Agibot, which led global humanoid robot shipments in 2025 with 5,168 units, demonstrates production-ready technology through massive robot performance event in Shanghai.

An Indian university is facing backlash after one of its professors was caught falsely presenting a Chinese-made robot dog at a major artificial intelligence summit, it has reportedly since been asked to leave, as the institution's own. "You need to meet Orion. This has been developed by the Centre of Excellence at Galgotias University," Neha Singh, a professor of communications, told Indian state-run broadcaster DD News this week. The episode has drawn sharp criticism and has cast an uncomfortable spotlight on India's AI ambitions. The embarrassment was amplified by Electronics and Information Technology Minister Ashwini Vaishnaw, who shared the video clip on his official social media account before the backlash.

Should we be impressed or worried by China's humanoid robot display? - video China's dancing robots: how worried should we be? Dancing humanoid robots took centre stage on Monday during the annual China Media Group's Spring Festival Gala, China's most-watched official television broadcast. They lunged and backflipped (landing on their knees), they spun around and jumped. The display was impressive, but prompted some to wonder: if robots can now dance and perform martial arts, what else can they do? Experts have mixed opinions, with some saying the robots had limitations and that the display should be viewed through a lens of state propaganda.

An example prompt for constructing PDDL models of the action "close a

A.1 More Information About The Continuous Environment We provide a detailed description of the continuous environments with constrained settings: Let's consider an optimization problem in the form of: minimize α After analyzing Table C.1 and Figure C.1, it is evident that the B2CL, MEICRL, and InfoGAIL-ICRL Although MMICRL-LD shows a notable improvement, its performance remains mediocre in environments involving three types of agents. Table C.2 presents the mean std results of all algorithms in Mujoco. Figure C.2 depicts the distribution of x-coordinate values Half-Cheetah, Blocked Swimmer, and Blocked Walker environments. It demonstrates the algorithm's capacity to infer and restore incorrect We employ "/" to separate the results for various We present the mean std results calculated over 20 runs for each random seed.Method Setting 1 Setting 2 Setting 3 Setting 4 Feasible Cumulative Rewards B2CL 0.24 0 .40 Figure C.1: The feasible cumulative rewards (left two columns of the first three rows and second-to-last row) and constraint violation rate (right two columns of the first three rows and last row). The first row showcases the expert demonstration, followed by the results of B2CL, MEICRL, InfoGAIL-ICRL, MMICRL-LD, and MMICRL algorithms.