Research papers, technical reports, benchmarks and datasets.
Welcome to the era of embodied AI. We are launching AA-Robotics v0.1, our research preview that measures frontier language model performance on understanding, planning, and acting in the physical world. In v0.1, each model is given control of a 6-axis robot arm (5 joints and a https://t.co/fCwpnDlvB2
Reporting on Jiayuan Mao’s IROS 2026 presentation, Leiphone describes research from UPenn, MIT, and Stanford on robot learning and compositional generalization. RAPID combines a coding agent with physics validation to learn complex non-grasping tasks from one human demonstration. In separate video-action research, the team introduced Temporal Ratio to measure attention to future versus current frames; adaptive guidance based on this metric raised average success on unseen compositional tasks with a real dual-arm robot from 71.8% to 83% without retraining.
WorldArena 2.0 Challenge, run by a Tsinghua-led team, released its final leaderboard on September 16 with three tracks covering video quality, world models as reinforcement learning environments, and real-robot manipulation. Visincept's WorldIncept won Track 1, Zhongguancun Academy's MW2 won Track 2, and Xiaomi Robotics' ViTacX topped Track 3, with total prize money of 7,000 USD. Organizers said contact-rich real-robot operation remains a bottleneck.
Columbia University's Li Yunzhu team presented progress on FlexiTac, an open-source flexible piezoresistive tactile sensor platform, at an IROS 2026 workshop. The sensor assembles in 3 minutes, ships with a full BOM and readout software, and integrates with the LeRobot robot-learning framework. Analog Devices is developing a commercial version with tactile resolution five times that of a human finger, produced via semiconductor manufacturing.
Can today's frontier language models control a real robot arm, zero-shot, with no coaching? AA-Robotics v0.1 coming soon.
RobotEra's in-house world action model VPP2 ranked first on the RoboDojo simulation leaderboard, with the highest overall average success rate (32.26%) and average score (39.26 points), ahead of GPT-6-Astra at 22.48% and 28.97 points.
The MirroS team launched AgentGarten, connecting an executable code environment to a real-time neural renderer: code defines the physics, while the model generates visual feedback, enabling continuous agent interaction at more than 30 fps. In a recreated hide-and-seek experiment, hiders learned to move barriers into cover by round 4, while seekers learned to use a ramp to climb over a wall by round 10; comparable OpenAI experiments in 2019 took about 25 million and 100 million rounds, respectively. The team also published a blog, technical report, code repository, and project site.
The GaLbot team at Galaxy General, together with Tsinghua, Peking University, and others, proposed LATENT, which trains humanoid robots to play tennis using about five hours of imperfect motion data from five amateur players. The method first trains a motion tracker and compresses it into a latent action space with a Latent Action Barrier constraint. On a Unitree G1, it achieved a 90.90% success rate on real forehands and 77.78% on backhands.
IROS 2026 accepted 1,933 papers. A multi-label analysis counted about 809 papers in Robot Learning / Embodied AI and 564 in Navigation / Planning.
The IROS 2026 Best Paper Award went to Yumin Lee and colleagues for LT-Mem. The work addresses temporal forgetting in long-running robots with a three-layer structure of Live Memory, Delta Memory, and Meta Memory, and the concept of volatility.
Researchers at PhAI Labs, Stanford, Oxford, and Princeton proposed JEPA-Anything. It splits JEPA's predicted state into four orthogonal factors, assigns a prediction module to each, and recombines them. It outperformed standard JEPA with the same architecture and data on ten test tasks in physics, robotics, weather, and other areas.
At an ECCV 2026 workshop, NVIDIA Research scientist Li Boyi presented two projects, MultiGen and DexImit. MultiGen uses diffusion models to add synchronized audio to videos generated by physical simulators.
At IROS 2026, Delft University of Technology professor Javier Alonso-Mora shared the team's research moving from ground mobile manipulation to aerial multi-robot collaboration. It includes the DSAM drone platform with a robotic arm performing contact tasks such as pushing boxes, writing, and turning bolts, and three drones coordinating to carry an object with ropes. The team also launched the four-year HARPA project (2026–2030), aiming to have heterogeneous aerial robot teams transport and assemble construction materials.
Stanford's Movement Lab and Caltech published HomeBody, connecting GPT Astra to a Unitree G1 so the robot can explore an unfamiliar kitchen and then carry out sequences such as finding objects, getting medicine, throwing out trash, and opening drawers.
Microsoft Research systematically measured mobile-robot manipulation workloads and found that moving physical-AI inference from an onboard GPU to an edge or cloud GPU can improve task performance and extend battery life.
Why it matters: Microsoft Research measured robot inference workloads and reports performance and battery-life data for offloading inference from onboard hardware to edge or cloud GPUs.
Jim Fan announces T-Rex, an open-source tactile robot learning methodology developed through a collaboration between NVIDIA and Berkeley. Its mixture-of-transformer architecture pairs a slow visuomotor expert with a fast tactile expert running at 4 touch ticks per vision tick; a 50-hour (~5,500 episodes) synchronized robot play dataset collected on tactile hand hardware with 22 degrees of freedom is available today on HuggingFace. Extending EgoScale, its training recipe combines human egocentric video pretraining with tactile robot play mid-training, which Fan says helps bridge contact-free pretraining and contact-rich manipulation.
Why it matters: T-Rex offers a transferable approach to contact-rich robot learning by combining human video pretraining, synchronized tactile robot data, and separate visual and tactile processing rates.
NVIDIA has released ENPIRE, a framework in which coding agents drive physical robots through autonomous experiments and improvements. It achieved 99% success on tasks including PushT, peg sorting, and cutting zip ties with a cutter; tests also tried inserting a GPU into a motherboard.
