5arXiv cs.AI (Artificial Intelligence)·
FurnitureVLA: Vision-Language-Action model for long-horizon bimanual furniture assembly
Researchers introduce FurnitureVLA, a Vision-Language-Action model system for real-scale bimanual robot furniture assembly, addressing tasks with up to 7 subtasks and 1550 control steps. The approach combines a progress-enhanced VLA finetuned on semantically grounded subtasks with joint action and continuous progress signal prediction to enable automatic subtask transitions and reduce compounding errors. The system improves simulation success from 48% to 80% over baselines across three furniture types and validates on a real Kinova Gen3 robot platform. This represents the first systematic study of real-scale bimanual furniture assembly using VLAs.
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CamVLA: Calibration-free view-robust Vision-Language-Action model for robot deployment
Researchers from Alibaba DAMO Academy introduce CamVLA, a Vision-Language-Action model that eliminates the need for explicit camera calibration during robot deployment. The model decouples manipulation controls from camera geometry by predicting camera-centric end-effector actions and a 6-DoF hand-eye matrix, composing them into robot base-frame actions via deterministic geometric transformation. Operating on a single monocular RGB image without depth or calibration data, CamVLA improves success rates across diverse unseen viewpoints in both simulation and real-world evaluations.
LabVLA: Vision-Language-Action model and RoboGenesis data engine for scientific laboratory robotics
Researchers introduce LabVLA, a Vision-Language-Action model designed to bridge written scientific protocols and physical robot execution in laboratory settings. To address the data scarcity problem, they build RoboGenesis, a simulation-based data engine that composes lab workflows from atomic skills and generates structured demonstrations across robot embodiments. LabVLA uses a two-stage training recipe combining FAST action token pretraining on a Qwen3-VL-4B-Instruct backbone with flow matching posttraining via a DiT action expert. On the LabUtopia benchmark, LabVLA achieves the highest average success rate among evaluated baselines in both in-distribution and out-of-distribution settings.
InSight: Self-guided autonomous skill acquisition for vision-language-action models via primitive steerability
InSight is a framework enabling VLA models to autonomously acquire new manipulation skills beyond their training data by decomposing demonstrations into labeled primitive actions (e.g., 'move gripper to bowl', 'pour the bottle') and running a VLM-guided data flywheel that identifies missing primitives, attempts demonstrations, and integrates successful ones back into training. The system requires no human demonstrations of target skills and is evaluated on simulation and real-world tasks including block flipping, drawer closing, sweeping, and pouring. Learned primitives can be composed for novel long-horizon tasks, offering a practical path toward continual skill acquisition in robotic VLA policies.
Two-stage action prior pretraining improves cross-embodiment VLA robot manipulation
Researchers propose a two-stage training framework for Vision-Language-Action (VLA) models that pretrains the action module with motion priors before cross-modal alignment begins. Stage 1 uses a flow-matching-based encoder-decoder to learn temporal motion structure from unconditioned action trajectories alone; Stage 2 transfers this prior to VLA training via decoder reuse and latent distillation. Evaluated across 13 cross-embodiment tasks in simulation and real-world settings, the approach achieves faster convergence, higher success rates, and notably better performance in data-scarce real-world scenarios compared to VLA training without action priors.
TempoVLA: Speed-Controllable Vision-Language-Action Policy for Robot Manipulation
Researchers introduce TempoVLA, a Vision-Language-Action model that enables explicit speed control during robot manipulation by conditioning on a speed signal rather than inheriting a fixed speed from training data. The system pairs Variable-Speed Trajectory Augmentation (VSTA), which re-times demonstrations by merging or splitting actions, with a model-side conditioning mechanism. Experiments in simulation and real-world tasks show flexible bidirectional speed control, with dynamic adaptation—accelerating in low-risk transit phases and decelerating for high-risk contact stages—achieved by coupling with a large multimodal model.
Qwen-VLA: Unified Vision-Language-Action Model Across Robot Tasks, Environments, and Embodiments
Alibaba's Qwen team presents Qwen-VLA, a unified embodied foundation model that extends the Qwen vision-language stack to continuous action and trajectory generation via a DiT-based action decoder. The model is jointly pretrained on diverse data spanning manipulation trajectories, egocentric demonstrations, synthetic simulation, and navigation data, with embodiment-aware prompt conditioning to support multiple robot platforms. A unified action-and-trajectory prediction framework covers manipulation, navigation, and trajectory prediction tasks. Benchmarks show strong results: 97.9% on LIBERO, 73.7% on Simpler-WidowX, 69.0% OSR on R2R navigation, and 76.9% average OOD success in real-world ALOHA experiments.
SmolVLA: Efficient Vision-Language-Action Model trained on Lerobot Community Data
Hugging Face introduces SmolVLA, a compact Vision-Language-Action model designed for robotics control, trained on community-contributed data from the LeRobot ecosystem. The model targets efficient deployment on resource-constrained hardware while maintaining competitive manipulation performance. This release represents a continuation of Hugging Face's strategy to democratize robotics AI through open community data pipelines.
CHORUS: Single VLA policy enables decentralized multi-robot collaboration without inter-robot communication
CHORUS is a framework that adapts a single vision-language-action (VLA) backbone to control diverse multi-robot teams in a fully decentralized manner, with each robot running an independent copy conditioned only on its own observations and a robot-identifying prompt. Real-world experiments across tasks like tape measurement, book handovers, and laundry basket lifting show a 64-percentage-point improvement over decentralized from-scratch models and 40-point improvement in reactivity to teammate behavior, while outperforming centralized baselines. The key insight is that pretrained VLA visuomotor priors are sufficient to enable reactive coordination without explicit inter-robot communication or alignment procedures at inference time.