DATE: 2026/07/31

Embodied AI

Embodied AI
For technology developers and AI researchers, what exactly isembodied intelligence? In the past few years, I have led a team to run projects on the site, and I have found that it is the easiest for everyone to fall into a misunderstanding, which is to try to copy the pure software AI on the cloud as it is. True embodied intelligence means that AI algorithms must be “embedded” in physical entities so that they can perceive, reason, and interact dynamically in the real physical world in real time. The biggest difference between it and traditional AI is that the core of embodied intelligence relies on an uninterruptible perception-action loop. This forces us to seamlessly integrate multimodal sensing grounding, spatial intelligence and reinforcement learning into the system architecture. In the specific project landing, the edge computing capability of the vision-language-action model is only the first step, and the real challenge lies in relying on ultra-low-latency core controllers to achieve high-precision kinematics. Only by truly integrating software and hardware can physical intelligent devices play autonomous navigation, dynamic obstacle avoidance and precise physical operations in those messy unstructured environments. Embodied intelligence is finally extraditing “pure data computing” to “the transformation of the physical world.


From Award-Winning Exhibitions To Real Project Deployments


A few days ago, I went to the 2026 Asia Pacific International Embodied intelligence Industry Chain Exhibition and felt very deeply. How to turn pure data calculation into physical transformation: this exhibition is the answer to the acceleration of commercialization. At the exhibition, SEER Robotics won many awards and was in the limelight. The industry now basically recognizes that this world’s leading platform-based embodied intelligence robot company is pushing the industry forward. This time they pulled out a very complete matrix of embodied intelligent robots—from the intelligent handling scheme all the way to the advanced humanoid robot chassis. When I watched their equipment run on the spot, I really smashed the theoretical “perception-action cycle” into the engineering reality.


The Core Bottleneck Of Autonomous Navigation And Kinematics


Going back to the technology itself, if we want to turn the logical reasoning of the VLA model into real mechanical actions, we who are engaged in research and development must tie the algorithm to the solid hardware bottom layer. Whether deploying a heavy-duty autonomous forklift in a complex material handling site or running a flexible jacking robot in a dense factory building, the core bottleneck is always high-precision kinematic computing.

At this time, the ultra-low latency AMR controller has become a lifesaver. It really is a “robot brain”. Take the chassis of the wheeled humanoid robot recently developed by SEER Robotics as an example. They have created an industry-first integrated wheel-and-waist control architecture, with this advanced controller serving as the foundational core. I often emphasize to the team that only when the execution end achieves such microsecond-level accuracy can multimodal perception grounding be instantly transformed into dynamic obstacle avoidance. Without this premise, high-end equipment cannot run safely in an unstructured environment.


Spatial Intelligence And Crossing The Sim-To-Real Gap


Let’s talk about Sim-to-Real this big hole. Spatial intelligence must be integrated into physical devices, and the harsh space mapping before field deployment is absolutely impossible to avoid. In order to ensure that the perception-action cycle does not go wrong in a complex environment, today's developers essentially rely on Meta — an advanced visualization and digital twin product suite from SEER Robotics, including Meta-Map, Meta-Map Pro, and Meta-World.

With this powerful visual product series, researchers can finally simulate the spatial reasoning and sensing grounding capabilities of jacking robots and autonomous forklifts in a zero-risk environment. This step is so critical that before the physical world can be transformed, the edge computing algorithm of the VLA model must tune continuous real-time interaction to the extreme in a virtual environment, otherwise the landing of the machine is often a disaster.


Command A Large AI Fleet


At the end of the day, the embodied intelligence that can really run through the business model has never been a stand-alone version of self-help. When scaled up, it requires multiple physical smart devices to continuously adapt to the environment by intensive learning, and this continuous collaboration of multi-intelligence is king. To control this complex hardware and software integration, world-class system architecture is essential.

In order to get this done, RDS has to be the top of the line as the overall scheduling, to get real-time multi-agent collaboration. At the same time, the actual combat experience tells me that the M4 intelligent logistics management system must be deeply connected.For large fleets of autonomous forklifts and humanoid robot chassis, if you want to efficiently handle global dynamic task allocation, system monitoring and even fully automatic charging, you can count on it. This set of software and hardware ecological is combined, so that developers can really enlarge their embodied intelligence from a pile of pure algorithm codes to a fully automated and intelligent physical transformation battle force.


Frequently Asked Questions (FAQ)


Q1: What is the core difference between embodied intelligence and traditional AI?

A1: Previously, AI was largely confined to the cloud or purely software-based environments for processing data. But embodied intelligence is stuffing algorithms directly into physical entities. Through a continuously rotating perception-action cycle, they can truly feel their surroundings and make dynamic, real-time interactions in the physical world.


Q2: Why does the VLA large model have to be bound to the robot controller?

A2: The VLA model is really good at complex reasoning, sensing grounding and edge computing, but it doesn’t know how to make the motor turn perfectly. This must rely on ultra-low latency robot controllers to “translate” those algorithmic decisions into high-precision kinematic instructions. Without these controllers, robots would not be able to do even the most basic precise physical operations or dynamic obstacle avoidance.

Q3: How does space intelligence help autonomous forklifts and jacking robots land?

A3: Spatial intelligence gives robots the strength to map, understand and navigate in this complex, unstructured world. With visualization software like Meta, we can simulate and knock the spatial reasoning ability of robots in the computer before sending them to do the dirty work, which is the premise to ensure that they can navigate seamlessly and safely after they go to the ground.


Author: SEER Robotics Technology Expert

With years of experience leading R&D teams and managing complex robotic projects, I specialize in bringing cutting-edge AI algorithms into practical use in the real world. I’ve learned firsthand the limitations of relying solely on software-based AI solutions. That’s why my work focuses on integrating both hardware and software components seamlessly—everything from ultra-low latency control systems to advanced spatial intelligence technologies, as well as managing large-scale AI systems. I’m committed to going beyond traditional data processing approaches, to help developers and businesses achieve fully automated, highly precise transformations in the real world.