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DEEP Robotics unveils five research results around embodied brain DEEPRobotics-Brain

2026-11-01 Category: Briefs Comments(0)

DEEP Robotics said it has published multiple papers around its self-developed embodied brain DEEPRobotics-Brain and released five research results: world model DR-WM-1.0, embodied foundation model DR-VLA-2.0, embodied agent DR-Agent, policy optimization framework DR-APO and data closed-loop platform DR-Benchmark. The company said the five results cover the full technology chain of the embodied brain and outline a clear architecture for the system.

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Briefs

  • RoboScience raises several hundred million yuan in A2 funding round

    Chinese embodied-AI startup RoboScience had completed an A2 funding round worth several hundred million yuan. The round was co-led by Amber Capital, Hongruida Investment and Shanghai Semiconductor Industry Investment, with Shuang Capital, 360 Strategic Investment, Guolian Tongbao, Jinding Capital and platforms of several listed companies participating. Proceeds will be used to deepen its presence in embodied intelligence, focusing on continued iteration and upgrading of large models, advancing proof-of-concept validation and scaled deployment in core scenarios including logistics, retail and industry, and further strengthening its high-end talent pool.

  • AGIBOT launches 24-hour convenience-store livestream to test humanoid robots at work

    Chinese robotics company AGIBOT had launched a 24-hour convenience-store challenge livestream titled “Robot Workers Can’t Stop,” with six Genie G2 robots serving as store employees. The robots are handling tasks including greeting customers, making coffee, serving prepared food, picking and restocking goods and cleaning tables, working continuously for 24 hours under real-time online monitoring. Using core technologies including the GO-3 foundation model and the Genie Master embodied agent, AGIBOT aims to validate robots' continuous operation and autonomous service capabilities in a real operating scenario and move humanoid robots from demonstrations to real jobs.

  • DeepSeek Harness team outlines “Super-agent” design for more autonomous AI systems

    The DeepSeek Harness team said in a technical article on its official WeChat account that it has outlined a “Super-agent” design aimed at building more autonomous agent systems. DeepSeek said gains in model capabilities have spurred the emergence of personal agents, but existing mainstream agent designs struggle to support multi-tasking and team collaboration at scale, with multi-task limits tied to human attention. The team said its approach uses a two-tier architecture, event-driven operation, asynchronous multi-channel interaction, separation of storage and computation and an external watchdog to overcome these limits. The system takes over cumbersome session management and context synchronization at the underlying layer, allowing humans to move from execution details to higher-level decision-making and acceptance. DeepSeek said internal Super-agent instances built on the approach are already running continuously, participating in issue reproduction and fixes, code review responses and iteration of DeepSeek Harness itself. When team members report software problems in group chats, the agent can identify potential defects even without being mentioned and try to reproduce the issue and find fixes in the background.

  • UBTech launches Thinker-Sim embodied AI simulation platform

    UBTech has launched Thinker-Sim, an embodied intelligence simulation platform built around Real2Sim2Real, creating a one-stop technical system covering the digitalization of real-world scenarios, large-scale synthetic simulation data and robot capability evaluation.

  • Light of the Internet Expo opens in Wuzhen with 671 exhibitors

    The 2026 Light of the Internet Expo, a key part of the World Internet Conference Wuzhen Summit, opened on Monday, with 671 companies from 54 countries and regions showcasing more than 1,000 new products and technologies.

  • DEEP Robotics unveils five research results around embodied brain DEEPRobotics-Brain

    DEEP Robotics said it has published multiple papers around its self-developed embodied brain DEEPRobotics-Brain and released five research results: world model DR-WM-1.0, embodied foundation model DR-VLA-2.0, embodied agent DR-Agent, policy optimization framework DR-APO and data closed-loop platform DR-Benchmark. The company said the five results cover the full technology chain of the embodied brain and outline a clear architecture for the system.

  • China's PrimeBOT unveils PrimeSim world model, tops WorldSimProbe LIBERO leaderboard

    China's PrimeBOT has launched PrimeSim, a world-model simulator for robot training, and said it topped the WorldSimProbe LIBERO leaderboard, setting a new benchmark for robot simulation performance. PrimeSim builds an interactive virtual world in which robots can trial-and-error and learn continuously, enabling efficient policy validation and reinforcement learning. In tests, reinforcement learning using PrimeSim raised policy success rates to 73.2% from 61.7%.

  • D-Robotics, Unitree team up to launch embodied AI education solution

    Chinese robotics firms D-Robotics and Unitree Robotics made their first joint appearance as exhibitors and announced a deep collaboration, launching an embodied intelligence education solution built on the Sunrise S100 compute box as its core computing platform and combining Unitree's Go2 quadruped robot and R1 humanoid robot. The solution covers hardware, curricula, practice projects integrating coursework and competitions, and teaching-environment development, providing complete support for primary and secondary schools to systematically deliver embodied AI education.
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