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AI in the physical world: humanoid robots, embodied foundation models, and action in real environments.

4 picksPast 30 days: 3 itemsTotal: 124 items

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Sep 23

Sep 23WedItems 1–4
  1. Microsoft ResearchAI score60

    Microsoft Research shows offloading robot AI inference improves performance and battery life

    Microsoft Research reports that running physical AI inference on onboard GPUs can limit robot performance and battery life, while offloading inference to edge or cloud GPUs improved results in mobile manipulation tests. In its evaluation, smaller onboard GPUs slowed mapping and planning by up to 383% compared with an A100, and large onboard GPUs such as Jetson Thor drained robot batteries by up to 160%.

    AIWhy it matters: The study measures how offloading robot inference to edge or cloud GPUs changes task success, battery life, and model size, offering evidence for infrastructure design.

Sep 22

Sep 22Tue
  1. Black Forest Labs · new models on Hugging FaceAI score62

    Black Forest Labs releases FLUX 3 Action, a 7B open-weights robot world action model

    Black Forest Labs released FLUX 3 Action, an open-weights 7B world action model that outputs robot joint commands from camera frames, robot state, and a text instruction. On the RoboLab-120 benchmark it reports 42.92% task success, ahead of Cosmos3-Nano-Policy at 36.8% and π0.5 at 28.0%. The model is fine-tuned on DROID, is distributed under the FLUX Kommunity License v.1.0, and runs in about 32 GB of GPU memory in bfloat16.

    AIWhy it matters: The model card gives a benchmark comparison, parameter counts, and an action contract, so readers can judge how it compares with existing robot policies.

  2. Black Forest Labs · new models on Hugging FaceAI score60

    Black Forest Labs releases FLUX 3 Action base weights for robot adaptation

    Black Forest Labs has released flux-3-action-base, an open-weights 7B world action model that takes camera frames, robot state, and a text instruction to output the next action chunk. The release is an adaptation component rather than a complete robot policy, and new embodiments require their own action heads. The source says the weights are paired with shared video VAE and Qwen3-VL-4B-Instruct text encoders and is governed by the FLUX Kommunity License v.1.0.

    AIWhy it matters: The source separates the adaptation base from full robot policies and states the shared encoders and new-embodiment requirements, which clarifies what developers must still build for their robots.

Aug 27

Aug 27Thu
  1. Anthropic · YouTubeAI score62

    Anthropic and HHMI Janelia launch Model Hardware Standard for AI lab equipment

    Anthropic is building the Model Hardware Standard (MHS), a common way for AI models to connect to lab and manufacturing equipment and operate it with safety limits built into each device. MHS started as a collaboration between Anthropic and HHMI Janelia Research Campus and is launching as a research preview with partners across science, robotics, and manufacturing.

    AIWhy it matters: The source describes a standard for connecting AI models to lab and manufacturing hardware, which matters for anyone building automated experimentation workflows.