstrating the efficacy of full EgoDex pre-training.

Experiment 1	Pick the dumpling	Pick the hippo	Carry the box	Overall SR
Baseline ( $\Psi_{0}$ )	9/10	9/10	10/10	8/10
Variant (HE)	9/10	4/10	10/10	4/10
Experiment 2	Grasp bottle	Wipe the bowl	Stack up	Overall SR
Baseline ( $\Psi_{0}$ )	10/10	9/10	7/10	7/10
Variant (HE)	10/10	9/10	4/10	4/10

TABLE VI: Ablation of Pre-Training on HE. We discover that the HE variant achieves high performance on tasks that do not require fine-grained manipulation; however, it still lags behind our baseline on subtasks requiring more precise manipulation.

Fig. 11: Multi-Task Fine-Tuning. Joint fine-tuning (Cyan) across multiple tasks leads to a performance drop on individual tasks (Pink).

Robot Foundation Models

Robot Foundation Models are pretrained systems that combine perception, reasoning, and action to support diverse tasks and robot embodiments.

See all 128 papers

Oct 13, 2026

$λ_0$ : Scaling Open-World Egocentric Human Data for Humanoid Loco-Manipulation

Humanoid loco-manipulation requires coordinated whole-body interaction with the physical world, yet collecting robot demonstrations at scale remains costly. Human demonstrations offer a scalable alternative, but existing egocentric human supervision often omits locomotion and posture, while embodiment differences and controller dynamics hinder accurate end-effector execution. We introduce HumanVerse, a 500-hour dataset of open-world human loco-manipulation pairing egocentric video with synchronized body and hand motion. Our human-to-humanoid pipeline aligns camera viewpoints and arm appearance, and refines motion references and controller inputs offline to reduce end-effector errors while preserving whole-body coordination. We validate the pipeline through zero-shot transfer to Unitree G1 on representative whole-body manipulation and long-range loco-manipulation tasks, without target-task robot demonstrations. Building on this pipeline, we develop

λ_0

, a humanoid foundation model for whole-body loco-manipulation, with robot-aligned human demonstrations providing supervision for mid-training. Increasing whole-body human data consistently improves downstream task progress.

λ_0

achieves 90.0% success on SIMPLE and 62.5% across real-world loco-manipulation tasks. We expect this study to guide scalable whole-body human data collection toward humanoid foundation models for richer interaction with the physical world. Homepage: https://lambdahumanoid.github.io/

Lambda-humanoid Team

Alibaba Token Hub and Amap, Alibaba Group

Humanoid Loco-Manipulation Whole-Body Robotic Manipulation Robot Foundation Models Cross-Embodiment Robot Learning Motion Retargeting

Ask PDF

Sep 30, 2026

Towards a General Humanoid Loco-Manipulation Model via Egocentric Whole-Body Human Data Pretraining

Humanoid whole-body manipulation has advanced rapidly, enabling policies to coordinate locomotion, posture, bimanual interaction, and dexterous hand movements. Meanwhile, egocentric human videos provide diverse examples of everyday interactions across objects and scenes, offering scalable supervision without robot operation. However, existing supervision from these videos provides limited coverage of whole-body movement and coordination with hand-object interaction, while obtaining such supervision through humanoid teleoperation is also costly and difficult to scale. We therefore explore how human experience can support scalable learning of humanoid loco-manipulation. To support this study, we introduce HumanVerse-500, a 500-hour dataset of diverse human loco-manipulation behaviors in open-world environments, collected with a lightweight wearable system that synchronizes egocentric video with body and hand motion. Building on this dataset, we develop

λ_0

, a whole-body humanoid vision-language-action policy, through three-stage training that first learns interaction from diverse egocentric datasets, then coordinates body and hand motion using HumanVerse-500, and finally adapts the policy to downstream tasks and robot embodiments. Across these stages,

λ_0

learns a shared representation space for human experience transfer, while domain-specific interfaces handle differences between human and robot states and actions. We evaluate

λ_0

on SIMPLE and 4 real-world loco-manipulation tasks, achieving state-of-the-art performance, and further analyze its scaling behavior, generalization, and training-stage contributions to understand how human data support downstream whole-body humanoid control. We will release our code, models, and data to support further research.

Chongyang Xu, Zhao Wu, Jin Chen +9

Alibaba Group · Sichuan University · Shanghai Innovation Institute +2

Humanoid Loco-Manipulation Cross-Embodiment Robot Learning VLMs for Robotics Robot Learning

Ask PDF

Jun 20, 2026

OpenHLM: An Empirical Recipe for Whole-Body Humanoid Loco-Manipulation

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

Ψ_0

) 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

Humanoid Loco-Manipulation Long-Horizon Robotic Manipulation Robot Teleoperation Cross-Embodiment Robot Learning Whole-Body Robotic Manipulation Vision-Language-Action Models

Ask PDF

Abstract

Robot Foundation Models

Explore similar work

λ0λ_0λ0​: Scaling Open-World Egocentric Human Data for Humanoid Loco-Manipulation

Towards a General Humanoid Loco-Manipulation Model via Egocentric Whole-Body Human Data Pretraining

OpenHLM: An Empirical Recipe for Whole-Body Humanoid Loco-Manipulation

$λ_0$ : Scaling Open-World Egocentric Human Data for Humanoid Loco-Manipulation