SimForcing: Distilling Simulation Motion Priors into Real-Domain Robot World Models
cs.RO, cs.AI, cs.CV
Submitted: 2026-10-05
Updated: 2026-10-05
Comments: Code: https://github.com/Wang-Xiaodong1899/SimForcing
Code: https://github.com/Wang-Xiaodong1899/SimForcing
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Cosmos World Foundation Model Platform for Physical AI
- Cosmos-Transfer1: Conditional World Generation with Adaptive Multimodal Control
- World Simulation with Video Foundation Models for Physical AI
- Masked Visual Actions for Unified World Modeling
- Persistent Robot World Models: Stabilizing Multi-Step Rollouts via Reinforcement Learning
- UniT: Toward a Unified Physical Language for Human-to-Humanoid Policy Learning and World Modeling
- RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation
- FlowWAM: Optical Flow as a Unified Action Representation for World Action Models
- ABot-PhysWorld: Interactive World Foundation Model for Robotic Manipulation with Physics Alignment
- WoW: Towards a World omniscient World model Through Embodied Interaction
- GeniWorld: A Generalizable Interactive World Model for Robotic Manipulation via Visual Actions
- Unified 4D World Action Modeling from Video Priors with Asynchronous Denoising
- VLAW: Iterative Co-Improvement of Vision-Language-Action Policy and World Model
- Video Prediction Policy: A Generalist Robot Policy with Predictive Visual Representations
- PointWorld: Scaling 3D World Models for In-The-Wild Robotic Manipulation
- DreamGen: Unlocking Generalization in Robot Learning through Video World Models
- EnerVerse-AC: Envisioning Embodied Environments with Action Condition
- WoVR: World Models as Reliable Simulators for Post-Training VLA Policies with RL
- Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning
- VAG: Dual-Stream Video-Action Generation for Embodied Data Synthesis
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