Embodied AI & Robotics

DreamX repurposes a pretrained video diffusion model to extract compact latent actions that reconstruct future video and transfer across human and robot embodiments.

RoboColosseum is a distributed in-the-wild robot benchmark that evaluates VLA policies and frontier agents across four embodiments via Open and Fine-tune tracks, revealing weak cross-embodiment ranking agreement and sample-efficient active matchmaking.

uni-robot-agent is a unified, modular harness that lets open-weight models perform competitive robot use in simulation and the real world, and the paper links effective robot use to four general capabilities.

REA is a routed embodied agent runtime that coordinates a slow System 2 brain, a router and fast System 0 policies so that no running policy waits on the brain.

HiFOR couples multi-horizon future prediction (short-term state, semantic milestone, goal) with action-guided retrieval from a structured latent world to condition a flow-matching VLA policy during denoising.

OSWAM learns compact imagination tokens from a video DiT as structured start points for a one-step action DiT denoising, achieving competitive planning and manipulation results with 1 action denoising step and substantially lower latency than multi-step world action models.

CaPtain is an open post-training pipeline (SFT on synthetic in-harness trajectories, then multi-turn RL with a privileged simulator critic) that turns a 4B open VLM into a code-as-policy robot agent operating over 22 low-level perception and motion primitives, reaching 73.8% on held-out LIBERO.

WorldBridge is a World Action Model that couples separate video and action experts through structured attention and joint flow matching, and its supervision study shows OOD videos raise RoboTwin 2.0 OOD success from 25.07% to 55.90% with sparse action labels further raising it to 70.70%.

S2 is a training-free offline metric that measures nearest-neighbor overlap between sensory features and end-effector trajectories and correlates with robot task success across model families.

CausGround distills interaction trajectories into reusable action-centered causal knowledge over a factorized world state, then grounds task-relevant preconditions and effects to guide frozen MLLM planners without parameter updates.

HuGe trains humanoid generalist VLAs on over 2,000 hours of egocentric human and robot data through a three-stage pipeline, using SONIC motion-token actions and data adaptation to achieve state-of-the-art HumanoidArena results and improved real-world G1 pick-and-drop success.