2026 World Robot Conference Concludes: Who Is Sprinting Ahead in the Physical AI Race?

[Core Event and Technical Overview]

The 2026 World Robot Conference was held in Yizhuang, Beijing, bringing together over a thousand robots of various forms in an exhibition space of approximately 60,000 square meters, covering multiple segments such as industrial manufacturing, home services, and commercial delivery. Unlike previous editions that focused primarily on technology demonstrations, this year's conference introduced a Procurement Day for the first time, where professional buyers entered with demand lists, enabling direct supply-demand matching. On-site sales exceeded 200 million yuan in total. This shift marks that embodied intelligence is accelerating from the laboratory demonstration and concept validation stage to the tipping point of large-scale commercial deployment, with the industry ecosystem's maturity significantly improved.

From a technical narrative perspective, the focus of embodied intelligence is rapidly shifting from early "performance stunts" — such as somersaults, dancing, and simple grasping — to "real-world operations," where robots complete tasks with actual economic value in real physical environments. The concentrated demonstration of scenarios such as cooking, playing ball, delivering coffee, sorting parcels, and scooping cat litter reflects substantial breakthroughs in the underlying algorithms' generalization ability, environmental adaptability, and task robustness. The physical AI world depicted by Jensen Huang — where AI steps beyond the boundaries of screens and interacts with the physical world through various robotic bodies — is rapidly turning from a vision into reality.

The participating institutions at this conference covered the world's leading AI laboratories, robot manufacturers, core component suppliers, and system integrators, forming a complete industry chain display from chip computing power, perception modules, motion control, to the upper-level AI brain. Notably, domestic Chinese embodied intelligence companies performed particularly prominently at this conference. Local manufacturers such as Unitree Robotics, Agibot (Zhiyuan Robotics), and Xingdong Jiyuan (Star Dynamics) brought multiple mass-production robots for industrial and consumer scenarios, demonstrating China's rapid catching-up momentum in the embodied intelligence track.

[Technical Principles and Core Breakthroughs]

The technical core of embodied intelligence lies in building an end-to-end closed loop of perception-decision-execution. The current mainstream technical route adopts the VLA (Vision-Language-Action) model architecture, which unifies a visual encoder, a language understanding module, and an action generation network into a single neural network for training. The visual side typically uses ViT (Vision Transformer) or its variants to extract environmental features, the language side uses large-scale pre-trained language models to understand natural language instructions, and the action side outputs joint torques or end-effector pose commands through a policy network. This unified architecture enables robots to understand complex semantic instructions and directly map them into physical actions, significantly reducing the information loss in traditional hierarchical control architectures.

In terms of training paradigms, embodied intelligence is evolving from pure simulation training to simulation-reality hybrid training. The introduction of World Model technology allows robots to simulate physical interaction results in a latent space, thereby pre-training policy networks in virtual environments, and then transferring them to real scenes through domain randomization and domain adaptation techniques. Representative models such as RT-2 and OpenVLA have demonstrated the feasibility of this route; after training on millions of robot manipulation data, they can complete zero-shot or few-shot tasks on unseen objects and environments. In addition, the introduction of the MoE (Mixture of Experts) architecture enables the model to activate different expert networks for different task types while keeping the overall parameter scale manageable, significantly improving inference efficiency and task generalization.

Long-context processing capability is equally critical in embodied intelligence. Robots need to maintain environmental state memory, task progress tracking, and historical action sequences during continuous interaction, which places higher demands on the model's context window. Current cutting-edge solutions extend the context window to tens of thousands of tokens by introducing state-space models (such as Mamba) or long-context Transformer variants, enabling robots to handle complex tasks with multiple steps and long time spans. Meanwhile, advances in quantization inference — such as 4-bit and 8-bit quantization — make it possible to deploy embodied intelligence models with billions of parameters on edge computing devices, providing computational support for real-time robot response capabilities.

[Industry Background and Competitive Landscape]

The global embodied intelligence track has formed a competitive landscape dominated by China and the United States. In the United States, Figure AI, through its deep cooperation with OpenAI, has taken a first-mover advantage in the field of general-purpose humanoid robots. Its Figure 02 robot has already been deployed in factories and has demonstrated autonomous learning and remote supervision capabilities. Tesla Optimus, relying on the multimodal perception and reinforcement learning technologies accumulated by Tesla in autonomous driving, has unique advantages in production cost control and large-scale manufacturing. Agility Robotics' Digit robot has achieved commercial operation in logistics and warehousing scenarios, and its reinforcement learning-based motion control algorithm excels in dynamic obstacle avoidance and gait stability.

The Chinese camp demonstrated strong catching-up momentum at this conference. Unitree Robotics, leveraging its deep accumulation in quadruped robots, has transferred its technology to humanoid robot platforms; its H1 and G1 series have reached internationally advanced levels in motion performance and cost control. Agibot (Zhiyuan Robotics) focuses on industrial scenarios, and its Yuanzheng (Expedition) series robots have been deployed in multiple domestic factories, demonstrating reliability in precision assembly and material handling tasks. In addition, startups such as Xingdong Jiyuan (Star Dynamics) and Agibot are rapidly iterating their products, forming a complete domestic supply chain from body manufacturing to the AI brain.

From an industry chain perspective, competition in embodied intelligence has surpassed the contest among single robot manufacturers and extended to upstream core components and underlying AI infrastructure. NVIDIA has built an "Android ecosystem" for embodied intelligence through its Isaac Sim simulation platform and GR00T foundation model, attempting to establish standards at the platform level. NVIDIA's Jetson series edge computing modules provide robots with a complete computing solution from perception to inference. Meanwhile, domestic computing chips such as Huawei's Ascend series are also accelerating adaptation to embodied intelligence scenarios, providing alternative solutions for domestic robots. This full-stack competition from chips to models to robot bodies will profoundly affect the industry landscape in the next three to five years.

[Implications for Developers and Industrial Deployment]

From an engineering implementation perspective, the core challenge facing embodied intelligence lies in balancing generalization ability and reliability. Although current VLA models perform excellently in demonstration scenarios, there is still significant room for improvement in success rates when facing complex factors such as lighting changes, object deformation, and dynamic disturbances in real industrial environments. In actual deployment, developers typically need to combine traditional control algorithms as a safety net, forming a hybrid architecture of "AI policy + classical control." In addition, the high cost of data collection is a key bottleneck constraining model generalization. The cost of collecting each high-quality robot manipulation data sample is far higher than that of pure text or image data, prompting the industry to actively explore data augmentation solutions based on synthetic data and simulation pre-training.

At the hardware level, the memory and computing power requirements of embodied intelligent robots place significant pressure on deployment costs. A typical VLA model requires at least 24GB of memory for inference, while a complete system supporting real-time visual processing and policy inference typically requires dual GPUs or a dedicated edge computing platform. Edge computing modules such as the NVIDIA Jetson AGX Orin provide relatively feasible solutions, but their power consumption and thermal design impose additional requirements on the robot body's engineering integration. For industrial scenarios, developers also need to consider the robot's protection level, battery life, and maintenance costs, which together determine the total cost of ownership (TCO) of embodied intelligence solutions.

In terms of toolchains and ecosystem support, platforms such as NVIDIA Isaac Sim, Google DeepMind's MuJoCo, and the open-source Isaac Lab provide developers with a complete toolchain from simulation training to physical deployment. ROS 2, as the de facto standard for robot operating systems, offers a reliable middleware foundation for embodied intelligence applications through its distributed architecture and real-time communication capabilities. However, APIs and development frameworks from different vendors still suffer from fragmentation, and cross-platform migration costs are high. The advancement of industry standardization organizations and the efforts of the open-source community are gradually alleviating this issue, but in the short term, developers still need to perform adaptation development for specific platforms.

[Comprehensive Review and Key Points]

The core signal released by the 2026 World Robot Conference is that embodied intelligence has moved from the technology validation period into the commercial deployment period. The establishment of Procurement Day and the achievement of 200 million yuan in sales mark a shift in market demand from watchful waiting to actual purchasing, and the industry value chain has begun to form a positive cycle. Behind this transformation are continuous breakthroughs in underlying AI technologies — the maturation of VLA model architectures, the popularization of world model training paradigms, and the declining cost of edge computing power — which together have driven embodied intelligence from the laboratory to factories and homes. In the next one to two years, we can expect to see more embodied intelligence products achieve large-scale deployment in specific vertical scenarios, especially in areas with clear demand and quantifiable ROI, such as logistics and warehousing, industrial assembly, and home services.

Looking ahead to future evolution trends, embodied intelligence will accelerate along three main lines. First, at the foundation model layer, more general-purpose physical AI large models will emerge, similar to the GPT series in the language model domain. Such models will be pre-trained on large-scale multimodal data and, with a small amount of fine-tuning, can be adapted to multiple robot platforms and task types. Second, simulation-to-reality transfer technologies will significantly lower deployment barriers. Advances in world models and domain randomization enable strategies trained in simulation environments to transfer to real scenes with lower sample requirements. Third, human-robot collaboration trust mechanisms will become a key research direction, including interpretable decision-making, safety boundary definition, and the design of human supervision interfaces. These technologies will determine whether embodied intelligence can truly integrate into human work and life scenarios. The match point moment of physical AI is essentially the moment of building human-robot collaborative trust.

This page provides an editorial summary based on publicly available information. It is not a republished article. Use the source link below for the original report.