#Embodied AI

AIWaymo introduced the Ojai, a new vehicle that will run the same Waymo Driver autonomous system. The post gives no further details on specifications, deployment timing, or availability.

AIAnthropic 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.

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

AIPollen Robotics has unveiled Microduck, a 25 cm open-source biped with 15 actuators and sensors including a camera, speaker, and LiDAR that users can train with reinforcement learning. The robot ships with more than half a dozen pre-trained policies for walking, sitting, roller-skating, and picking up objects with its articulated beak, and costs less than $400.

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.

AIA post from hardmaru compares novel locomotion policies discovered in MuJoCo simulation to real-world robot behavior, noting they now work outside simulation. The reply context shows slow-motion footage of a 400m champion's running form at the World Humanoid Robot Games, suggesting the comparison concerns humanoid running gait.

AINVIDIA and Berkeley are open-sourcing T-Rex, a methodology for adding touch sensing to robot manipulation models. It uses a mixture-of-transformer with a slow visuomotor expert and a fast tactile expert running four touch ticks per vision tick. A 50-hour dataset of about 5,500 episodes from 22-degree-of-freedom tactile hands is available on Hugging Face.

AIA humanoid robot preparing for the Robot Olympics in Beijing ran too fast, failed to stop, and slammed into a safety cushion, breaking at the waist. The main post, by hardmaru, is only a short comment reading "Winning at all costs," so the criticism is implied rather than explicit.

AIZhang Yijia, founder and CEO of Chinese tech think tank 甲子光年, presented a 2026 AI trend report at a Beijing investment conference, arguing China's venture capital has entered a new cycle as financing, investment, and exits rebounded in H1 2026. He said AI absorbed over 70% of global venture investment in H1 2026, with OpenAI and Anthropic together raising $217 billion, and that global AI-related capex is expected to exceed $1 trillion in 2026.

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.

AIThe model sometimes uses both hands to rotate a jar lid, employing a fundamentally different contact and motion strategy than in the fine-tuning demonstrations. The post does not specify further details about the setup or results.

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.

AIA fine-tuned robot can clear obstacles, such as a paper covering a bowl, to complete a block-placing task. It does this even though such obstacle-clearing behavior was absent from its training demonstrations.

AIRobot behaviors fine-tuned or prompted on specific demonstrations can generalize beyond them and improvise fundamentally different manipulation strategies toward the same goal. In one example, a model fine-tuned to sweep a block into a bowl with a brush accomplished the task with a dustpan instead, using a very different approach.

AIGeneralist AI reports a robot that, when a Lego brick unexpectedly sticks to its fingertips, uses its other hand to remove it. The post shows this recovery behavior but gives no details on the model, hardware, or benchmarks.

AIGeneralist reports that its robot model can adapt to new physical tasks in 1–10 gradient steps using 1–5 minutes of data, roughly 10–50 demonstrations. The post describes this as test-time training in a low-data regime, and says the results were obtained without tuning the procedure or sweeping hyperparameters.

AIGeneralist's model reached 59% average success on 10 diverse physical tasks with one-shot prompting straight from pretraining. With few-shot learning, using 10 gradient steps on 5 minutes of data per task, performance rose to 83%. The post calls it the first model it knows of that learns a wide range of dexterous closed-loop physical tasks from one or few demonstrations.

AIGeneralist reports that with GEN-1.5, in-context learning can transfer across the embodiment gap entirely in some cases. A human demonstrates a task with their own hands, observed through the robot's cameras, and the robot can reproduce it immediately afterward.

AIPrompts built entirely from simulated experience, such as from a scripted policy, an RL agent, or a teleoperated simulated robot, can produce behaviors on a real robot. The model was not trained on the task in either the simulator or the real world, so the transfer is zero-shot.

AIResearchers from Keio University and the MIT Media Lab built a soft, helium-filled robot with gentle flapping fins and no face, rotors, or pinch points. In demos it served as an alarm clock, study buddy, movement reminder, and dance partner, communicating through movement. The team designed it to avoid the uncanny valley and be safe to touch, betting people will welcome it into their personal space.

AIThe 2026 Global Developers Pioneer Conference (GDPS) and International Embodied Intelligence Skills Competition has opened global calls for scenario challenges, teams, and experts ahead of its October 23–25 event at Shanghai Zhangjiang Science Hall.

AITri Dao reports that connecting an LLM as the "brain" to robot control policies quadruples state-of-the-art performance with no extra training. He says he was surprised by how well it works and expects agents running on robots to arrive soon. Background from a quoted post reports real-robot success rising from 16.7% to 97.3% and simulated LIBERO-PRO success from 12.8% to 53.3%.

AIWorld Labs, following its SceniX acquisition, says spatial intelligence must cover interacting with worlds as well as perceiving and generating them. It is sharing early results on building generated worlds that can be used to train robots. The post itself gives no further technical details or figures.

AIWeave Robotics has opened preorders for a robot priced at $8,000 that folds laundry and makes beds. Deliveries are slated for this fall.

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.

AIThe University of Pittsburgh's RAMMP team is integrating Meta's DINOv3 and SAM models into on-device assistive robotics to detect door buttons, cups, and curbs for navigation assistance. The models run on compact, battery-powered hardware, with optimizations such as reduced memory footprint and lower precision, enabling real-time perception without network connectivity. RAMMP's perception system pairs SAM-based auto-labeling with an RF-DETR detector fine-tuned on DINOv2 embeddings, and the team is now testing voice and touch input for object selection.

AIFei-Fei Li announced that SceniX is joining World Labs. She framed spatial intelligence as more than perceiving and generating worlds, emphasizing interaction with them.

AIAt RAAIS, DeepMind VP of Research Raia Hadsell argued that the field focuses too much on language and should apply large-model training to worlds, robots, biology, and weather. The article cites DeepMind's DiffusionGemma, a 26-billion-parameter open text model that generates blocks by denoising rather than one token at a time, and the Genie-3 world model, which runs in real time for several minutes. It also describes world models as a source of synthetic training data for robots.

AIA Chinese company has revealed an autonomous robot toilet that drives itself to users when summoned by voice command or remote control. The post provides no further technical details, pricing, or availability.

AIJim Fan posted uncut footage of a robot performing an assembly task with an end-to-end policy, without any speedup. He describes the model as slow but careful, measuring each grasp and alignment with precision.

AIStanford SVL and NVIDIA Robotics introduced RoboTTT, which uses test-time training to give robot policies up to 8,000 timesteps of context at constant inference cost. The source reports that 8K-context pretraining beats 1K by 62%, and that performance keeps improving from 128 to 8K timesteps with no sign of saturation. The authors also describe one-shot imitation from human video and in-episode error recovery.

AIJim Fan introduced RoboTTT, a robot model that uses test-time training to compress history into a tiny inner model updated at each sensor reading. The post reports closed-loop performance rising steadily from 128 to 8K timesteps, and 8K-context pretraining beating 1K by 62%. It also claims one-shot in-context learning from human video and mid-episode error recovery, with learning continuing after deployment.

AIBAAI introduced RoboBrain Orca, which learns world latent representations from multimodal data and models world-state transitions through Next-State-Prediction instead of only next-token, next-frame, or next-action prediction.

AIJim Fan announces ASPIRE, a system where coding agents use multimodal sensory traces from simulation and real robots to run evolutionary search over control programs and add the results to a growing skills library. The post claims up to a roughly 10x reduction in transfer learning tokens for sim-to-real and single-arm to bimanual transfer, and says the full stack will be open-sourced.

AIJim Fan introduces ASPIRE, a system in which coding agents observe multimodal sensory traces and run evolutionary search over control programs to distill skills into a growing library. The post says ASPIRE shares know-how rather than pixels or weights across the sim-to-real gap, reducing transfer learning tokens by up to about 10x. The author also says the full stack will be open-sourced and provides a gallery of 150+ tasks and 90+ skills.

AIThe 8th BAAI Conference opened an immersive zone featuring SoulAgent, embodied intelligence, FlagOS, and AI for Science. Hands-on demonstrations covered humanoid robot tasks, multi-chip systems, and AI-assisted cardiac diagnosis, neuroscience, and drug discovery.

AINVIDIA GEAR's ENPIRE gives eight Codex agents a fleet of robots, GPUs, and a token budget to solve physical tasks with minimal human oversight. The author reports tasks such as tying zip-ties, organizing fine pins, and installing GPUs, and a faster time-to-solution with eight parallel robots than with fewer. Safety uses a kinematic limit that resets a robot leaving its envelope, a torque-limited gripper, and a frozen reward function classifier. The team says everything will be open-sourced.

AIJim Fan introduces ENPIRE, which gives eight Codex agents a fleet of robots, GPUs, and a token budget to solve physical tasks autonomously. The post reports that the system can tie zip-ties, organize fine pins, and install GPUs, and that eight robots exploring in parallel improve faster than fewer. The team plans to open-source everything.