New framework for scalable robotic reward modeling using trajectory comparisons to train general-purpose reward models.

A single generalist VLA built on Qwen3.5-4B + 1.15B DiT flow-matching action decoder that unifies manipulation, navigation, and trajectory prediction across 11 embodiments via text-described embodiment prompts. Trained in four stages and outperforms task-specific specialists on real ALOHA and sim benchmarks without per-task fine-tuning.

Extends the EXPO method with real-world RL post-training for VLAs using image observations, action chunking, DAgger, and on-the-fly Q-value maximization. Achieves 30/30 success on 8 challenging manipulation tasks with only ~19 min of RL data on average.

RoboMeter trains dense reward models from both successful and failed robot trajectories, solving a key gap in prior methods that only learn from expert demos.

Browser-hosted mobile environment with JSON state, deterministic judges, and 256 parallel rollouts. Reports +40.7 real-device points after GRPO training on 416 tasks for GUI agent development.

Introduces a method to transfer a Unitree G1 foundation policy (Gear-Sonic) to LimX Oli/Luna humanoids using only 1% of the original compute/data. Achieves fast convergence and strong tracking performance for humanoid whole-body control.

Outputs physics-engine-compatible triangle meshes directly from sparse, unposed images without Gaussian splatting or post-processing.

Releases a benchmark suite for systematically evaluating memory in Vision-Language-Action policies on tabletop manipulation tasks.

Introduces large-scale 3D world models pretrained on diverse real-world video to enable robust robotic manipulation policies that generalize beyond simulation.

New LeRobot-based bimanual pegboard manipulation dataset with 52 episodes, 30k frames, 3 camera views, and 14-DOF arms for VLA evaluation. Provides standardized benchmark for vision-language-action model assessment.