Zeva: In-Context Causal Learning for Generalizable Embodied Manipulation
cs.RO
Submitted: 2026-08-31
Updated: 2026-09-22
Project page: https://air-embodied-brain.github.io/Zeva
Terminology
Sources
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
- RT-1: Robotics Transformer for Real-World Control at Scale
- Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning
- Causal World Modeling for Robot Control
- World Action Models are Zero-shot Policies
- RoboTTT: Context Scaling for Robot Policies
- WAM-TTT: Steering World-Action Models by Watching Human Play at Test Time
- $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
- $\pi^{*}_{0.6}$: a VLA That Learns From Experience
- GR-2: A Generative Video-Language-Action Model with Web-Scale Knowledge for Robot Manipulation
- FAST: Efficient Action Tokenization for Vision-Language-Action Models
- PACT: Perception-Action Causal Transformer for Autoregressive Robotics Pre-Training
- Action-Effect Memory Pretraining for Robot Manipulation
- ALAM: Algebraically Consistent Latent Action Model for Vision-Language-Action Models
- UniVLA: Learning to Act Anywhere with Task-centric Latent Actions
- MEM: Multi-Scale Embodied Memory for Vision Language Action Models
- DIM-WAM: World-Action Modeling with Diverse Historical Event Memory
- MemoryWAM: Efficient World Action Modeling with Persistent Memory
- VLA-RL: Towards Masterful and General Robotic Manipulation with Scalable Reinforcement Learning
- Fast-WAM: Do World Action Models Need Test-time Future Imagination?
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