General-purpose mobile manipulation requires a combination of capable robot hardware, scalable demonstration collection, expressive whole-body control, and effective policy learning. Yet these components are often developed independently, creating mismatches that limit the capabilities of the complete system. We present Pixel, a co-designed full-stack system for learning general-purpose mobile manipulation. Pixel combines a low-cost wheeled humanoid with human-aligned kinematics and compliant grippers, multiple teleoperation and robot-free human data-collection interfaces, a unified whole-body controller, and a common policy learning pipeline across human and robot demonstrations. A hierarchical learning stack further enables language-conditioned visuomotor control and long-horizon task execution. We evaluate the system across hardware, data collection, and policy learning, and demonstrate mobile-manipulation behaviors requiring dexterity, active perception, navigation, and whole-body coordination. We hope that Pixel provides an integrated and extensible foundation for studying how robot hardware, data, control, and learning can be designed together for robot learning. More results at https://pixel-robot.com/pixel
Figures & tables
Fig. 2: Hardware co-design. Our robot hardware and sensorized human data collection system are co-designed at all levels, with shared camera perspectives, aligned human–robot kinematics, paired end effectors designed from the ground up, and a functionally aligned lower-body design.
Fig. 3: Pixel hardware overview. Our low-cost robot platform is designed for high performance, reliability, and reproducibility. The system integrates a holonomic mobile base, telescoping vertical lift, articulated torso, active-perception head, and custom high-force, compliant end effectors.
Fig. 4: Data collection interfaces. Pixel supports three main forms of data collection. Left: A kinematically matched leader-arm station for precise bimanual teleoperation. Center: VR for untethered whole-body teleoperation. Right: Sensorized human data collection system for scaling robot-free demonstrations. All interfaces produce a common task-space representation used by the shared whole-body controller.
Fig. 5: Policy overview. Left: The high-level policy processes voice commands, prior interaction context, and selected keyframes to determine the next subtask and generate a verbal response to the user. Right: The low-level policy conditions on the subtask description and visual observations to generate robot actions using a DiT-based action expert.
Fig. 6: Evaluation tasks. We evaluate our robot hardware, data-collection interfaces, and policy-design choices across everyday mobile-manipulation tasks requiring precision, long-horizon execution, and whole-body coordination.
Fig. 7: Top: Test objects for the gripper test. Bottom: Experimental setup showing a grasping test with the AgileX Piper gripper.
Fig. 8: Relative-base action space. We express predicted head and bimanual end-effector trajectories in a shared pseudo-base frame constructed at the start of each action chunk. This representation preserves long-horizon precision and provides a common action space for sensorized human and robot demonstrations. Relative-pure and Relative-head reference frames are shown for comparison.
Fig. 9: Workspace comparison. Pixel achieves the strongest human–robot workspace alignment among the evaluated platforms. Mobile ALOHA, YOR, and AGIBOT G1 cover a smaller fraction of the human workspace, while Dexmate Vega and RB-Y1M achieve broader reach through articulated lower bodies but substantially exceed human workspace volume.
Robot
Overall Match ↑
Arm-Spec. Match ↑
Human Coverage ↑
Relative Volume →1
Pixel (Ours)
90.99%
81.04%
89.89%
1.11
YOR
89.80%
84.27%
79.65%
0.89
AGIBOT-G1
75.22%
74.44%
80.71%
1.30
Mobile ALOHA
66.50%
57.47%
46.00%
0.52
RB-Y1M
54.65%
52.29%
100.00%
3.02
Dexmate
41.28%
46.79%
100.00%
4.37
TABLE I: Human–robot workspace comparison. Reach-match scores use a 50mm tolerance; coverage and relative volume use the original 25mm voxel occupancy.
Fig. 10: (a) We find that torso, lift, and head motion jointly support consistent task success across the evaluated tasks. (b) Pixel’s gripper outperforms commercial alternatives such as the AgileX Piper gripper in grasping force and stability.
Fig. 11: (a) User preferences obtained through a survey on different data collection methods. (b) Mean completion times for two user-study tasks using different data collection interfaces. (c) Our shared interface and controller design enables co-training to improve performance.
Fig. 12: (a) Comparison of observation- and action-space design choices for policy training with sensorized human data . (b) Comparison of observation- and action-space design choices for policy training with robot teleoperation data .
Task
m
k
d
rd
Each hand pose
3
60
50
0.2
Head pose
4
50
100
0.05
Upper-body posture
0.2
50
100
0.2
Torso posture
0.3
150
100
0.2
Lift posture
1
50
5000
1.0
Base yaw attractor
1
50
100
0.2
TABLE II: Attractor parameters used in Equation 11 . All rows use α=40 and sd=10 . The head row applies separately to its forcing and geometric terms; damping applies only to forcing terms.
Fig. 13: Offline and online temporal synchronization. Dots indicate source samples; orange dots show the latest samples at or before the highlighted grid time. (a) Offline synchronization resamples the shared recording interval (shaded), with one grid time illustrated. (b) Online synchronization selects from currently buffered samples. Rings mark the latest sample in each stream; the earliest of these determines the reference time after rounding down to the grid. Timing is schematic.
Parameter
Setting
Temperature
0.1
Maximum output tokens
4096
Image resolution / API tier
Low / priority
Response format
Schema-constrained JSON
Thinking level
Planning: low; monitoring: minimal
Request timeout
Planning: 8 s; monitoring: 4 s
TABLE III: High-level VLM configuration.
Skill
Low-level policy instruction
Visual completion criteria
Tidy toys
Pick up the toys from the table and place them neatly in the toy basket.
No toys remain on the table, and the toys are visibly in the basket.
Tidy pillows
Navigate to the couch and tidy the pillows.
Both pillows are fully visible, upright, and neatly arranged on the couch.
Hang clothing
Grab the clothing item from the person and hang it on the clothes rack.
The clothing item is visibly hanging on the rack (a wooden pole).
Deliver jacket
Grab the jacket from the clothes rack and give it to the person.
The requesting person visibly receives the jacket.
Clean table
Pick up the handheld vacuum and clean the dining table.
The table is visibly clean after vacuuming, and the vacuum is retracted from the surface.
Return vacuum
Put the vacuum back.
The handheld table vacuum is visibly returned to storage.
TABLE IV: Configured high-level skill library.
Hyperparameter
Value
Vision backbone
DINOv3 ViT-B/16
Vision parameters per view
Approximately 86M
Language encoder
Frozen DistilBERT
Language embedding dimension
768
Visual context length
591 tokens
Token / hidden dimension
768
TABLE V: Low-level policy training hyperparameters.
Hyperparameter
Value
Random resized crop area
0.9–1.0
Random rotation
±5∘
Color jitter: brightness
0.3
Color jitter: contrast
0.4
Color jitter: saturation
0.5
Color jitter: hue
0.08
TABLE VI: Image augmentation settings.
Fig. 14: Representative robot task sequences. Each row shows four key stages, ordered from left to right. Rows (a–c) correspond to the sensorized human data tasks, and rows (d–f) to the robot teleoperation data tasks.
Task
Score
Condition
Toy Cleanup
0.000
No task-relevant progress.
0.500
The toy is grasped and lifted from the table.
1.000
The toy is released inside the basket.
Tidy Pillow
0.000
No task-relevant progress.
0.143
The robot navigates to the couch.
0.429
The first pillow is picked up.
TABLE VII: Progress-based task scoring rubrics. Each score is cumulative.
Configuration
Toy Cleanup
Tidy Pillow
Trash Disposal
Average
Complete system
100.0±0.0
100.0±0.0
100.0±0.0
100.0±0.0
Torso disabled
60.0±54.8
14.3±0.0
68.0±46.0
47.4±29.0
Lift disabled
60.0±54.8
85.7±24.7
68.0±46.0
71.2±13.2
Head control disabled
100.0±0.0
20.0±12.8
36.0±41.0
52.0±42.3
TABLE VIII: Hardware degree of freedom ablations.
Configuration
Toy Cleanup
Tidy Pillow
Trash Disposal
Average
All cameras, relative-base
75.0±44.4
95.0±10.6
76.0±37.6
82.0±11.3
Head camera only
5.0±15.4
80.7±25.9
34.0±42.6
39.9±38.2
Wrist cameras only
82.5±37.3
72.9±29.3
44.0±41.3
66.5±20.0
Relative-pure actions
85.0±36.6
47.9±22.4
69.0±25.5
67.3±18.6
Relative-head actions
20.0±41.0
25.0±27.4
32.0±37.5
25.7±6.0
TABLE IX: Policy ablations with sensorized human data.
Configuration
Vacuum Cleaning
Snack Retrieval
Hang Jacket
Average
All cameras, relative-base
65.0±45.9
88.3±27.3
82.9±22.1
78.7±12.2
Head camera only
0.0±0.0
100.0±0.0
78.6±22.6
59.5±52.7
Wrist cameras only
70.0±38.7
93.3±14.1
58.6±22.8
74.0±17.7
Absolute joint-space actions
77.5±41.6
90.0±31.6
91.4±18.1
86.3±7.7
TABLE X: Policy ablations with robot teleoperation data.
Sensorized human demonstrations offer scalable data for mobile manipulation, but embodiment differences complicate policy learning. We present Pixel-UMI, a pipeline for learning environment-aware whole-body policies from human data. We first introduce Environment-Aware Retargeting (EAR), which combines reconstructed scene geometry with demonstrated head and gripper trajectories to determine robot posture and generate collision-aware joint and base targets. Delay-calibrated action assignment converts these trajectories into actions. However, kinematic differences can leave residual pose errors, creating viewpoint mismatches between human demonstrations and robot observations. We therefore introduce Retargeting-Error-Aware Masking (REAM), which uses these errors during training to reduce reliance on mismatched views at deployment. Finally, because estimated actions cannot fully capture contact-rich interactions, we collect on-policy human corrections through DAgger to improve performance. We evaluate Pixel-UMI on five challenging tasks spanning articulated-object manipulation, deformable-object handling, constrained-space reaching, whole-body collision avoidance, and contact-rich bimanual coordination, achieving 87.0% average task progress. Video results are available at https://pixel-robot.com/pixel-umi
We present Whole-Body Mobile Manipulation Interface (HoMMI), a data collection and policy learning framework that learns whole-body mobile manipulation directly from robot-free human demonstrations. We augment UMI interfaces with egocentric sensing to capture the global context required for mobile manipulation, enabling portable, robot-free, and scalable data collection. However, naively incorporating egocentric sensing introduces a larger human-to-robot embodiment gap in both observation and action spaces, making policy transfer difficult. We explicitly bridge this gap with a cross-embodiment hand-eye policy design, including an embodiment agnostic visual representation; a relaxed head action representation; and a whole-body controller that realizes hand-eye trajectories through coordinated whole-body motion under robot-specific physical constraints. Together, these enable long-horizon mobile manipulation tasks requiring bimanual and whole-body coordination, navigation, and active perception. Results are best viewed on: https://hommi-robot.github.io
High-quality demonstration data are essential for humanoid robot skill learning, especially for whole-body behaviors that require coordinated perception, locomotion, and manipulation. Existing data-collection methods largely rely on robot teleoperation, which is constrained by hardware accessibility, operator expertise, and limited efficiency. Inspired by the Universal Manipulation Interface (UMI), we propose HumanoidUMI, a portable and robot-free framework for humanoid whole-body data collection. HumanoidUMI uses lightweight VR devices and UMI-inspired grippers to collect sparse human keypoint trajectories, wrist-view observations, and gripper actions. These demonstrations train a high-level policy to predict future keypoints, which are retargeted to robot-native whole-body references and executed by a whole-body controller. Experiments in five real-world scenarios demonstrate the effectiveness of the proposed framework and validate the collected demonstrations for transferable humanoid whole-body skill learning.