IronMan: Information-Constrained Video-Action Learning for Robot Manipulation
cs.RO
Submitted: 2026-10-06
Updated: 2026-10-06
Sources
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
- RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
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- RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation
- $\pi^{*}_{0.6}$: a VLA That Learns From Experience
- ${\pi}_{0.7}$: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities
- OpenVLA: An Open-Source Vision-Language-Action Model
- Causal World Modeling for Robot Control
- Video Generators are Robot Policies
- Flow Matching for Generative Modeling
- Being-H0.7: A Latent World-Action Model from Egocentric Videos
- DiT4DiT: Jointly Modeling Video Dynamics and Actions for Generalizable Robot Control
- Cosmos World Foundation Model Platform for Physical AI
- GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
- $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
- StarVLA: A Lego-like Codebase for Vision-Language-Action Model Developing
- World Guidance: World Modeling in Condition Space for Action Generation
- Wan: Open and Advanced Large-Scale Video Generative Models
- ST-WAM: Semantic-Temporal World Action Model for Robust Manipulation under Visual Distribution Shifts
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