Technology areas
Robot models and learning
Robot VLA models, general action and world models, imitation and reinforcement learning, skill and motion policy learning, online adaptation, and transfer across tasks and robot bodies. The contribution is a model, policy, or learning method. Dataset releases belong under Data and simulation; inference deployment belongs under Software and compute systems.
206 selected · 116 in the last 30 days
云深处科技 · 微信公众号 ·
DEEP Robotics unveils five embodied brain results: world model DR-WM-1.0, VLA model DR-VLA-2.0, and more
DEEP Robotics has released five research results centered on its self-developed embodied brain, DEEPRobotics-Brain, including the world model DR-WM-1.0, the embodied foundation model DR-VLA-2.0, the embodied agent DR-Agent, the policy optimization framework DR-APO, and the data closed-loop platform DR-Benchmark. The company is also collaborating with seven universities, including MIT, Tsinghua, and Zhejiang University.
简智机器人 · X ·
Genrobot open-sources Gen-HumanWholeBody first-person whole-body manipulation dataset
Genrobot has released and open-sourced Gen-HumanWholeBody, a first-person whole-body manipulation dataset it says has the industry's highest precision, hosted on Hugging Face. The dataset contains 600 hours of whole-body manipulation data, approximately 3 cm whole-body MPJPE, 16,976 real-world scenes, and 1,281 human skills, and is open for research on whole-body motion learning, human-to-robot transfer, and embodied foundation models.
NEURA Robotics ·
NEURA Robotics unveils 4NE1 Gen 4 humanoid robot and opens TUM RoboGym with Technical University of Munich
NEURA Robotics and the Technical University of Munich (TUM) have opened TUM RoboGym at LabCampus near Munich Airport, covering more than 2,300 square meters. TUM MIRMI is a co-builder, the initial collaboration runs for three years, and the two parties are jointly investing about 17 million euros, of which NEURA is contributing about 11 million euros.
3D视觉工坊 · 微信公众号 ·
SenseTime and joint team evaluate GPT-6-Astra embodied capabilities, open-source SenseNova-RoboRSI framework and 27B model
A joint team including SenseTime and The Hong Kong University of Science and Technology (Guangzhou) released the report "Has GPT-6-Astra Achieved Embodied Intelligence?", systematically evaluating GPT-6-Astra's embodied capabilities across 12 benchmarks, and simultaneously open-sourced the SenseNova-RoboRSI evaluation framework and a 27B embodied model.
arXiv cs.RO ·
Learning from Fleet Experience: Collective Experience Learning Across 96 Robots
The paper proposes the Collective Experience Learning (CEL) paradigm, in which deployed robots pool their own execution experience to update a shared policy that is then returned to the fleet for use. It is implemented through failure-guided experience retrieval fine-tuning (FERT), which uses failure contexts to retrieve relevant segments from successful trajectories and prioritizes their recorded actions for supervised updates, without additional expert demonstrations or corrective actions; a supporting fault-tolerant asynchronous training system (FATS) coordinates robot execution, cloud inference, and policy optimization.
机器人前瞻 · 微信公众号 ·
NVIDIA open-sources robot data engine Flora, labeling cost about 1/125 that of GPT-6 Astra
NVIDIA released and open-sourced the data engine Flora, which converts raw robot video into training data such as timestamped action descriptions, process rewards, and outcome rewards; it is already used to train the GR00T series models. Flora consists of an action description model, a temporal segmentation model, and a reward model, fine-tuned from the Qwen3.5 series of vision-language models, and can label 200,000 hours of data per day on 512 GB300 GPUs.
NVIDIA ·
NVIDIA Expands Isaac Platform with New Humanoid Reference Platform, Isaac OS, and Isaac Lab 3.0
At GTC Berlin, NVIDIA announced an expansion of the Isaac open-source robotics development platform, adding the Isaac OS runtime, the officially released Isaac Lab 3.0 learning framework, and an open-source reference workflow based on DGX Station.
arXiv cs.RO ·
Activation injection study dissects vision-language-action models
Researchers propose an interpretability pipeline to analyze what a fine-tuned vision-language-action (VLA) policy is computing internally. On pi0.5, injecting task A's activations into the action expert while the policy views task B's scene caused 99.6% of episodes (n=1968) to execute task A's motor trajectory, indicating the injected program is bound to absolute workspace coordinates and ignores the visible scene.
arXiv cs.RO ·
SuperMimic: Scaling Humanoid Whole-Body Control with One Differentiable World Model
SuperMimic uses a differentiable world model of shared dynamics to train the world model and the tracking policy together in a single training loop, giving the policy analytic gradients through backpropagation-through-time over short imagined rollouts.
Xbotics具身智能实验室 · 微信公众号 ·
RLinf v0.4 released: open-source training framework upgrade for embodied reinforcement learning
RLinf, an open-source training framework for embodied reinforcement learning developed by Tsinghua University, Infinigence AI, and the Beijing Zhongguancun Academy, has officially released v0.4, with updates across four areas: a Lego-style real-machine system, real-machine post-training algorithms, full-modality model support, and a domestic computing power foundation.
The Humanoid Hub ·
Ropedia releases open-world model W0, trained on about 10,000 hours of first-person human video
Ropedia released the open-world model W0, trained on about 10,000 hours of first-person human video. A single set of weights can predict the first-person view of humans, robotic arms, and humanoid robots (Unitree G1, Fourier GR-1), using a shared camera-plus-skeleton representation; adding a new robot requires only its URDF.
Xbotics具身智能实验室 · 微信公众号 ·
SenseTime launches SenseNova-RoboRSI and a 27B embodied model, achieving SOTA on 12 simulation benchmarks
The SenseTime SenseNova team launched the SenseNova-RoboRSI unified execution framework and the 27B embodied model SenseNova-RoboRSI-27B-Preview.
雷科技 ·
WorldOracle world-model evaluation platform launched, using real-robot testing to fact-check the industry
On October 16, WorldOracle, an action-driven world-model evaluation platform jointly completed by researchers from AgiBot, NVIDIA, Princeton University, the University of Texas at Austin, and the University of Toronto, was officially released, opening evaluation submissions to developers.
arXiv cs.RO ·
Evaluating a Frozen Frontier VLM Directly as a Robot Policy on the RoboMME Memory-Dependent Manipulation Benchmark
The study uses a frozen frontier vision-language model, GPT-6-Astra, directly as the robot policy and tests it on RoboMME, a benchmark of 16 memory-dependent manipulation tasks. The model keeps the entire episode context throughout and directly outputs each end-effector waypoint and gripper command, with no training specific to this benchmark.
AI前线 · 微信公众号 ·
Google releases Gemini 4 Argon physical reasoning model, connecting the full robotics R&D chain
Google has released Gemini 4 (Argon), positioned as a general physical reasoning model focused on interaction with the physical world, covering the full robotics lifecycle from control execution and simulation reconstruction to data annotation and self-improvement. On the ERQA embodied reasoning benchmark, Argon scored 87%, surpassing Claude Opus 5.5 (82.3%) and GPT-6 Astra (81%); in the AA-Omniscience test, its hallucination rate was 15%, lower than the 51% and 59% of the latter two.
Xbotics具身智能实验室 · 微信公众号 ·
Shanghai AI Lab evaluates GPT-6-Astra's capability boundaries as a robot policy on EBench
Shanghai AI Laboratory's embodied evaluation team used EBench to evaluate GPT-6-Astra as a robot policy directly, without task-specific post-training, across 26 tasks and 510 total executions. Its average task success rate of 46.7% ranked third, and its average task score of 0.654 ranked second; OpenWAM-α led with a 55.3% success rate.
Dexmate ·
Dexmate and CMU Release MeMoREval Benchmark: Robot Storing Camera Frames Beats Text Descriptions
Dexmate and CMU introduced the MeMoREval benchmark, which uses a two-stage protocol of experiencing first and asking questions later to evaluate what embodied agents should store as memory. It includes 53 tasks and 4 simulated indoor environments, and runs on the Vega 1 Pro dual-arm mobile manipulator. Results show that storing raw camera frames scored 70% and had lower write cost, surpassing text descriptions at 56 to 60%; adding scene graphs achieved the highest overall success rate among all tested models. The protocol also completed two memory retrieval demonstrations on a real Vega 1 Pro using a SLAM map and head camera.
OFweek Robotics ·
Unitree market value retraces nearly 70%; Wang Xingxing admits at CIIF he did not believe in AI enough
Less than two months after its listing, Unitree Technology's share price fell from a high of 1,237 yuan to around 420 yuan, wiping out more than 270 billion yuan in market value, with a maximum drawdown of nearly 70%. Founder Wang Xingxing said on October 12 at the Shanghai International Industry Fair that his biggest mistake over the past decade-plus was not believing in AI enough, and gave a forecast that the robotics era could reach an inflection point in as soon as one to two years. Unitree is building a physical AI self-evolution system. The stock closed up 3.64% the day after his remarks.
深圳市机器人协会 · 微信公众号 ·
HKU MMLAB releases PhysicalRSI 1.0, tops RoboDojo and sharply cuts token costs
The University of Hong Kong's MMLAB jointly released PhysicalRSI 1.0, a self-evolving embodied intelligence system baseline, achieving a score of 36.27 and an average task success rate of 31.38% on RoboDojo, ranking first.
云启资本 ·
Stardust Intelligence completes new RMB 1 billion funding round at RMB 15 billion valuation
Stardust Intelligence recently completed a new funding round of RMB 1 billion, reaching a valuation of RMB 15 billion. The round was led by Toushe Donghai (Shenzhen Investment Holdings), with participation from Hongtai Fund, Ninghai Xingtaaihe Fund, and Shanheng Capital, among others, and existing shareholder Xianmu increased its investment. This is its fourth funding round in the past six months; its Series B valuation first exceeded RMB 10 billion in June 2026 and rose to RMB 15 billion three months later.