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Updated
AIOdyssey has launched Odyssey-3, its most powerful foundation world model, with a public research preview. Odyssey-3 Pro scored 66.1 on Physics-IQ Verified video-to-video with best-of-8 sampling, the highest reported result. The model generates environments from prompts and predicts changes in real time as users move through scenes.
AIOdyssey released Odyssey-3 Pro, which sets a new top score on Physics-IQ Verified, a benchmark where models continue videos of real physics experiments. In robotics, a robot arm with tens of hours of demonstrations recovered from a missed grasp, a behavior absent from those demos.
AIGeneralist has announced GEN-1.5, its latest foundation model for the physical world. The post provides only a link to the company's blog for further details, so no specifications, benchmarks, or availability information can be confirmed from this source.
AIGeneralist's GEN-1.5 is shown repeatedly packing bottles into boxes, one after another. The post links to a blog post with more details about the model.
AIOdyssey 3 Pro, a world model, is now live as a research preview and ranks first on the Physics-IQ Verified video-to-video benchmark. The post says it can learn from visual observations and map that knowledge to physical controls for robots, cars, video games, and drones. Odyssey-3, the model launched alongside it, is described as free to try.
AIOdyssey says its Odyssey-3 model's learned world knowledge can be adapted by physical AI developers to control robots, power humanoids, drive cars, and fly drones. The post describes this as a capability for autonomous machines generally, without providing benchmarks, specifications, or availability details.
AIGeneralist (@GeneralistAI) has introduced GEN-1.5, its latest foundation model for the physical world. The post links to a playlist of other tasks its models can perform and to a blog post with further details on GEN-1.5.
AILiquid AI and NVIDIA Robotics announced a collaboration around Liquid AI's models on NVIDIA hardware. The main post is brief and gives no further technical detail or figures.
AIGeneralist (@GeneralistAI) has introduced GEN-1.5, its latest foundation model for the physical world. The post links to a playlist of other tasks its models can perform and to a blog post with further details on GEN-1.5.
AIGeneralist's GEN-1.5 robot demonstrated putting an O-ring onto a hydraulic rod gland, then repeatedly removing and replacing it. The post is a short demonstration video that points readers to a blog post in the comments for more about GEN-1.5.
AIRunway's Head of Robotics Andy Chen outlined the company's approach to robotics and premiered Praxis-1, an open-weight world action model. Early access requests are available via the Praxis-1 page on Runway's website.
AIBlack 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.
Why 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.
AIBlack 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.
AIBlack 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.
Why 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.
AIOdyssey says its new Odyssey-3 model could enable physical agents, a new kind of agent that interfaces natively with physical and virtual systems. The company expressed excitement about the promise its models are showing, linking to an introduction page for Odyssey-3.
AIOdyssey announced Odyssey-3, a foundation world model it describes as a major step forward. The post claims it can control robots, power humanoids, drive cars, train AIs, pilot drones, and play video games, though it gives no benchmarks or technical specifications.
AINVIDIA 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.
AINVIDIA 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.
AIWorld Labs has introduced Atlas, a multimodal world model it describes as trained from scratch. The post says Atlas generates frames with pixel-perfect camera control, reconstructs large scenes from as few as one input image, and outputs 3D spaces from one or more images. The author cites use cases including VFX and robotics.
AIGeneralist AI introduces GEN-1.5, its latest foundation model for the physical world, with additional task demonstrations shared in a YouTube playlist. The post links to a blog post with further details about the model.
AIGeneralist AI demonstrated that GEN-1.5 behaves differently when given different physical prompts in the same environment. The post shares a simple example responding to a request from @JagdeepBhatia8 about whether the model follows physical prompts or just the most likely action for the scene.
AIGeneralist has announced GEN-1.5, its latest foundation model for the physical world. The post provides only a link to the company's blog for further details, so no specifications, benchmarks, or availability information can be confirmed from this source.
AIGeneralist says it has reduced the time needed to go from a physical prompt to robot behavior, making it faster to teach robots new tasks. The company links this speedup to easier scaling of physical work, and points readers to its GEN-1.5 blog post for details.
AIGeneralist says its GEN-1.5 model shows a new level of generality when pretrained on physical interaction data at a scale few thought feasible without shortcuts. The company says it does not yet see where performance gains level off.
AIGeneralist (@GeneralistAI) posted an uncut video of its model performing two tasks back-to-back: unzipping a pencil pouch and then retrieving money from it.
AIBlack Forest Labs released FLUX 3, a multimodal model trained on images, video, and audio, and mimic built FLUX-mimic on its video backbone to control robots. In a soft-body kitting task, mimic reports a 95% success rate without single-task fine-tuning, compared with 55% for an adapted π0.5 model. FLUX 3 Video is in early access, with action prediction offered to selected partners and an open-weight backbone planned.
AINVIDIA researchers trained SONIC, a 42M-parameter transformer, to control a humanoid robot's whole body using motion tracking on over 100M mocap frames. After three days of training in simulation, the policy transferred zero-shot to the real G1 robot and reported a 100% success rate across 50 real-world motion sequences. One policy supports VR teleoperation, webcam human video, text prompts, music, and GR00T N1.5 VLA integration with 95% success on mobile tasks, and the code and checkpoints are open-sourced.
AIBAAI released RoboBrain 2.5, an embodied AI model that turns 2D scene understanding into actionable 3D trajectories and provides dense temporal value estimates for real-time progress feedback on long-horizon tasks. The post says it achieves SOTA across multiple spatial and temporal reasoning benchmarks, though it names no specific scores. Project page, paper, GitHub code, and model weights are linked.