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AIStar Motion Era's VPP2, a world action model, ranked first on the RoboDojo simulation leaderboard with a 32.26% average success rate and 39.26 average score. The article attributes gains to staged training that separates video prediction from action learning, and reports a 58.5% zero-shot success rate on a real ALOHA dual-arm robot versus 40% for π0.5. The code is open source on GitHub.
AISimplexity Robotics says it trained a single robot to load and unload two CNC lathes on its own, using 600 real-robot trajectories and reporting a 100% success rate on the precision CNC insertion task. The work, presented at IROS 2026 on September 29, combines the SimpleWAM world action model, a DRAM memory module and DPE action scoring, with force and torque feedback for insertion recovery. The company did not say how many trials the 100% figure covers, and it does not describe the yield of the whole cell.
AIMirroS released AgentGarten, which pairs executable code environments with a real-time neural renderer running above 30 fps so agents can act, observe, and learn. In a one-on-one hide-and-seek setup, the hider learned to block passages by round 4 and the seeker learned to climb ramps by round 10, guided by notes the agents wrote after each round. The authors report applying the same loop to four other tasks, including a dog-companion game, a narrow-bridge car passing task, herding, and quarry loading.
AIDonghua University researchers spun transistors, resistors and capacitors into a continuous fiber that functions as a circuit, using microfluidic encoded spinning, according to a Nature Electronics paper. The fibers integrated multicolor electroluminescence, analog and digital logic, and non-contact spatial sensing, and in demonstrations guided a robotic gripper and let a finger control a robotic arm and drone without touch.
AIOf 1,933 IROS 2026 papers, Robot Learning/Embodied AI appears in about 809, while Navigation/Planning covers 564 and Perception/Vision 556. The article argues large models are being embedded into traditional planning, geometry, and control rather than replacing them. Vision-language-action models are shifting toward efficiency, 3D understanding, memory, and system integration.
AIA Galbot, Tsinghua University and collaborators paper won IROS 2026's Best Entertainment and Amusement Paper Award for LATENT, a humanoid tennis-return method trained on imperfect amateur motion-capture clips. In simulation, the full forehand policy succeeded on 96.52 percent of returns, versus 71.85 percent for PULSE. On a real Unitree G1, the paper reports 90.90 percent forehand success across 20 consecutive rallies, with motion capture still used rather than the robot's own cameras.
AIHiPHI is a 617.5-hour whole-body human motion dataset captured with optical motion capture at sub-millimeter accuracy, including 245.7 hours of human-object interaction with synchronized object trajectories and meshes. The dataset organizes coverage using FrameNet, a linguistic framework for human action. The white paper also reports results from policies trained on HiPHI and deployed on a physical Unitree G1 humanoid robot.
AIOdyssey introduces PROWL-2, a system in which agents and their world model improve through recursive learning. The post says agents expose errors in imagination, and repairing those errors enables further learning. Odyssey frames this open-ended learning as a critical step toward superintelligence.
AIOdyssey introduces PROWL-2, a recursive loop that delivers up to 91% relative gains over the StarCraft world-model baseline. The post also reports stronger robot coordination in simulation.
AISakana 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.
AIA blog post by @Reveur_7 asks whether GEPA-style evolutionary search can improve NVIDIA's Graph-as-Policies, which generates robot controllers. Matei Zaharia praises the research as a cool approach to automatically generating state-of-the-art robot controllers.
AIMicrosoft Research reports that moving AI inference off the robot itself can improve task success and efficiency. The findings suggest this approach can support more advanced physical AI workloads as robots grow smarter.
AIBAAI reports that robot models trained on individual skills such as grasping, placing, pulling, and opening performed poorly when asked to chain them into full tasks without extra practice. The best score was 16.7%, and some models scored zero. The post's example notes a robot may open a drawer yet get stuck on the handle.
AIIn simulation, the best embodied AI models complete easy tabletop tasks about 98% of the time. On physical Franka robots, their success rate falls to 24%–72% of simulated performance, and to 13%–60% in a dual-arm setup. Models that look tied in simulation can differ by 30 points on real hardware.
AIBAAI introduces FlagEval-Robo, an open dual-track evaluation suite linking simulation with real-world execution. The team post-trained and stress-tested 12 leading open-weight embodied AI models under strictly aligned conditions. The post raises whether high benchmark scores reflect physical reality, though it does not yet report specific results.
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.
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.
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.
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.
AIBAAI has released RoboBrain-Dex, a dexterous manipulation model for embodied intelligence trained on large-scale, diverse human egocentric data rather than massive robot teleoperation datasets. The company says this approach yields strong generalization, marking a shift from small-data, weakly generalizing methods toward big-data robotic manipulation. The code has been open-sourced on GitHub.
AIResearchers trained a humanoid with 22-DoF dexterous hands mainly on over 20,000 hours of egocentric human video, with no robot in the loop, to perform tasks such as assembling model cars and folding shirts. They report a log-linear scaling law (R² = 0.998) between human video volume and action prediction loss, and state that this loss predicts real-robot success rate. The recipe, called EgoScale, pre-trains GR00T N1.5 on the video, adds only 4 hours of robot play data, and reports a 54% gain over training from scratch across five dexterous tasks.