Tagged articles

Cross-Embodiment

5 articles · Page 1 of 1

Sep 24, 2026 · Artificial Intelligence

OpenWAM: Open-Source World-Action Model Stack for Robotics

Researchers from seven universities open-source OpenWAM, a modular world-action model stack combining video generation priors with robot action learning, featuring controlled experiments on architecture, information flow, and cross-embodiment pretraining, achieving state-of-the-art results on eight simulation benchmarks and real-robot tasks.

Cross-EmbodimentPretrainingReal-Robot Experiments

0 likes · 21 min read

OpenWAM: Open-Source World-Action Model Stack for Robotics

Data Party THU

Sep 23, 2026 · Artificial Intelligence

Berkeley PhD Thesis: Building Generalist Robots via Data, Policy, Cross-Embodiment & Memory

Berkeley PhD thesis presents a four-part framework for generalist robots: BridgeData V2 dataset (60k trajectories), Octo policy (800k demos, 29% better zero-shot), CrossFormer (single policy across 20 embodiments, 73% success), and MEM (multi-scale memory for 15-min tasks).

BridgeData V2Cross-EmbodimentCrossFormer

0 likes · 19 min read

Berkeley PhD Thesis: Building Generalist Robots via Data, Policy, Cross-Embodiment & Memory

Machine Heart

Jul 23, 2026 · Artificial Intelligence

Rendering Robot Actions as Video for Cross‑Embodiment Bidirectional Inference

The article reviews the Masked Visual Actions approach, which renders robot motion as pixel‑level masks for video world models, enabling both forward prediction of environment changes and inverse generation of robot actions, and demonstrates significant performance gains across multiple robotic tasks and embodiments.

AICross-EmbodimentRoboCasa

0 likes · 10 min read

Rendering Robot Actions as Video for Cross‑Embodiment Bidirectional Inference

Machine Heart

Jun 15, 2026 · Artificial Intelligence

HyVLA-0.5: Sub‑millimeter UMI Data and Real‑Robot Reinforcement Eliminate Heavy Tele‑operation

HyVLA-0.5, an open‑source embodied VLA model from Tencent Robotics X, leverages over 10,000 hours of sub‑millimeter UMI demonstration data and a novel FlowPRO reinforcement pipeline to achieve more than 90% success on simulated and real‑world tasks, while supporting cross‑embodiment transfer and asynchronous deployment.

Cross-EmbodimentFlowPROHyVLA-0.5

0 likes · 16 min read

HyVLA-0.5: Sub‑millimeter UMI Data and Real‑Robot Reinforcement Eliminate Heavy Tele‑operation

Amap Tech

Oct 5, 2025 · Artificial Intelligence

Can One Navigation Brain Power All Robots? Inside CE-Nav’s Cross‑Embodiment Breakthrough

CE-Nav introduces a two‑stage imitation‑then‑reinforcement framework that decouples generic geometric planning from robot‑specific dynamics, enabling low‑cost, high‑performance navigation across quadrupeds, humanoids, and drones while requiring only brief online fine‑tuning.

Cross-EmbodimentImitation LearningReinforcement Learning

0 likes · 11 min read