vla

Presents ForeWAM, a direct-policy World Action Model that provides predictive context to action generation via hidden future-slot KV states, avoiding explicit future video decoding while achieving high success rates on LIBERO benchmarks.

Xiaomi announces Robotics-1, a new robot foundation model with a VLA architecture trained on 100K+ hours of real-world robot data, featuring cross-embodiment generalization and fast adaptation to new tasks.

Introduces W2-VLA, a vision-language-action model for fine-grained robot manipulation that models task-conditioned future wrist observations, achieving improved manipulation performance on benchmarks and real-world tasks.

MIT researchers developed VLASH, a method enabling vision-language-action models to predict future robot states, doubling speed and reducing lag in tasks like pick-and-place and table tennis.

Anchor-Align augments behavioral cloning with vision-language anchoring to preserve pretrained representations and language-action alignment, improving real-robot success rates by over 20% on xArm7 and showing consistent gains in simulation benchmarks.

CLAP proposes a method to convert pretrained vision-language models (VLMs) into vision-language-action models (VLAs) by prepending natural-language action descriptions to action token sequences, preserving semantic capabilities without architectural changes. It achieves 90.8% on LIBERO and improves robustness.

This paper introduces robot-centric pointmaps, which encode 3D scene coordinates in the robot frame directly into image pixels, to resolve the frame mismatch between camera observations and robot action definitions in Vision-Language-Action models. The method improves Pi0.5 and SmolVLA on RoboCasa and generalizes better to unseen camera placements in real-robot experiments.

LingBot-VLA 2.0, an open-source embodied foundation model, has been released with 60,000 hours of pretraining data supporting 20 robot configurations across 17 brands, capable of sub-130ms inference on RTX 4090.

This paper presents Semantic Action RL, which uses reinforcement learning over Vision-Language-Action (VLA) prompts to enable robots to learn new tasks quickly in the real world.

Introduces Neuro-Symbolic Drive, a framework that uses rule-grounded reasoning traces from classical planners to fine-tune a driving VLA (Qwen3.5-4B), achieving significant reductions in average displacement error and miss rate compared to standard CoT reasoning.

This paper introduces PersonaDrive, a pipeline that conditions a vision-language-action (VLA) driving agent on retrieved demonstrations from a style-instructed human driving dataset, enabling style-diverse non-ego agents for closed-loop simulation and improving driving scores on Bench2Drive.

Robot world models and simulation platforms are experiencing open-source acceleration: NVIDIA launched Cosmos 3 and Isaac GR00T physical AI foundation models, AGIBOT released Genie Sim 3.0, a fully open-source simulation platform, VLA models become mainstream for manipulation policies, collectively lowering the entry barrier for the robotics field.

AffordanceVLA introduces a unified framework using structured affordance forecasting as an intermediate representation to improve perception-action mapping in robotic manipulation, leveraging vision-language models and a Mixture-of-Transformer architecture.

X Square Robot releases Wall-OSS-0.5, a 4B open-source VLA robot foundation model evaluated on a 17-task real-robot zero-shot suite without task-specific fine-tuning, aiming to directly measure pretraining capability.

Release of Wall-OSS-0.5, an open-weights vision-language-action model that achieves over 80% task progress on 4 of 17 real-robot tasks with zero fine-tuning, including on a deformable rope task not seen during pretraining. The model preserves general vision-language ability while improving embodied grounding.

FrameSkip is a data-layer frame selection method that improves Vision-Language-Action (VLA) policy training by prioritizing high-importance frames based on action variation and visual-coherence metrics, achieving a macro-average success rate of 76.15% across three benchmarks while using only 20% of unique frames.

NVIDIA's Jim Fan spoke at Sequoia AI Ascent 2026, declaring the VLA architecture obsolete and proposing World Action Models (WAM) as a new paradigm for robotics. He introduced key technologies including DreamZero, EgoScale, and the neural simulator Dream Dojo.

Allen AI releases MolmoAct2, an open-weight Vision-Language-Action model designed for real-world robotic deployment, featuring new datasets, an open action tokenizer, and adaptive reasoning to reduce latency.

NVIDIA and Hugging Face publish a hands-on demo showing Gemma 4 running as a vision-language-action model entirely on the Jetson Orin Nano Super, using local STT/TTS and webcam input.

FlashDrive reduces reasoning vision-language-action model inference latency from 716 ms to 159 ms on RTX PRO 6000—up to 5.7× faster—with zero accuracy loss, enabling real-time autonomous applications.