Embodied AI

OpenAI has listed more than a dozen San Francisco robotics roles for OpenAI Robotics, a team that evolved from Aditya Ramesh’s Worldsim work, with the actuator design engineer role offering $342,000 to $445,000 in base cash pay plus PPU incentives.

VAST released Project Eden, a three-layer world-model architecture that separates persistent state evolution from visual rendering, and disclosed nearly $200 million across its A+ and A++ funding rounds.

CMG Lion Rock AI Lab released the LiOS edge-cloud architecture for embodied robotics, reporting about 30 ms one-way latency from local camera to cloud GPU memory in cross-machine tests, and open-sourced the low-latency video transmission module plus the LeFold laundry-folding dataset.

VAST raised nearly $200 million in A+ and A++ rounds and disclosed Project Eden, a world model architecture that separates state evolution from visual rendering through a structured state layer, a conditional interface layer, and a generative rendering layer.

Runway joined Cosmos Coalition as a founding member and will co-develop the first open world-model foundation model for physical AI with NVIDIA.

NVIDIA released Cosmos 3 as an open physical AI generalist model with native visual reasoning, world generation, and action generation, offering two variants: Super at 32B parameters and Nano at 8B parameters.

MWC26 Shanghai will host a humanoid robot penalty shootout in June 2026, with eight Chinese embodied intelligence teams competing under rules that require autonomous play without human control or preset scripts.

OpenAI formed the OpenAI Robotics team and is hiring full-stack hardware, systems, and ML engineers; Aditya Ramesh leads the project, with a near-term focus on supporting skilled workers, while the post does not disclose hiring scale.

MouShen Intelligence released STI-WM, a spatiotemporally integrated world-action model for robotics, claiming support for RGB, point-cloud, and proprioceptive inputs, hundred-second task planning, and disclosing five funding rounds in six months plus a RMB 300 million Pre-A round.

Moushen Intelligence released STI-WM, a spatiotemporally integrated world action model for robotics, with RGB, depth point cloud, and proprioceptive inputs; the post says it supports hundred-second-scale long-horizon task rollout and closed-loop replanning, but does not disclose benchmark scores or deployment costs.

Tesla FSD V14.3.3 completed a 6,051 km zero-intervention drive from Vancouver to Halifax in 4 days and 21 hours, with the system handling lane changes, complex road conditions, and parking without disengagements or human corrections.

The FT headline says the UK military is examining lethal strikes without human approval, but the accessible body is a subscription page and does not disclose the weapon types, approval mechanism, legal conditions, or deployment timeline.

NVIDIA, Tsinghua, University of Toronto, and Vector Institute released Gamma-World, a multi-agent world model using simplex-based positional encoding and hub tokens to cut interaction cost from quadratic to linear, with 8-player latency dropping from 17.6 ms to 4.5 ms.

Shift said it would clean New Yorkers’ homes for free if it can film cleaners doing chores such as washing dishes, wiping counters, dusting tables, and mopping floors, creating domestic robot training data; the snippet does not disclose consent terms, data retention, pricing, or expansion timing for cities such as London.

Shift offers free home cleaning and records cleaners scrubbing, vacuuming, dusting, tidying, and washing to collect robot training footage; the RSS snippet does not disclose service cities, privacy terms, consent mechanics, or dataset scale.

Tesla claimed FSD can be up to 10 times safer than humans, but Reuters found flaws in the comparison, with 11 traffic safety researchers saying Tesla used inappropriate baselines against broader federal crash data.

NVIDIA Research presented 8 ICRA papers on sim-to-real robotics: ScheduleStream delivered a 3x speedup for multi-arm planning, COMPASS reached about 80% success across 20 real-world navigation trials, and Grasp-MPC achieved about 75% real-robot grasping success.

Mistral AI integrated the Emmi AI team and launched a physics AI foundation model for industrial engineering, with the post saying it can learn from geometry, boundary conditions, or measurement data and predict full physical fields on a single GPU in seconds.

NTU AutoMan Lab, Harvard, and Xiaomi Auto proposed AutoMoT, a unified VLA driving model using a 4B Qwen3-VL Understanding Expert and a 1.6B Action Expert with asynchronous inference, reaching 89.42 DS and 74.09% SR on Bench2Drive with AutoMoT+.

X Square Robot open sourced Wall-OSS-0.5, a VLA model whose 400k pretraining checkpoint scored above 80 on 4 of 17 real-robot zero-shot tasks, with weights, code, training recipe, ablations, and a DMuon optimizer implementation released.