WARPED: Wrist-Aligned Rendering for Robot Policy Learning from Egocentric Human Demonstrations

Harry Freeman, Chung Hee Kim, Jenny Wang, George Kantor
Carnegie Mellon University
CoRL 2026
Paper Video Code (coming soon)
WARPED transforms egocentric human demonstrations into wrist-camera observations and robot trajectories for policy learning.
WARPED transforms egocentric human demonstrations into wrist-camera observations and robot trajectories for training robot policies.

Scan → Demo → Retarget → Train and Deploy

Abstract

Recent advancements in learning from human demonstration have shown promising results in addressing the scalability and high cost of data collection required to train robust visuomotor policies. However, existing approaches are often constrained by a reliance on multiview camera setups, depth sensors, or custom hardware, and are typically limited to policy execution from third-person or egocentric cameras. In this paper, we present WARPED, a framework designed to synthesize realistic wrist-view observations and actions from human demonstration videos to facilitate the training of visuomotor policies using only monocular RGB data. With data collected from an egocentric RGB camera, our system leverages vision foundation models to initialize the interactive scene. A hand-object interaction pipeline is then employed to track the hand and manipulated object and retarget the trajectories to a robotic end-effector. Lastly, photorealistic wrist-view observations are synthesized via Gaussian Splatting to directly train a robotic policy. We demonstrate that WARPED achieves success rates comparable to teleoperation on four of five rigid-object tabletop tasks and one articulated-object task, while requiring 4-9× less data collection time.

Method

WARPED recovers hand–object motion from an egocentric RGB demonstration, retargets it to the robot end effector, and renders the recovered trajectory from the robot’s wrist camera using Gaussian Splats. These observation–action pairs train a diffusion policy without collecting task-specific robot demonstrations.

Overview of the WARPED pipeline from scene capture and human demonstration through scene initialization, hand-object optimization, retargeting, wrist-view rendering, policy training, and robot deployment.

Egocentric Human to Wrist-Camera Robot Demonstrations

Egocentric Human Demonstration

Wrist-Camera Render

Augmented Render

Robot Rollouts

Policy rolled out using wrist camera. Exocentric camera for visualization only.

Exocentric View Wrist View

Novel Objects

Rotate Box

Bottle from Rack

Can on Plate

Results

WARPED achieves success rates comparable to teleoperation while requiring substantially less data collection time.

Success rates over 20 evaluation trials.
Method Rotate Box Pour Mug Bottle from Rack Wipe Brush Can on Plate Open Microwave
Teleoperation 16/20 19/20 16/20 15/20 19/20 18/20
Alter (Heng et al.) 7/20 3/20 0/20 0/20 8/20 0/20
WARPED 20/20 18/20 17/20 11/20 17/20 17/20

Data Collection Time

Total demonstration collection time (mm:ss).
Method Rotate Box Pour Mug Bottle from Rack Wipe Brush Can on Plate Open Microwave
Teleoperation 22:51 24:59 31:49 30:16 15:20 35:17
WARPED 3:37 3:18 3:27 5:19 3:41 4:12

Novel Objects

Success rates on novel object instances over 10 evaluation trials.
Method Novel Object Rotate Box Bottle from Rack Wipe Brush Can on Plate
Teleoperation Obj 1 8/10 4/10 7/10 9/10
Obj 2 2/10 2/10 4/10 9/10
WARPED Obj 1 10/10 8/10 7/10 10/10
Obj 2 8/10 5/10 2/10 9/10

BibTeX

@inproceedings{freeman2026warped,
  title     = {WARPED: Wrist-Aligned Rendering for Robot Policy Learning from Egocentric Human Demonstrations},
  author    = {Freeman, Harry and Kim, Chung Hee and Wang, Jenny and Kantor, George},
  booktitle = {Conference on Robot Learning (CoRL)},
  year      = {2026}
}