Every funding round, launch and paper, as it's reported.
Anthropic economists scored about 19,000 O*NET job tasks and concluded that robots can technically perform 74% of physical tasks in the US, yet beat human labour on cost for only 0.3% of tasks. At the historical 3% annual decline in robot prices, the study estimates it would take 40 years for that share to reach 10%.
Data engineering company Innodata Inc. has opened a motion-capture laboratory in New Jersey to gather real-world movement data for training humanoid robots and other physical AI systems, The Robot Report reported on Sept. 30. The lab uses Vicon infrared optical tracking cameras that record the 3D motion of human and robot bodies with sub-millimeter accuracy and millisecond latency. Innodata said it will sell ready-made motion datasets, run custom collection projects, retarget data across robot platforms and offer independent performance and safety validation of robots.
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A Tsinghua University team working with ByteDance Seed and China Agricultural University published a reinforcement learning controller in Science Robotics on August 19, 2026 that links a humanoid's camera directly to its legs. On Booster Robotics' T1, the authors report a 46% cut in ball-position error, up to 64% faster time-to-kick and about 90% kick success in frontfield positions, with no real-world fine-tuning.
NeuralActuator, a model from MIT's Computational Design and Fabrication Group with Amazon, won the Outstanding Systems Paper Award at RSS 2026. It learns how cheap servo motors really behave from motor current, voltage and temperature, and estimates external force with a reported mean error of 0.12 N, against 0.66 N to 1.41 N for classical baselines.
FlashSAC, an off-policy reinforcement learning method from a team spanning Holiday Robotics, KAIST, KRAFTON, TU Darmstadt, KTH and others, won the Outstanding Paper Award at Robotics: Science and Systems 2026 in Sydney. The authors report training a Unitree G1 walking policy in about 20 minutes on a single RTX 5090, against about three hours for the standard PPO method.
OmniRetarget, a motion retargeting method from Amazon's Frontier AI & Robotics team and university collaborators, won both the Best Conference Paper Award and the Best Paper Award on Robot Manipulation and Locomotion at IEEE ICRA 2026 in Vienna. The method preserves contacts between body, objects and terrain, and the authors used it to generate more than 8 hours of trajectories that trained a Unitree G1 to perform parkour and box carrying in the real world without real-robot fine-tuning.
Korea's science ministry on May 18 started a five-year, 50.4 billion won programme to build a national AI humanoid platform led by KIST, with LG Electronics, LG AI Research and LG Energy Solution among the partners. The plan includes a vision, touch, language and action model, an all-solid-state battery for humanoids and a field test of at least 20 robots in care settings.
Sony AI's autonomous table tennis system Ace won three of five matches against elite players and lost both matches against two Japanese T.League professionals in April 2025 trials, according to a paper published in Nature on April 22, 2026. The robot combines event-based vision sensors from Sony Semiconductor Solutions, an eight-axis arm and reinforcement learning policies trained in simulation, and reaches a perception latency of 10.2 milliseconds.
An 82-author team from Toyota Research Institute, Cornell University and MIT reported in Science Robotics on April 15, 2026 that multitask large behavior models, built on diffusion policy, outperform single-task baselines in blind, randomized trials and need far less task-specific data. The preprint version describes about 1,700 hours of robot demonstrations across more than 500 tasks and 1,800 controlled real-world trials.
Generalist AI released GEN-1 on April 2, 2026, an embodied foundation model pretrained on more than 500,000 hours of human physical interaction data captured with wearable devices and no robot data. The company reports 99% average success on tasks where its previous model reached 64%, about one hour of robot data per task and task times roughly three times faster than the prior state of the art; the results are the company's own and have not been independently replicated.
Figure AI on Jan. 27, 2026 released Helix 02, a robot foundation model that runs a humanoid from camera pixels and fingertip touch down to joint commands at 1 kHz, replacing 109,504 lines of hand-written C++ with a 10-million-parameter learned controller. The company showed a Figure 03 unloading and reloading a dishwasher across a full kitchen in a four-minute, 61-action run it says had no resets and no teleoperation.