dWorldEval: Scalable Robotic Policy Evaluation via Discrete Diffusion World Model
- URL: http://arxiv.org/abs/2604.22152v1
- Date: Fri, 24 Apr 2026 01:50:53 GMT
- Title: dWorldEval: Scalable Robotic Policy Evaluation via Discrete Diffusion World Model
- Abstract summary: We propose dWorldEval, which uses a discrete diffusion world model as a scalable evaluation proxy for robotics policies.<n>dWorldEval maps all modalities - including vision, language, and robotic actions - into a unified token space, reaching them via a single transformer-based denoising network.
- Score: 14.221014931347327
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Evaluating robotics policies across thousands of environments and thousands of tasks is infeasible with existing approaches. This motivates the need for a new methodology for scalable robotics policy evaluation. In this paper, we propose dWorldEval, which uses a discrete diffusion world model as a scalable evaluation proxy for robotics policies. Specifically, dWorldEval maps all modalities - including vision, language, and robotic actions - into a unified token space, modeling them via a single transformer-based denoising network. In this paper, we propose dWorldEval, using a discrete diffusion world model as a scalable evaluation proxy for robotics policy. Specifically, it maps all modalities, including vision, language, and robotics action into a unified token space, then denoises them with a single transformer network. Building on this architecture, we employ a sparse keyframe memory to maintain spatiotemporal consistency. We also introduce a progress token that indicates the degree of task completion. At inference, the model jointly predicts future observations and progress token, allowing automatically determine success when the progress reaches 1. Extensive experiments demonstrate that dWorldEval significantly outperforms previous approaches, i.e., WorldEval, Ctrl-World, and WorldGym, on LIBERO, RoboTwin, and multiple real-robot tasks. It paves the way for a new architectural paradigm in building world simulators for robotics evaluation at scale.
Related papers
- World-Action Models for Robot Learning and Control: A Survey [41.45329550202786]
Recent advances in world models, video generation, and Vision-Language-Action (VLA) policies have motivated the development of World-Action Models (WAMs)<n>This survey provides a robotics-oriented review of WAMs.<n>We clarify their scope relative to conventional world models, model-based reinforcement learning, action-conditioned video generation, and reactive VLA policies, and organize existing methods through a unified taxonomy.<n>We discuss key challenges in action alignment, world-action factorization, spatial and multi-view consistency, long-horizon memory, neural simulation for closed-loop policy learning, and efficient inference
arXiv Detail & Related papers (2026-09-13T16:35:56Z) - GigaWorld-1: A Roadmap to Build World Models for Robot Policy Evaluation [73.02743604295246]
WMBench is a benchmark constructed from real-robot teleoperation data and matched policy rollouts.<n>We analyze 7 video world models, 4 action representation schemes, and over 324,000 simulated policy rollouts paired with real robot executions.<n>Our experiments deliver three core insights: evaluator quality is dominated by long-horizon, action-faithful rollout consistency rather than short-term visual realism.
arXiv Detail & Related papers (2026-07-02T17:02:43Z) - 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) - WestWorld: A Knowledge-Encoded Scalable Trajectory World Model for Diverse Robotic Systems [30.47929124291285]
Trajectory world models play a crucial role in robotic dynamics learning, planning, and control.<n>We introduce WestWorld, a knoWledge-Encoded Scalable Trajectory World model for diverse robotic systems.<n>WestWorld achieves significant improvements over competitive baselines in zero- and few-shot trajectory prediction.
arXiv Detail & Related papers (2026-03-15T14:12:43Z) - Large Video Planner Enables Generalizable Robot Control [117.49024534548319]
General-purpose robots require decision-making models that generalize across diverse tasks and environments.<n>Recent works build robot foundation models by extending multimodal large language models (LMs) with action outputs, creating vision--action (VLA) systems.<n>We explore an alternative paradigm of using large-scale video pretraining as a primary modality for building robot foundation models.
arXiv Detail & Related papers (2025-12-17T18:35:54Z) - WorldGym: World Model as An Environment for Policy Evaluation [41.204900701616914]
WorldGym is an autoregressive, action-conditioned video generation model which serves as a proxy to real world environments.<n> Policies are evaluated via Monte Carlo rollouts in the world model, with a vision-language model providing rewards.<n>We show that WorldGym is able to preserve relative policy rankings across different policy versions, sizes, and training checkpoints.
arXiv Detail & Related papers (2025-05-31T15:51:56Z) - Robotic World Model: A Neural Network Simulator for Robust Policy Optimization in Robotics [50.191655141020505]
This work advances model-based reinforcement learning by addressing the challenges of long-horizon prediction, error accumulation, and sim-to-real transfer.<n>By providing a scalable and robust framework, the introduced methods pave the way for adaptive and efficient robotic systems in real-world applications.
arXiv Detail & Related papers (2025-01-17T10:39:09Z) - Robot Utility Models: General Policies for Zero-Shot Deployment in New Environments [26.66666135624716]
We present Robot Utility Models (RUMs), a framework for training and deploying zero-shot robot policies.
RUMs can generalize to new environments without any finetuning.
We train five utility models for opening cabinet doors, opening drawers, picking up napkins, picking up paper bags, and reorienting fallen objects.
arXiv Detail & Related papers (2024-09-09T17:59:50Z) - Transferring Foundation Models for Generalizable Robotic Manipulation [82.12754319808197]
We propose a novel paradigm that effectively leverages language-reasoning segmentation mask generated by internet-scale foundation models.<n>Our approach can effectively and robustly perceive object pose and enable sample-efficient generalization learning.<n>Demos can be found in our submitted video, and more comprehensive ones can be found in link1 or link2.
arXiv Detail & Related papers (2023-06-09T07:22:12Z) - RT-1: Robotics Transformer for Real-World Control at Scale [98.09428483862165]
We present a model class, dubbed Robotics Transformer, that exhibits promising scalable model properties.
We verify our conclusions in a study of different model classes and their ability to generalize as a function of the data size, model size, and data diversity based on a large-scale data collection on real robots performing real-world tasks.
arXiv Detail & Related papers (2022-12-13T18:55:15Z)
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.