1 citations · 2 across the 13 of their papers we have counts for
14 papers
WorldOracle: Benchmarking Action-Conditioned World Models against Real-World Executions
Haoyi Duan, Liliang Chen, Nan Wang +13
We introduce WorldOracle, a benchmark specifically designed to evaluate action-conditioned world models (ACWMs) against paired real-world executions. Given the same initial observa…
SocialReasonBench: A Video-QA Benchmark for Social Reasoning with Counterfactual Narrative Videos
Zheyu Huang, Zijing Shi, Haozhe Luo +4
Recent advances in Large Multimodal Models (LMMs) have greatly improved video understanding, yet their ability to reason about human-centered social situations remains limited. Exi…
PhysisForcing: Physics Reinforced World Simulator for Robotic Manipulation
Peiwen Zhang, Yufan Deng, Shangkun Sun +11
Video generation models have emerged as a promising paradigm for embodied world simulation. However, both general-domain video generators and robot-specific data fine-tuned models…
SC3-Eval: Evaluating Robot Foundation Models via Self-Consistent Video Generation
Wei-Cheng Tseng, Gashon Hussein, Yuzhu Dong +9
Evaluating generalist robot manipulation policies in the real world is expensive, slow, and difficult to scale. Action-conditioned video world models offer a scalable alternative b…
Polar: Agentic RL on Any Harness at Scale
Binfeng Xu, Hao Zhang, Shaokun Zhang +9
Reinforcement learning for language agents increasingly depends on custom harnesses that manage long-running context, multi-turn tool use and multi-agent orchestration. However, po…
VLM-AutoDrive: Post-Training Vision-Language Models for Safety-Critical Autonomous Driving Events
Mohammad Qazim Bhat, Yufan Huang, Niket Agarwal +7
The rapid growth of ego-centric dashcam footage presents a major challenge for detecting safety-critical events such as collisions and near-collisions, scenarios that are brief, ra…
DreamDojo: A Generalist Robot World Model from Large-Scale Human Videos
Shenyuan Gao, William Liang, Kaiyuan Zheng +27
Being able to simulate the outcomes of actions in varied environments will revolutionize the development of generalist agents at scale. However, modeling these world dynamics, espe…
Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning
Moo Jin Kim, Yihuai Gao, Tsung-Yi Lin +8
Recent video generation models demonstrate remarkable ability to capture complex physical interactions and scene evolution over time. To leverage their spatiotemporal priors, robot…
PointWorld: Scaling 3D World Models for In-The-Wild Robotic Manipulation
Wenlong Huang, Yu-Wei Chao, Arsalan Mousavian +4
Humans anticipate, from a glance and a contemplated action of their bodies, how the 3D world will respond, a capability that is equally vital for robotic manipulation. We introduce…
Openpi Comet: Competition Solution For 2025 BEHAVIOR Challenge
Junjie Bai, Yu-Wei Chao, Qizhi Chen +13
The 2025 BEHAVIOR Challenge is designed to rigorously track progress toward solving long-horizon tasks by physical agents in simulated environments. BEHAVIOR-1K focuses on everyday…
Scalable Policy Evaluation with Video World Models
Wei-Cheng Tseng, Jinwei Gu, Qinsheng Zhang +4
Training generalist policies for robotic manipulation has shown great promise, as they enable language-conditioned, multi-task behaviors across diverse scenarios. However, evaluati…
Alpamayo-R1: Bridging Reasoning and Action Prediction for Generalizable Autonomous Driving in the Long Tail
NVIDIA, :, Yan Wang +41
End-to-end architectures trained via imitation learning have advanced autonomous driving by scaling model size and data, yet performance remains brittle in safety-critical long-tai…
World Simulation with Video Foundation Models for Physical AI
NVIDIA, :, Arslan Ali +87
We introduce [Cosmos-Predict2.5], the latest generation of the Cosmos World Foundation Models for Physical AI. Built on a flow-based architecture, [Cosmos-Predict2.5] unifies Text2…
DreamGen: Unlocking Generalization in Robot Learning through Video World Models
Joel Jang, Seonghyeon Ye, Zongyu Lin +25
We introduce DreamGen, a simple yet highly effective 4-stage pipeline for training robot policies that generalize across behaviors and environments through neural trajectories - sy…