Advancing intelligence and reasoning for humanoid robots
Image courtesy of Tara Winstead from Pexels © 2021
Humanoid robots are moving closer to real-world deployment – and their progress depends on physical intelligence and real-time reasoning. With the recent announcement of general availability of NVIDIA Jetson Thor, Analog Devices (ADI) is further accelerating the development of humanoids and autonomous mobile robots (AMRs).
Combining ADI’s edge sensing, precision motion control, power integrity and deterministic connectivity with Jetson Thor’s compute capabilities, Holoscan Sensor Bridge and Isaac Sim, creates a path to scale reasoning-enabled robots from simulation to deployment.
Jetson Thor redefines what’s possible for robotics. With a NVIDIA Blackwell GPU, transformer engine, Multi-Instance GPU (MIG), a 14-core Arm Neoverse V3AE CPU, and up to 128GB of LPDDR5X memory, it delivers 2070 FP4 TFLOPS server-class AI compute in a mobile power envelope. Its high-throughput I/O, including 4×25GbE, provides the bandwidth needed to fuse dense multimodal sensing in real time.
This capability makes NVIDIA Jetson Thor the first platform to run robotics foundation models at scale, from vision-language to vision-language-action models, enabling robots to move beyond perception into reasoning and physically intelligent behaviour. That aligns directly with ADI’s R&D focus: sensing, perception, control and connectivity that makes such reasoning actionable in the real world with high physical accuracy.
“For the first time, robots can understand complex tasks. ADI delivers the precision physical substrate which, combined with NVIDIA Jetson Thor’s reasoning, responds to real world physics in real time,” says Paul Golding, VP of Edge AI, ADI. “Together, we’re taking humanoids from simulation to shift ready deployment.”
The key to reasoning and physical intelligence
Robotics foundation models compress decades of challenges into perception-rich humanoids capable of dexterous, human-speed manipulation. But their real breakthrough is in reasoning: integrating multimodal inputs to plan, adapt and act in real time.
As noted on our third-quarter 2025 earnings call, ADI’s content opportunity grows with this shift. Every joint needs precise current, position and torque control. Every contact needs tactile and sensory feedback. Humanoids require multiple perception nodes. Each node is a signal chain, perception stack, and power-management opportunity that must run deterministically and with low latency – ADI’s strength.
Closing the Sim2Real gap
ADI is embedding robotics foundation models into the ADI development stack, closing the Sim2Real gap so its hardware behaves in NVIDIA Isaac Sim as it will in the real world. The goal is to build the most physically accurate robotics content in NVIDIA Isaac Sim, enabling teams to iterate at simulation speed and then scale seamlessly to real systems with ADI hardware and NVIDIA Jetson Thor.
Physical intelligence fuses sensing, actuation and policy learning and reasoning so robots can execute precise industrial tasks. It demands high-fidelity edge sensing, energy efficient and functionally safe power, deterministic connectivity to central compute, and a digital twin that closes the Sim2Real loop.
This can now be achieved: NVIDIA Jetson Thor is the compute substrate, and ADI delivers the signal chain fidelity, power integrity, and content that make it actionable.
“With NVIDIA Jetson Thor as the brain and ADI’s high-fidelity sensing, signal-chain fidelity and deterministic connectivity as the nervous system, we take robots from NVIDIA Isaac Sim to the factory floor with physical accuracy – faster,” says Golding
The future of reasoning and physical intelligence
ADI sees growing demand for humanoids across logistics, agriculture and surgical robotics. Frontier use cases include dexterous manipulation of cable assemblies in data centres and automotive manufacturing – tasks that reward speed, precision, and repeatability. ADI’s collaboration on digital twins and policy training in NVIDIA Isaac Sim will address this demand and shorten timelines from concept to production humanoids using ADI’s stack with NVIDIA Jetson Thor.
The same stack – high-fidelity sensing, deterministic connectivity, and digital-twin grounded policy training – extends to other platforms, such as AMRs, where ADI is working with NVIDIA to incorporate ADI perception into cuVSLAM via its IMUs, depth sensors and wheel encoders.
