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#Embodied AI

#Embodied AI

Oct 1

Oct 1Thu
  1. OdysseyAI score30

    Today we’re introducing PROWL-2, where agents and their world model improve through recursive learning. Agents expose errors in imagination, and repairing those errors enables further learning. We believe this open-ended learning is a critical step towards superintelligence.

    Today we’re introducing PROWL-2, where agents and their world model improve through recursive learning. Agents expose errors in imagination, and repairing those errors enables further learning. We believe this open-ended learning is a critical step towards superintelligence.

  2. OdysseyAI score26

    We're very excited about PROWL-2. This recursive loop delivers up to 91% relative gains over the StarCraft world-model baseline, alongside stronger robot coordination in simulation. Learn more, read the PROWL-2 paper, and let us know any questions! https://odyssey.systems/introducing-prowl-2

    We're very excited about PROWL-2. This recursive loop delivers up to 91% relative gains over the StarCraft world-model baseline, alongside stronger robot coordination in simulation. Learn more, read the PROWL-2 paper, and let us know any questions! https://odyssey.systems/introducing-prowl-2

Sep 30

Sep 30Wed
  2. RunwayAI score34

    Runway Head of Robotics Andy Chen laid out Runway's unique approach to robotics, ending with the premiere of our open-weight world action model, Praxis-1. Learn more about Praxis-1 and request early access: https://runway.com/praxis-1

    Runway Head of Robotics Andy Chen laid out Runway's unique approach to robotics, ending with the premiere of our open-weight world action model, Praxis-1. Learn more about Praxis-1 and request early access: https://runway.com/praxis-1

  3. CursorAI score16

    Innate is a team of 7 building capable robots for everyday life. "The seven of us here do the work of 50," says Vignesh Anand, co-founder of @innate_bot. We spent time with the team to capture what building means to them.

    Innate is a team of 7 building capable robots for everyday life. "The seven of us here do the work of 50," says Vignesh Anand, co-founder of @innate_bot. We spent time with the team to capture what building means to them.

Sep 28

Sep 28Mon
  2. Microsoft ResearchAI score30

    Microsoft Research Asia – Singapore marks one year advancing AI research, partnerships and talent

    Microsoft Research Asia – Singapore, opened July 24, 2025 as Microsoft's first Southeast Asian research lab, reports progress after its first year. The lab's work spans next-generation AI models and agentic systems, domain-specific AI for real-world impact, AI-native research practices, and ecosystem and talent development. Its healthcare collaborations on multimodal and agentic AI for clinical decision-making are being deployed through partnerships across Singapore's healthcare ecosystem.

Sep 27

Sep 27Sun
  1. Sakana AIAI score46

    Sakana AI's SAIL boosts VLM robot trajectory success via test-time scaling

    Sakana AI and the University of Tokyo introduced SAIL, a method that generates robot trajectories with a VLM and refines them through simulator testing, VLM feedback, and Monte Carlo tree search. Across six simulated manipulation tasks, raising the search budget from one candidate to 45 increased the success rate of finding a working trajectory from 25% to 73%. The authors also tested the approach on a physical robot, though the post frames further transfer to real hardware as an open question.

Sep 23

Sep 23Wed
  1. Thomas DohmkeAI score22

    Our friend @spedemo has made a real Marvin, 3D printed, remote controlled, with voice detection and speech, all open source. And it connects to ChatGPT and Entire. Come meet Marvin, Stefano and us @wearedevs San Jose, booth 753. 🌠

    Our friend @spedemo has made a real Marvin, 3D printed, remote controlled, with voice detection and speech, all open source. And it connects to ChatGPT and Entire. Come meet Marvin, Stefano and us @wearedevs San Jose, booth 753. 🌠

  2. Microsoft ResearchAI score34

    Robots are getting smarter, but how can their hardware match that growth? New Microsoft Research findings show that moving AI inference beyond the robot can improve task success, boost efficiency, and support more advanced physical AI workloads. https://msft.it/6014agepg

    Robots are getting smarter, but how can their hardware match that growth? New Microsoft Research findings show that moving AI inference beyond the robot can improve task success, boost efficiency, and support more advanced physical AI workloads. https://msft.it/6014agepg

  3. 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.

  4. LiveKitAI score8

    Come hear about the latest developments in Robotics and Physical AI from fellow developers, researchers, & hobbyists. LiveKit x @dimensionalos Robotics Happy Hour, #SFTechWeek Tue Oct 6 · 5:30pm · SF RSVP : https://partiful.com/e/u610QKw40q4lh1MR5ZLJ

    Come hear about the latest developments in Robotics and Physical AI from fellow developers, researchers, & hobbyists. LiveKit x @dimensionalos Robotics Happy Hour, #SFTechWeek Tue Oct 6 · 5:30pm · SF RSVP : https://partiful.com/e/u610QKw40q4lh1MR5ZLJ

  5. Julien ChaumondAI score36

    📦 New JS package just dropped: huggingface/lerobot Read @LeRobotHF datasets on the Hub straight from the browser. No download. Point your coding agent at it and build custom viewers fast. As an example, here's a cool UI implemented in ~600 lines of JS on top of the package ⤵️ (GitHub repo in reply) Kudos to @mishig25 and team LeRobot!

    📦 New JS package just dropped: huggingface/lerobot Read @LeRobotHF datasets on the Hub straight from the browser. No download. Point your coding agent at it and build custom viewers fast. As an example, here's a cool UI implemented in ~600 lines of JS on top of the package ⤵️ (GitHub repo in reply) Kudos to @mishig25 and team LeRobot!

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 score58

    Black Forest Labs releases open-weights FLUX 3 Action SO-101 robot policy

    Black Forest Labs has published FLUX 3 Action SO-101 on Hugging Face as an open-weights 7B world action model. It takes two camera frames, the robot state, and a text instruction, then returns the next 42 actions with predicted video frames, with 32 executed at 30 Hz before replanning. The card also provides a rank-32 LoRA fine-tuning recipe for user datasets and states that the application must enforce joint velocity, force, and workspace limits.

  3. 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.

Sep 20

Sep 20Sun
  1. LMSYS OrgAI score32

    RLinf adds Cosmos3 support with SGLang, boosting evaluation throughput 3.33x

    RLinf, an open-source framework for embodied intelligence and AI agents, now supports Cosmos3 from fine-tuning through robot evaluation. With SGLang inference, it delivers 3.33x end-to-end evaluation throughput, batching inference for 128 parallel environments on 8 GPUs across 500 episodes of the full LIBERO-10 evaluation. RLinf also overlaps CPU simulation with GPU inference to reduce waiting between stages.

Sep 15

Sep 15Tue
  2. OdysseyAI score22

    We see Odyssey-3 as enabling physical agents, a new kind of agent that speaks the language of the world—enabling it to interface natively with physical and virtual systems. We couldn't be more excited at the promise our models are showing. https://odyssey.systems/introducing-odyssey-3

    We see Odyssey-3 as enabling physical agents, a new kind of agent that speaks the language of the world—enabling it to interface natively with physical and virtual systems. We couldn't be more excited at the promise our models are showing. https://odyssey.systems/introducing-odyssey-3

  3. OdysseyAI score38

    Today we’re unveiling Odyssey-3, a big step forward for foundation world models. It can control robots, power humanoids, drive cars (on the roads of India!), train AIs, pilot drones, and even play video games. We can’t wait to see what intelligent systems it enables.

    Today we’re unveiling Odyssey-3, a big step forward for foundation world models. It can control robots, power humanoids, drive cars (on the roads of India!), train AIs, pilot drones, and even play video games. We can’t wait to see what intelligent systems it enables.

Sep 14

Sep 14Mon
  1. NVIDIA · new models on Hugging FaceAI score36

    NVIDIA's FoundationPose estimates 6-DoF object pose without fine-tuning given a CAD model

    NVIDIA released FoundationPose, a transformer-based model for 6-DoF object pose estimation and tracking that works on novel objects at test time without fine-tuning, given a CAD model. It takes RGB and depth images, a 2D bounding box, a CAD model, and camera intrinsics as inputs, and is licensed under the NVIDIA Open Model License for commercial use. The model is trained on synthetic data from Objaverse and Google Scanned Objects, with evaluation on LINEMOD and YCB-Video.

Sep 8

Sep 8Tue
  2. BAAIAI score34

    Knowing one move is not the same as doing the whole job. The models were trained to grasp, place, pull, open. Then they were asked to string those moves together. No extra practice on the full task. Best score: 16.7%. Some models: ZERO. A robot can open a drawer and then get stuck on the handle.

    Knowing one move is not the same as doing the whole job. The models were trained to grasp, place, pull, open. Then they were asked to string those moves together. No extra practice on the full task. Best score: 16.7%. Some models: ZERO. A robot can open a drawer and then get stuck on the handle.

  3. BAAIAI score46

    In simulation, the best models finish easy tabletop tasks about 98% of the time. On physical Franka hardware, the success rate falls to 24%–72% of what the model achieves in simulation. With a dual-arm configuration, the range is 13%–60%. Two models that look tied in sim can be 30 points apart on hardware. Sim is the practice room. The robot is the test.

    In simulation, the best models finish easy tabletop tasks about 98% of the time. On physical Franka hardware, the success rate falls to 24%–72% of what the model achieves in simulation. With a dual-arm configuration, the range is 13%–60%. Two models that look tied in sim can be 30 points apart on hardware. Sim is the practice room. The robot is the test.

  4. BAAIAI score43

    Embodied AI demos are advancing rapidly, but how much do high benchmark scores actually reflect physical reality? Introducing FlagEval-Robo — an open, dual-track evaluation suite connecting simulation with real-world execution. We systematically post-trained and stress-tested 12 leading open-weight models under strictly aligned conditions. Here is what we discovered👇

    Embodied AI demos are advancing rapidly, but how much do high benchmark scores actually reflect physical reality? Introducing FlagEval-Robo — an open, dual-track evaluation suite connecting simulation with real-world execution. We systematically post-trained and stress-tested 12 leading open-weight models under strictly aligned conditions. Here is what we discovered👇

Sep 3

Sep 3Thu
  1. Understanding AI (Timothy B. Lee)AI score43

    Robot startups are trying everything they can think of to get more data

    Robot startups are racing to collect training data, from companies paying cleaners to wear cameras to firms recording VR-controlled humanoid robots. The article says the largest openly available robot task dataset, ABC-130K, contains only 3,500 hours of demonstrations. Skild CEO Deepak Pathak argues companies must gather high-quality data before robots can do enough useful work to generate it through deployment.

Sep 2

Sep 2Wed
  1. NVIDIA · new models on Hugging FaceAI score36

    NVIDIA Releases EgoHand-1.0 Model for Single-Image 3D Hand Pose Estimation

    NVIDIA released EgoHand-1.0, a 883.5M-parameter DINOv3-based transformer that predicts SOMA hand pose, MHR shape coefficients, and camera translation from a single 256×256 hand crop. The model is evaluated on the HOT3D egocentric benchmark and is intended for research and demonstration rather than production use. Its outputs can supply hand trajectories for training robotic manipulation policies, and it runs on NVIDIA Ampere GPUs under Linux with PyTorch.

Sep 1

Sep 1Tue

Aug 27

Aug 27Thu
  2. 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.

Aug 26

Aug 26Wed
  1. GeneralistAI score35

    @JagdeepBhatia8 Other tasks our models can do: https://www.youtube.com/playlist?list=PLIv5TE4TO-ZgiMiHpD5ZSiJVLa9I6557R Read more about GEN-1.5, our latest foundation model for the physical world: https://generalistai.com/blog/gen-1.5

    @JagdeepBhatia8 Other tasks our models can do: https://www.youtube.com/playlist?list=PLIv5TE4TO-ZgiMiHpD5ZSiJVLa9I6557R Read more about GEN-1.5, our latest foundation model for the physical world: https://generalistai.com/blog/gen-1.5

  2. GeneralistAI score22

    A simple example of physical prompt steerability: same environment, different prompts, different behaviors. Thanks @JagdeepBhatia8 for the suggestion. Read more about GEN-1.5 in our blog post in the comments below.

    A simple example of physical prompt steerability: same environment, different prompts, different behaviors. Thanks @JagdeepBhatia8 for the suggestion. Read more about GEN-1.5 in our blog post in the comments below.

Aug 24

Aug 24Mon
  2. GeneralistAI score38

    We've reduced the time it takes to go from physical prompt → robot behavior. The faster anyone can teach a robot to do something new, the easier it becomes to scale physical work. Read more about GEN-1.5 in our blog post in the comments below.

    We've reduced the time it takes to go from physical prompt → robot behavior. The faster anyone can teach a robot to do something new, the easier it becomes to scale physical work. Read more about GEN-1.5 in our blog post in the comments below.