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Monday

  1. SenseTimeSelected

    SenseTime Releases Empirical Study on GPT-6-Astra Embodied Intelligence and the SenseNova-RoboRSI Framework

    SenseTime has released an empirical study on GPT-6-Astra's embodied intelligence, covering 12 simulation benchmarks and 8 real robot platforms, and has simultaneously open-sourced the SenseNova-RoboRSI framework and a 27B model whose operating cost is about 1/50 that of GPT-6-Astra. Under a unified protocol (ICL plus up to 10x interaction budget), GPT-6-Astra achieved SOTA on all 12 benchmarks: RoboDojo rose from 27.6% at 1x budget to 81.9% at 10x, DexJoCo reached 87.3% versus the previous SOTA of 40.3%, and the LIBERO benchmark was pushed to saturation. The study also notes that the inference cost for simple tasks such as putting a pen into a pen holder is about $55, and that real robots overheat and wait times are too long; the conclusion is therefore that GPT-6-Astra has largely crossed the threshold of embodied capability, but practical-grade embodied intelligence has not yet arrived.

    SenseTime has released an empirical study on GPT-6-Astra's embodied intelligence, covering 12 simulation benchmarks and 8 real robot platforms, and has simultaneously open-sourced the SenseNova-RoboRSI framework and a 27B model whose operating cost is about 1/50 that of GPT-6-Astra. Under a unified protocol (ICL plus up to 10x interaction budget), GPT-6-Astra achieved SOTA on all 12 benchmarks: RoboDojo rose from 27.6% at 1x budget to 81.9% at 10x, DexJoCo reached 87.3% versus the previous SOTA

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    #editorial#Papers/Research#Embodied AI#Open-source ecosystem

Wednesday

  1. Story1 reports · 1 facts

    SenseTime Open-Sources SenseNova-RoboRSI Framework and 27B Embodied Model

    SenseTime has open-sourced the SenseNova-RoboRSI framework and a 27B embodied model, while also releasing an empirical study on embodied intelligence for GPT-6-Astra. The study covers 12 simulation benchmarks and 8 real robot platforms. Under a unified protocol (ICL plus up to 10x interaction budget), GPT-6-Astra achieved SOTA on all 12 benchmarks, with RoboDojo's success rate rising from 27.6% at 1x budget to 81.9% at 10x, and DexJoCo reaching 87.3%, compared with the previous SOTA of 40.3%. The study also notes that simple tasks such as putting a pen into a pen holder cost about $55 in inference, and that real robots overheat and wait too long; the conclusion is that GPT-6-Astra has essentially crossed the threshold of embodied capability, but practical-grade embodied intelligence has not yet arrived. The open-source framework packages each benchmark as an independent Docker image and provides environment interaction and scoring through a unified HTTP API; the 27B model raises the success rate on RoboCasa365 from 26.0% to 46.5%, reduces task token overhead by 73%, and costs about 1/50 of GPT-6-Astra per completed task.

    SenseTime has open-sourced the SenseNova-RoboRSI framework and a 27B embodied model, while also releasing an empirical study on embodied intelligence for GPT-6-Astra. The study covers 12 simulation benchmarks and 8 real robot platforms. Under a unified protocol (ICL plus up to 10x interaction budget), GPT-6-Astra achieved SOTA on all 12 benchmarks, with RoboDojo's success rate rising from 27.6% at 1x budget to 81.9% at 10x, and DexJoCo reaching 87.3%, compared with the previous SOTA of 40.3%. Th

    Matched fieldsSummary

Friday

  1. Leitech

    AgiBot, together with Tsinghua University and Infinigence AI, releases the CEL collective experience learning paradigm

    AgiBot, together with Tsinghua University and Infinigence AI, has released the collective experience learning paradigm CEL, which enables robots to use their peers' real operational trajectories as learning material, with humans only responsible for judging outcomes. On five real-machine tasks—flower arranging, clothes folding, phone charging, stool assembly, and phone packaging—the policies trained with CEL all exceeded the average level of 6 human teleoperators, leading by up to 16.7 percentage points, with an overall success rate of 97.5%; in the phone packaging task, the number of robots participating in training expanded from 16 to 96, and the success rate rose from 86.7% to 98.3%. The accompanying FERT algorithm uses failure trajectories to identify weaknesses and retrieves successful segments for prioritized sampling, while the FATS fault-tolerant asynchronous training system completed a cumulative 9,834 rounds of task collection in the 96-robot experiment, with the machine-time loss caused by 30 failures accounting for only 1.3% of the cluster's total operating time.

    AgiBot, together with Tsinghua University and Infinigence AI, has released the collective experience learning paradigm CEL, which enables robots to use their peers' real operational trajectories as learning material, with humans only responsible for judging outcomes. On five real-machine tasks—flower arranging, clothes folding, phone charging, stool assembly, and phone packaging—the policies trained with CEL all exceeded the average level of 6 human teleoperators, leading by up to 16.7 percentag

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    #hot-signal#Papers/Research#Embodied AI#English text is being prepared.

Wednesday

  1. QbitAI

    LimX Dynamics Releases FluxVLA Engine V2 Technical Report, Adding Online Reinforcement Learning and Real-Robot Deployment Validation

    LimX Dynamics has updated the second version of the technical report for its open-source engineering foundation FluxVLA Engine. Compared with the first version in September, it adds post-training experiments with online reinforcement learning and expands the coverage of simulation evaluation and real-robot deployment. The report shows that 14 policies were integrated into the same LIBERO closed-loop evaluation, and the inference frequency of 8 model-device combinations increased to 2.31–9.64 times; after online reinforcement learning, SmolVLA's success rates on LIBERO-10 and the ten tasks of RoboTwin Clean increased by 10.0 and 15.4 percentage points, respectively. The deployment pipeline has been validated on platforms including ALOHA, dual-arm Franka, Rebot, and LimX Oli.

    LimX Dynamics has updated the second version of the technical report for its open-source engineering foundation FluxVLA Engine. Compared with the first version in September, it adds post-training experiments with online reinforcement learning and expands the coverage of simulation evaluation and real-robot deployment. The report shows that 14 policies were integrated into the same LIBERO closed-loop evaluation, and the inference frequency of 8 model-device combinations increased to 2.31–9.64 tim

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    #chinese#English text is being prepared.#Embodied AI#English text is being prepared.
  2. QbitAI

    LimX Dynamics updates the FluxVLA Engine V2 technical report, adding online reinforcement learning and real-robot deployment evaluations

    LimX Dynamics has updated the second version of the technical report for its open-source embodied engineering foundation FluxVLA Engine. Compared with the first version in September, it newly adds post-training experiments with online reinforcement learning and expands the coverage of simulation evaluations and real-robot deployment. The report shows that after online reinforcement learning, SmolVLA's success rates on the ten tasks of LIBERO-10 and RoboTwin Clean increased by 10.0 and 15.4 percentage points, respectively; the inference frequency of 8 model-and-device combinations increased to 2.31–9.64 times. On the real-robot side, the deployment pipeline from model checkpoint to closed-loop execution was validated on ALOHA, the dual-arm Franka, Rebot, and the self-developed humanoid robot LimX Oli.

    LimX Dynamics has updated the second version of the technical report for its open-source embodied engineering foundation FluxVLA Engine. Compared with the first version in September, it newly adds post-training experiments with online reinforcement learning and expands the coverage of simulation evaluations and real-robot deployment. The report shows that after online reinforcement learning, SmolVLA's success rates on the ten tasks of LIBERO-10 and RoboTwin Clean increased by 10.0 and 15.4 perce

    Matched fieldsBody

    #chinese#Papers/Research#Embodied AI#Open-source ecosystem

Friday

  1. Cailian Press

    Microsoft's AI diagnostic tool simulates a doctor's step-by-step questioning, with accuracy on complex cases about twice that of general practitioners

    Microsoft's research team published in the journal Science an AI diagnostic tool powered by GPT-5, which achieved a diagnostic accuracy of about 83% on complex cases selected from the New England Journal of Medicine, compared with about 36% for general practitioners with reference materials and about 22% without them. Through a mechanism called the "Diagnostic Coordinator," the tool simulates multiple clinical experts analyzing cases collaboratively, deciding step by step which medical history to ask about and which tests to order, and continuously updating diagnostic hypotheses. The research team's next step is to collaborate with Boston's Beth Israel Deaconess Medical Center to test emergency triage capabilities; Microsoft has not yet specified the timing or form of commercialization.

    Microsoft's research team published in the journal Science an AI diagnostic tool powered by GPT-5, which achieved a diagnostic accuracy of about 83% on complex cases selected from the New England Journal of Medicine, compared with about 36% for general practitioners with reference materials and about 22% without them. Through a mechanism called the "Diagnostic Coordinator," the tool simulates multiple clinical experts analyzing cases collaboratively, deciding step by step which medical history t

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    #chinese#Papers/Research#Agents#OpenAI
  2. MIT News · Artificial Intelligence

    MIT CSAIL and Tianjin University launch X-Hinges design software, enabling 3D printing of self-sensing objects with built-in sensors

    Researchers at MIT CSAIL and Tianjin University have developed the X-Hinges design software, which lets users customize shapes and motion patterns; the software automatically embeds two different materials to separately fabricate sensors and conductive traces, avoiding signal interference. The tool supports motion in three directions—compression, up-down, and left-right—and was used to create a glove that can teach a robot to grasp, a small lamp that dims via pinching, and a device that can recognize placed objects. The research team will present the results at the ACM UIST conference in early November; currently, the circuit layout of each printed item differs slightly and requires recalibration using a computer vision system.

    Researchers at MIT CSAIL and Tianjin University have developed the X-Hinges design software, which lets users customize shapes and motion patterns; the software automatically embeds two different materials to separately fabricate sensors and conductive traces, avoiding signal interference. The tool supports motion in three directions—compression, up-down, and left-right—and was used to create a glove that can teach a robot to grasp, a small lamp that dims via pinching, and a device that can reco

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    #academic#Papers/Research#Open-source ecosystem#Multimodal

Tuesday

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Friday

Saturday