World Models · Alibaba Qwen Team
ABot-Earth 0.5: Generating 3D Cities From Satellite Images
ABot-Earth 0.5 uses satellite imagery to generate 3D Gaussian Splatting city scenes, reporting under 10 minutes per square kilometer and FID 16.1.
Topics
Robotics
Learning and control for physical robots.
Vision-Language-Action · Zhejiang University
LabVLA: A VLA Model for Scientific Lab Robots
LabVLA trains a Qwen3-VL-4B backbone plus DiT action expert on laboratory workflows and reports 71.1% ID and 70.0% OOD success on LabUtopia.
World Models · Independent Researcher
AnchorWorld: Egocentric World Simulation for Embodied AI
AnchorWorld: Egocentric World Simulation for Embodied AI turns egocentric world simulation into a checkable test, with concrete failure signals, benchmark limits, and builder takeaways.
World Models · Independent Researcher
Function2Scene: 3D Indoor Layout from Functional Specs
Function2Scene: 3D Indoor Layout from Functional Specs turns functional 3D scene layout into a checkable test, with concrete failure signals, benchmark limits, and builder takeaways.
AI Agents · Independent Researcher
SpatialWorld: Interactive Spatial Reasoning for Agents
SpatialWorld: Interactive Spatial Reasoning for Agents turns interactive spatial reasoning into a checkable test, with concrete failure signals, benchmark limits, and builder takeaways.
Robotics · Independent Researcher
TVRBench: Can Models Move to a Target Viewpoint?
TVRBench: Can Models Move to a Target Viewpoint? turns active 3D viewpoint reproduction into a checkable test, with concrete failure signals, benchmark limits, and builder takeaways.
Robotics · Tsinghua University
Humanoid-GPT: A GPT-Style Transformer for Humanoid Motion Tracking
Humanoid-GPT treats humanoid control like language modeling: a causal Transformer distilled from ~384 PPO experts on a 2-billion-frame corpus, 200x prior data. It hits 92.58 percent sim success, under 1.5ms.
Cosmos 3 Explained: NVIDIA's Omnimodal World Model for Physical AI
Cosmos 3 packs language, image, video, audio, and robot actions into one mixture-of-transformers model; NVIDIA reports it ranks first among open models on text-to-image, image-to-video, and RoboArena policy.
Vision-Language-Action · Allen Institute for AI
MolmoAct2: An Open Action Reasoning Stack for Real Robots
MolmoAct2 is an open vision-language-action stack that reasons in 3D before acting. On real-world DROID it hits 87.1% success, +38.7 points over the runner-up, and its Molmo2-ER brain beats GPT-5 and Gemini Robotics ER.
Vision-Language-Action · Shanghai AI Laboratory
PhysBrain 1.0: Turning Human Video into Physical Priors for Robots
PhysBrain 1.0 compiles human egocentric video into physics QA to pretrain a VLM, then adapts it to robot control — lifting Franka grasping from 47.1% to 63.3% over 50 trials versus a pi0.5 baseline.
Vision-Language-Action · Alibaba Qwen Team
Qwen-VLA: One Model for Manipulation, Navigation, and Trajectories
Qwen-VLA extends Qwen's vision-language stack with a DiT action decoder and embodiment-aware prompts to run manipulation, navigation, and trajectory prediction in one model — 97.9% on LIBERO and 69.0% OSR on R2R.
Vision-Language-Action · RLWRLD
RLDX-1: A Multi-Stream Vision-Language-Action Model for Dexterous Robots
RLDX-1, from RLWRLD and KAIST, adds motion, memory and tactile streams to a Qwen3-VL backbone. It catches fast-moving objects 87.5% of the time vs 29.2% for pi0.5, and beats GR00T N1.6 on LIBERO-Plus 86.7% to 72.6%.
Vision-Language-Action · ETH Zurich
Robots Need More than VLA and World Models: Four Missing Interfaces
A position paper from ETH Zurich, Stanford and TU Darmstadt argues scaling VLA and world models is not enough — robots need four interfaces to turn unstructured human and video behaviour into grounded supervision.
Vision-Language-Action · Physical Intelligence
π0 Explained: A Vision-Language-Action Flow Model for Robots
π0 bolts a flow-matching action expert onto a pretrained VLM, emitting ~50Hz action chunks so one policy can fold laundry, bus tables, and assemble boxes across single-arm, dual-arm, and mobile robots.
Vision-Language-Action · Google DeepMind
RT-2 Explained: Vision-Language-Action Models for Robot Control
RT-2 co-fine-tunes a web-pretrained vision-language model on robot trajectories, expresses actions as text tokens, and gets emergent generalization to novel objects, unseen commands, and basic reasoning across 6k trials.