OSCAR: Omni-Embodiment Action-Conditioned World Model for Robotics

• URL: http://arxiv.org/abs/2606.04463v2
• Date: Thu, 04 Jun 2026 13:11:48 GMT
• Title: OSCAR: Omni-Embodiment Action-Conditioned World Model for Robotics
• Abstract summary: We present a precise action-conditioned video world model that generalizes across different robot embodiments and enables robot policy evaluation.<n>Our model achieves significant improvement on action following, appearance quality, and motion consistency, compared to existing baselines.<n>We further deploy OSCAR to evaluate robot policies from RoboArena.
• Score: 6.134835623350618
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present OSCAR, a precise action-conditioned video world model that generalizes across different robot embodiments and enables robot policy evaluation. Existing video world models face three main challenges for real-world robot evaluation: limited scenario diversity in current robot training datasets, imprecise action following, and poor generalization across embodiments for broad adoption. We tackle these challenges from two perspectives. At its core is a large-scale standardized data pipeline that curates, filters, and deduplicates broad robotics and egocentric human datasets, yielding a clean joint-training dataset that spans diverse tasks, scenarios, actions, and robot embodiments. To condition the video model, we adopt 2D kinematic skeleton rendering as a unified conditioning representation that generalizes across different robot arms or even human hands. We finetune the Cosmos-Predict2.5-2B model on a single GH200 GPU. Our model achieves significant improvement on action following, appearance quality, and motion consistency, compared to existing baselines, which either have a much larger model size or require more GPUs. We further deploy OSCAR to evaluate robot policies from RoboArena. Extensive experiments demonstrate the significant correlation between our virtual policy evaluation in OSCAR and real-world evaluation, paving the way for the future where robot policies can be purely evaluated in virtual generated worlds.

Related papers

• H2R-Bench: Benchmarking Human-to-Robot Manipulation Video Generation in World Models [56.60098789696351]
  H2R-Bench is a benchmark for evaluating cross-embodiment human-to-robot manipulation video generation.<n>Each benchmark instance contains a human demonstration video, target embodiment constraints, and source-grounded annotations.<n>We benchmark eleven state-of-the-art video generation models across six manipulation families and two robot embodiments.
  arXiv  Detail & Related papers  (2026-08-13T10:14:33Z)
• GeniWorld: A Generalizable Interactive World Model for Robotic Manipulation via Visual Actions [25.431221165541874]
  Generalist robot policies exhibit strong capabilities, but their robustness in complex and unseen environments remains limited.<n>We present GeniWorld, an interactive world model for robots that generalizes robustly across unseen scenarios.
  arXiv  Detail & Related papers  (2026-08-06T17:40:32Z)
• Ego2Robot: Scalable Robot Data Synthesis from Egocentric Human Data [60.94418315274988]
  Ego2Robot supports both curated and in-the-wild videos, producing 18,561 hours of robot training data spanning 15 robot morphologies.<n>Joint pretraining on Ego2Robot-synthesized and robot data consistently improves out-of-distribution generalization across multiple types.
  arXiv  Detail & Related papers  (2026-08-03T17:52:26Z)
• Robot-Factored World Models via Robot Rendering [25.12876640584098]
  Action-conditioned video world models predict future observations from an initial observation and an action signal.<n>We propose robot-factored world models, which move two robot-specific factors outside the world model.<n>Our experiments show that the rendered interface outperforms vector-conditioned baselines and generalizes to unseen robot embodiments inference.
  arXiv  Detail & Related papers  (2026-07-24T17:59:57Z)
• RoboWorld: Fast and Reliable Neural Simulators for Generalist Robot Policy Evaluation [52.855830378221775]
  Video world models are emerging as a scalable alternative for evaluating generalist robot policies.<n>We introduce RoboWorld, an automated evaluation pipeline that pairs a fast video world model with a task-progress-aware vision-language model scoring.
  arXiv  Detail & Related papers  (2026-07-01T15:22:41Z)
• Robots Need More than VLA and World Models [38.16463528269755]
  Generalist robot intelligence is often framed as a policy-scaling problem.<n>In this paper we argue that this framing is incomplete.<n>We identify four missing components for the next generation of robotics.
  arXiv  Detail & Related papers  (2026-06-04T10:43:14Z)
• DreamDojo: A Generalist Robot World Model from Large-Scale Human Videos [110.98100817695307]
  We introduce DreamDojo, a foundation world model that learns diverse interactions and dexterous controls from 44k hours of egocentric human videos.<n>Our work enables several important applications based on generative world models, including live teleoperation, policy evaluation, and model-based planning.
  arXiv  Detail & Related papers  (2026-02-06T18:49:43Z)
• MiVLA: Towards Generalizable Vision-Language-Action Model with Human-Robot Mutual Imitation Pre-training [102.850162490626]
  We propose MiVLA, a vision-language-action model empowered by human-robot mutual imitation pre-training.<n>We show that MiVLA achieves strong improved generalization capability, outperforming state-of-the-art VLAs.
  arXiv  Detail & Related papers  (2025-12-17T12:59:41Z)
• DreamGen: Unlocking Generalization in Robot Learning through Video World Models [120.25799361925387]
  DreamGen is a pipeline for training robot policies that generalize across behaviors and environments through neural trajectories.<n>Our work establishes a promising new axis for scaling robot learning well beyond manual data collection.
  arXiv  Detail & Related papers  (2025-05-19T04:55:39Z)
• VidBot: Learning Generalizable 3D Actions from In-the-Wild 2D Human Videos for Zero-Shot Robotic Manipulation [53.63540587160549]
  VidBot is a framework enabling zero-shot robotic manipulation using learned 3D affordance from in-the-wild monocular RGB-only human videos.<n> VidBot paves the way for leveraging everyday human videos to make robot learning more scalable.
  arXiv  Detail & Related papers  (2025-03-10T10:04:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.