When to Trust Imagination: Adaptive Action Execution for World Action Models
cs.RO, cs.AI
Submitted: 2026-05-07
Updated: 2026-09-27
Terminology
Sources
- Motus: A Unified Latent Action World Model
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
- RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation
- Towards Human-level Intelligence via Human-like Whole-Body Manipulation
- ThriftyDAgger: Budget-Aware Novelty and Risk Gating for Interactive Imitation Learning
- Mixture of Horizons in Action Chunking
- Causal World Modeling for Robot Control
- Unified Video Action Model
- Video Generators are Robot Policies
- Adaptive Action Chunking at Inference-time for Vision-Language-Action Models
- F1: A Vision-Language-Action Model Bridging Understanding and Generation to Actions
- mimic-video: Video-Action Models for Generalizable Robot Control Beyond VLAs
- SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis
- SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics
- VLA Knows Its Limits: Adaptive Execution Horizons for Robot Policies
- Open-Loop Planning, Closed-Loop Verification: Speculative Verification for VLA
- Speedup Patch: Learning a Plug-and-Play Policy to Accelerate Embodied Manipulation
- GigaWorld-Policy: An Efficient Action-Centered World--Action Model
- Latent Action Pretraining from Videos
- World Action Models are Zero-shot Policies
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