Booster Robotics' T1 humanoid robot kicks soccer balls hard enough to dent walls, raising serious safety questions about powerful robots operating near people.

Why it's nearly impossible to build a robot without China Building on the country's electric vehicle industry, Chinese companies are making robot parts at a scale and price point others can't match. Japan led the world in robotics for decades. More than 50 years ago, Japanese researchers captured imaginations with the first robot capable of grasping objects and walking on two legs. In 1984, a team in Japan built one that could read sheet music and play the piano. When Honda unveiled its first humanoid in 2000, it seemed to cement the country's lead.

When you purchase through links in our articles, we may earn a small commission. A budget pool robot that handles basic cleaning well enough, but it stands out most for how affordable it is. Beatbot's Sora line, introduced earlier this year, marked the robot producer's aggressive foray into lower-cost pool cleaning systems, with three models on sale at stair-stepped price points. The Sora 10 stands at the bottom of that price band, typically available for under $500, which is pretty much the bare minimum you can get away with paying for a pool robot that has any real value. So, what does $500 get you?

Humanoid robots are promising to acquire various skills by imitating human behaviors. However, existing algorithms are only capable of tracking smooth, low-speed human motions, even with delicate reward and curriculum design. This paper presents a physics-based humanoid control framework, aiming to master highly-dynamic human behaviors such as Kungfu and dancing through multi-steps motion processing and adaptive motion tracking. For motion processing, we design a pipeline to extract, filter out, correct, and retarget motions, while ensuring compliance with physical constraints to the maximum extent. For motion imitation, we formulate a bi-level optimization problem to dynamically adjust the tracking accuracy tolerance based on the current tracking error, creating an adaptive curriculum mechanism. We further construct an asymmetric actor-critic framework for policy training. In experiments, we train whole-body control policies to imitate a set of highly dynamic motions. Our method achieves significantly lower tracking errors than existing approaches and is successfully deployed on the Unitree G1 robot, demonstrating stable and expressive behaviors. The project page is https://kungfubot.github.io.

India's workers are training AI robots to take their jobs With a smartphone strapped to her head, Indian housewife Nagireddy Sriramyachandra films herself slicing mangoes to train artificial intelligence-powered robots to take on household tasks in the future. Earning 250 rupees ($2.6) for one hour of video, her mundane recordings are invaluable for global tech companies teaching machines how to move like humans in the real world. The 25-year-old is one of a growing army of thousands of AI system trainers in the world's most populous country. "Who else will give you 250 rupees an hour just for doing housework?" "I may get a robot myself in the future," she added.

Spatial referring is a fundamental capability of embodied robots to interact with the 3D physical world. However, even with the powerful pretrained VLMs, recent approaches are still not qualified to accurately understand the complex 3D scenes and dynamically reason about the instruction-indicated locations for interaction. To this end, we propose RoboRefer, a 3D-aware vision language model (VLM) that can first achieve precise spatial understanding by integrating a disentangled but dedicated depth encoder via supervised fine-tuning (SFT). Moreover, RoboRefer advances generalized multi-step spatial reasoning via reinforcement fine-tuning (RFT), with metric-sensitive process reward functions tailored for spatial referring tasks. To support SFT and RFT training, we introduce RefSpatial, a large-scale dataset of 20M QA pairs (2x prior), covering 31 spatial relations (vs.

Uncrewed ground vehicles have already been tested for defending the front line by the Ukrainian military. There's a received piece of wisdom among militaries around the world that whatever new technologies appear, in the end, foot soldiers are what matters. As British Army officer Field Marshal Archibald Wavell put it shortly after the second world war: "All battles and all wars are won in the end by the infantryman." This may now finally be changing. Robots in battle are about to reach a critical point for Ukraine. In May, it began the mass production of Legit, a low-cost robot capable of carrying a machine gun.

Imitation learning (IL) with generative models, such as diffusion and flow matching, has enabled robots to perform complex, long-horizon tasks. However, distribution shifts from unseen environments or compounding action errors can still cause unpredictable and unsafe behavior, leading to task failure. Therefore, early failure prediction during runtime is essential for deploying robots in human-centered and safety-critical environments. We propose FIPER, a general framework for Failure Prediction at Runtime for generative IL policies that does not require failure data. FIPER identifies two key indicators of impending failure: (i) out-of-distribution (OOD) observations detected via random network distillation in the policy's embedding space, and (ii) high uncertainty in generated actions measured by a novel action-chunk entropy score.

Could humanoid robots be heading for the battlefield? I've come to an industrial space in a tech-heavy area of San Francisco expecting to see a menacing humanoid robot solider doing something combat-like: the future of land-based warfare, perhaps. Instead, the black shiny faceless Phantom robot is engaged in free play, manipulating a bunch of coloured kids blocks. We need data from it just interacting with its environment [and] this is today's menu, explains Sankaet Pathak, co-founder and CEO of two-year-old start-up Foundation Robotics, which is developing Phantom for military and civilian applications. Later he pushes its 80kg steel-covered body around the room to demonstrate its stability and shows me how it walks.

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