CES 2026 showed that humanoid and embodied AI systems still have a long way to go before delivering real-world value, particularly in homes. At the same time, there is a growing sense that the path to deployment is becoming clearer. A consensus has emerged across platforms: multi-camera perception, often wrist-mounted, paired with VLA models, is sufficient for most tasks. Increasingly, tactile hands and VTLA software are added. There was a clear split between industrial and home-care humanoids.

What could possibly go wrong? The 4-foot-tall humanoid robot that's in front of me seems, quite honestly, a bit drunk. After 30 seconds or so it abruptly stops, then strides toward me with an arm outstretched. The little robot is at the World Artificial Intelligence Conference, on the banks of the Huangpu river in Shanghai. The convention center is teeming with humanoids --dancing ones, box-toting ones, robot dog-walking ones doing circuits around trade show booths. A few lie slumped in a corner as their batteries recharge. A Unitree humanoid robot modified for experimental purposes at the BAAI.

CES always has its share of attention-grabbing robots. But this year in particular seemed to be a landmark year for robotics. The advancement in AI technology has not only given robots better "brains," it's enabled new levels of autonomy and given rise to an ambitious, if sometimes questionable, vision for our robot-filled future. From sassy humanoids to AI-powered pets and chore-handling assistants, we sought out as many cute, strange and capable robots as we could find in Las Vegas. These are the ones that made the biggest impression.

Sharpa's ping-pong playing, blackjack dealing humanoid is working overtime at CES 2026 The company's super dexterous robotic hand, SharpaWave, allows it to pull individual playing cards from a deck. There were no idle hands at Sharpa's CES booth. The company's humanoid may have been the busiest bot at show, autonomously playing ping-pong, dealing blackjack games and taking selfies with passersby. The hand has 22 active degrees of freedom, according to the company, allowing for precise and intricate finger movements. It mirrored my gestures as I wiggled my hand in front of its camera, getting everything mostly right, which was honestly pretty cool.

We tackle the challenges of synthesizing versatile, physically simulated human motions for full-body object manipulation. Unlike prior methods that are focused on detailed motion tracking, trajectory following, or teleoperation, our framework enables users to specify versatile high-level objectives such as target object poses or body poses. To achieve this, we introduce MaskedManipulator, a generative control policy distilled from a tracking controller trained on large-scale human motion capture data. This two-stage learning process allows the system to perform complex interaction behaviors, while providing intuitive user control over both character and object motions. MaskedManipulator produces goal-directed manipulation behaviors that expand the scope of interactive animation systems beyond task-specific solutions.

The development of athletic humanoid robots has gained significant attention as advances in actuation, sensing, and control enable increasingly dynamic, real-world capabilities. RoboCup, an international competition of fully autonomous humanoid robots, provides a uniquely challenging benchmark for such systems, culminating in the long-term goal of competing against human soccer players by 2050. This paper presents the hardware and software innovations underlying our team's victory in the RoboCup 2024 Adult-Sized Humanoid Soccer Competition. On the hardware side, we introduce an adult-sized humanoid platform built with lightweight structural components, high-torque quasi-direct-drive actuators, and a specialized foot design that enables powerful in-gait kicks while preserving locomotion robustness. On the software side, we develop an integrated perception and localization framework that combines stereo vision, object detection, and landmark-based fusion to provide reliable estimates of the ball, goals, teammates, and opponents. A mid-level navigation stack then generates collision-aware, dynamically feasible trajectories, while a centralized behavior manager coordinates high-level decision making, role selection, and kick execution based on the evolving game state. The seamless integration of these subsystems results in fast, precise, and tactically effective gameplay, enabling robust performance under the dynamic and adversarial conditions of real matches. This paper presents the design principles, system architecture, and experimental results that contributed to ARTEMIS's success as the 2024 Adult-Sized Humanoid Soccer champion.

A fully autonomous humanoid returns machine-fed shuttles in a motion-capture arena; overlaid arcs show an incoming (blue) and returned (orange) trajectory. Abstract--Humanoid robots have demonstrated strong capabilities for interacting with static scenes across locomotion, manipulation, and more challenging loco-manipulation tasks. Y et the real world is dynamic, and quasi-static interactions are insufficient to cope with diverse environmental conditions. As a step toward more dynamic interaction scenarios, we present a reinforcement-learning-based training pipeline that produces a unified whole-body controller for humanoid badminton, enabling coordinated lower-body footwork and upper-body striking without motion priors or expert demonstrations. Training follows a three-stage curriculum--first footwork acquisition, then precision-guided racket swing generation, and finally task-focused refinement--yielding motions in which both legs and arms serve the hitting objective. For deployment, we incorporate an Extended Kalman Filter (EKF) to estimate and predict shuttlecock trajectories for target striking. We also introduce a prediction-free variant that dispenses with EKF and explicit trajectory prediction. T o validate the framework, we conduct five sets of experiments in both simulation and the real world. In simulation, two robots sustain a rally of 21 consecutive hits. Moreover, the prediction-free variant achieves successful hits with comparable performance relative to the target-known policy. In real-world tests, both prediction and controller modules exhibit high accuracy, and on-court hitting achieves an outgoing shuttle speed up to 19.1 m/s with a mean return landing distance of 4 m. These experimental results show that our proposed training scheme can deliver highly dynamic while precise goal striking in badminton, and can be adapted to more dynamics-critical domains. Humanoid platforms have been proposed as general-purpose embodied agents for human-compatible skills [1, 2, 3, 4, 5, 6, 7]. Despite rapid progress in locomotion and motion imitation, agile, contact-rich interactions with fast-moving objects under tight reaction windows remain underexplored.

The integration of Supernumerary Limbs (SLs) on humanoid robots poses a significant stability challenge due to the dynamic perturbations they introduce. This thesis addresses this issue by designing a novel hierarchical control architecture to improve humanoid locomotion stability with SLs. The core of this framework is a decoupled strategy that combines learning-based locomotion with model-based balancing. The low-level component consists of a walking gait for a Unitree H1 humanoid through imitation learning and curriculum learning. The high-level component actively utilizes the SLs for dynamic balancing. The effectiveness of the system is evaluated in a physics-based simulation under three conditions: baseline gait for an unladen humanoid (baseline walking), walking with a static SL payload (static payload), and walking with the active dynamic balancing controller (dynamic balancing). Our evaluation shows that the dynamic balancing controller improves stability. Compared to the static payload condition, the balancing strategy yields a gait pattern closer to the baseline and decreases the Dynamic Time Warping (DTW) distance of the CoM trajectory by 47\%. The balancing controller also improves the re-stabilization within gait cycles and achieves a more coordinated anti-phase pattern of Ground Reaction Forces (GRF). The results demonstrate that a decoupled, hierarchical design can effectively mitigate the internal dynamic disturbances arising from the mass and movement of the SLs, enabling stable locomotion for humanoids equipped with functional limbs. Code and videos are available here: https://github.com/heyzbw/HuSLs.

DeepMind's chief says he envisions Gemini as an operating system for physical robots. The company has hired Aaron Saunders to help make that a reality. Google DeepMind has hired the former Chief Technology Officer of Boston Dynamics as the company pushes deeper into robotics. Aaron Saunders, who is partly responsible for giving the world backflipping and dancing machines, joined as the VP of hardware engineering earlier this month. The hire is a key part of CEO Demis Hassabis' vision for Gemini to become a sort of robot operating system, similar to how Google supplies its Android software to an array of smartphone manufacturers.