Larger models + more thinking + more context improve performance on some prompts but not others; a learned router enables better performance/latency trade-offs.

RoboGenesis generates 10K+ lab scenes across 16 robot embodiments; LabVLA (Qwen3-VL + DiT flow-matching) achieves 71.1% success on LabUtopia and transfers to real Franka arms.

GPT-style Transformer pretrained on 2 billion motion frames that achieves agile, generalist zero-shot control on a real Unitree G1 humanoid for tasks like soccer, dancing, and digging. Requires no fine-tuning or task-specific adaptation.

Finetunes a single Vision-Language-Action (VLA) foundation model so that any robot in a team can control any other. Outperforms both per-robot specialists and a monolithic centralized policy while scaling to large teams.

Trains a compositional world model on real robot data to enable closed-loop policy improvement via future prediction and progress evaluation, bypassing both risky real-world RL and traditional sim-to-real gaps.

Open-source 3B unified embodied foundation model trained on 1.5M interleaved vision-text-action samples for perception, planning, and acting.

A single vision-language model that unifies reasoning, task planning, and human-robot interaction for complex instructions and long-horizon tasks.

Presents a neural network-based world model for model-based reinforcement learning in robotics, focusing on sim-to-real transfer for quadrupedal and humanoid robots. Enables robust policy optimization through learned environment simulation.

Embodied egocentric simulation framework that controls first-person worlds with 3D human motion and customizes evolving scenes via pose-anchored views.

Few-step auto-regressive diffusion model that converts broken 3D reconstructions into fully realized scenes, outperforming prior methods by 1-3 dB PSNR.