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#Embodied AI

All collected and published items, ordered by time. Picks are a selected subset.
Models

New models and versions, open weights, and changes in capability and pricing, with their evaluations.

Today10/17Sat
  1. 量子位67

    UBTECH introduces Thinker-WM, unifying video, depth, and robot actions in one world model

    UBTECH introduces Thinker-WM, a multimodal world model that jointly models RGB video, depth, and robot actions through a shared Unified Dynamics Transformer. In material supplied by UBTECH and republished by QbitAI, the company reports leading results on WorldOlympiad, EWMBench, and WorldArena1.0 Track-1, including scores of 0.740 on WorldOlympiad and 4.66 on EWMBench. The model supports six capability modes through shared weights, and adding depth improved RoboTwin single-task success by 15 percentage points over a baseline without depth.

10/14Wed
  1. Google DeepMind · X73

    Gemini 4 Argon taught a robot to play cornhole just by watching a video

    Gemini 4 Argon taught a robot to play cornhole just by watching a video. It mapped the physics, coded its own simulation, and taught @Apptronik's Apollo humanoid to sink 7 throws in a row.

    Why it matters: The demonstration connects video observation, physics modeling, and a model-written simulation in a concrete example of teaching a humanoid robot a physical task.

  2. Google DeepMind82

    Google DeepMind Introduces Gemini 4 Argon for Robotics

    Google DeepMind introduces Gemini 4 Argon, a general-purpose physical reasoning model for robot control, simulation reconstruction, physical understanding, and self-improvement. Google DeepMind reports an 87% ERQA score using pure reasoning and a simulation-generated training example that raises real-world robot success from 56% to 75% while reducing completion time from 35s to 24s. Argon is available today to Google AI Ultra subscribers through Google AI Studio and Google Antigravity, and through the paid Gemini API tier using gemini-4-argon in the Interactions API.

    Why it matters: The simulation-to-hardware example provides a concrete basis for assessing how generated training episodes can improve robot task success and completion speed within a reported setup.

10/13Tue
  1. 量子位63

    MagicLab Studio’s EchoPolicy-0 tops MolmoSpaces Combined with 75.4% average success

    QbitAI reports that MagicLab Studio’s EchoPolicy-0 ranked first on MolmoSpaces Combined as of October 13, 2026, achieving 75.4% average success across all four v1 manipulation tasks in 3915 evaluation episodes. Its 3.4B-parameter MagicBrain ranked fourth with 72.4% average success and 94.4% grasping success. EchoPolicy-0 combines VLM-guided candidate trajectory selection with execution checks, while MagicBrain uses visual markers and post-training to connect target locations with actions without changing the VLA architecture.

10/11Sun
  1. The Decoder66

    Odyssey opens public preview of its Odyssey-3 world model

    Odyssey has launched a free public research preview of Odyssey-3, a world model that generates interactive environments from text prompts and runs in real time on an autoregressive diffusion transformer. The base model has 14 billion parameters and outputs 832 × 480 video, while Odyssey-3 Pro supports 1280 × 720. Its reported 66.1-point Physics-IQ Verified score came from a single run using a selection method, below the benchmark's four-run requirement.

10/6Tue
  1. The Decoder68

    Reka AI previews Rho-1, a 19B general-purpose model for text, images, video, and robot control

    Reka AI released a research preview of Rho-1, a 19B-parameter general-purpose model that can process and generate text, images, video, and robot-control actions in a single neural network. Unlike most systems that route tasks to specialized models, Rho-1 places all modalities as tokens in one shared context window without calling tools or external models. It can generate continuous video in real time, respond instantly to new instructions without restarting, and use the same weights that predict camera images to drive robot actions.

7/30Thu
  1. Google DeepMind71

    Google DeepMind releases Gemini Robotics ER 2 embodied-reasoning model

    Google DeepMind has released Gemini Robotics ER 2, positioned as the robot's high-level brain: it converses with people, understands the physical world, and plans multi-step tasks before handing execution to a lower-level VLA model. It can also call tools such as Google Search natively.

    Why it matters: The official release gives concrete figures for ER 2's progress classification and temporal localization, along with access details, helping readers assess its limits on long tasks.

7/28Tue
  1. Google DeepMind80

    Google DeepMind releases Gemini Robotics 2 for full-body robot intelligence

    Google DeepMind has released Gemini Robotics 2 with three models: Gemini Robotics 2, a vision-language-action model for translating visual and language input into motion; Gemini Robotics ER 2 for embodied reasoning; and Gemini Robotics On-Device 2, which runs locally on devices.

    Why it matters: The article explains the three models' roles and access points, helping readers assess pathways to full-body robot control and multi-robot collaboration.

7/27Mon
7/8Wed
  1. Mistral AI71

    Mistral AI releases Robostral Navigate embodied-navigation model

    Mistral AI has released Robostral Navigate, its first embodied-navigation model. The 8B model uses a single standard RGB camera, with no LiDAR or depth sensor, and achieves a 76.6% success rate on R2R-CE validation unseen—9.7 percentage points above the best single-camera approach and 4.5 points above the best systems using depth or multiple cameras.

    Why it matters: The source reports navigation success using a monocular RGB camera and describes the training approach, giving a view of how sensor requirements for embodied navigation are changing.

4/13Mon
  1. Google DeepMind71

    Google DeepMind releases Gemini Robotics-ER 1.6

    Google DeepMind has released Gemini Robotics-ER 1.6, a major upgrade to its reasoning-first robotics model. It improves spatial reasoning and multi-view understanding and adds the ability to read complex instruments such as pressure gauges and level meters.

    Why it matters: The source details changes in spatial reasoning, multi-view understanding, and instrument reading, along with access information, giving a view of progress in high-level robot reasoning.