Research Papers Topics
Learning and control for physical robots.
World Models · Interactive world models vs World models for agents
Kairos runs a 4B world model on one RTX5090 (PAI-Bench 80.84); WALL-WM grounds robot actions in events (75.86 vs pi0.5's 55.64); Gamma-World runs four players at 24 FPS.
Vision-Language-Action · RT-2 vs π0 vs Qwen-VLA vs MolmoAct2 vs World Pilot
RT-2 wrote actions as text tokens, π0 switched to ~50Hz flow-matching chunks, and 2026 models add 3D reasoning or world-model priors. Simulation is nearly saturated; real-robot margins and OOD shifts differentiate.
World Models · World models vs LLMs
An LLM reasons over rules and goals in language; a world model predicts what a scene does next. The 2026 evidence says they are complementary, and the hard part is knowing when a generated future is credible.
DragMesh-2 opens doors and drawers with a 51-DoF hand and no actuator on the object joint, so motion comes only from contact. PICA training hits 0.89 success at nominal damping and 0.56 at 4x, with no tactile sensing.
RATs lets a robot play before it is told what to do: agent teams propose their own goals, write code-as-policy, and bank wins as reusable skills. The library lifts LIBERO-PRO success from 23.2% to 43.8%.
Kairos packs a world model into 4B params with linear-time Hybrid Linear Temporal Attention, leads PAI-Bench at 80.84, and runs a 480P rollout in 11.4s on one RTX5090.
Vision-Language-Action · CASIA
World Pilot adds two world-model priors to a VLA policy and hits 84.7% total success on LIBERO-Plus zero-shot OOD, up 4.2 over the ABot-M0 base, and the scene prior works even from a world model never action-trained.
Vision-Language-Action · ACE Robotics
ACE-Ego-0 pretrains a VLA on 6,000+ hours mixing robot trajectories with human egocentric video turned into pseudo-actions. It averages 78.3% on six real bimanual tasks vs 71.7% for pi-0.5 and 35.6% for GR00T-N1.7.
OmniDreams is a Cosmos-based generative driving simulator that renders 68 FPS single-view on one GB300 and replaces NuRec in a closed-loop AV stack, cutting closed-loop incidents from 10.1% to 4.7%.
Vision-Language-Action · X Square Robot
WALL-WM organizes VLA pretraining around semantic action events, not fixed-length chunks. Its event mode scores 75.86 Task Progress on diverse real-robot manipulation versus 55.64 for pi0.5.
World Models · Alibaba Qwen Team
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.
Vision-Language-Action · Zhejiang University
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 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 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 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? turns active 3D viewpoint reproduction into a checkable test, with concrete failure signals, benchmark limits, and builder takeaways.
Robotics · Tsinghua University
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 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 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 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 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, 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
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 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 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.