ADI adopts NVIDIA Jetson Thor to advance physical intelligence and reasoning for humanoids

Humanoid robots are moving closer to real-world deployment—and their progress depends on physical intelligence and real-time reasoning. Analog Devices Inc. (ADI) announced their collaboration with NVIDIA for the general availability of NVIDIA Jetson Thor to further accelerate the development of humanoids and autonomous mobile robots (AMRs).

By 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, they are creating a path to scale reasoning-enabled robots from simulation to deployment.

New benchmark for physical intelligence
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 128 GB of LPDDR5X memory, it delivers 2070 FP4 TFLOPS server-class AI compute in a mobile power envelope. Its high-throughput I/O, including 4×25 GbE, 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 behavior. 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.

Paul Golding, VP of Edge AI, ADI, said: “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 realworld physics in real time. Together, we’re taking humanoids from simulation to shiftready deployment.”

Foundation models: 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 their 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
They are embedding robotics foundation models into the ADI development stack, closing the Sim2Real gap so that their hardware behaves in NVIDIA Isaac Sim, as it will in the real world. ADI’s 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.

They can now achieve this: NVIDIA Jetson Thor is the compute substrate, and ADI delivers the signal chain fidelity, power integrity, and content that make it actionable.

What ADI brings to humanoids?

• High-fidelity edge sensing for contact-rich manipulation: ADI provides novel multimodal tactile sensing in development, plus ToF depth, high-accuracy IMUs, joint encoders, and multi-axis force/torque sensors to capture contact and proprioception with precision.
• Precision motion and functionally safe power control: ADI offers drivers and control for current, position, and torque—along with advanced multi-turn magnetic sensors—to deliver accurate, energy-efficient, and safe actuation.
• Deterministic connectivity to central compute: ADI’s capabilities include time-synchronized data paths, integrated with Holoscan via custom operators optimized for our data-fabric stack, to enable bounded-latency ingest of dense sensor and perception flows into NVIDIA Jetson Thor.
• Simulation and digital-twin fidelity: ADI’s high-quality sensor models and parameterized device behavior for NVIDIA Isaac Sim/Omniverse matches ADI hardware, improving policy transfer and task completion from simulation to real systems.

How ADI’s robotics stack maps to Jetson Thor?

• Holoscan Sensor Bridge delivers deterministic ingest with NVIDIA JetPack 7, streaming synchronized ADI sensor/actuator data into NVIDIA Jetson Thor’s GPU/CPU with bounded latency using Holoscan operators optimized for ADI’s data stack.
• 4×25 GbE interconnect provides high-throughput, time-aligned fusion across hands, arms, torso, and perception nodes, with ADI connectivity expertise keeping the sense–reason–act loop synchronized and low latency.
• Thor’s 2070 FP4 TFLOPS capacity supports foundation models like NVIDIA Isaac GR00T alongside VLM/LLM reasoning, while ADI tactile, ToF depth, IMU, and encoder inputs enrich training and runtime policies, bringing physical accuracy to real-time reasoning.
• MIG-based workload partitioning enables isolated GPU slices for locomotion, grasp planning, perception, and VLA policies, simplifying functional decomposition.

“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,” added Paul Golding.

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 centers and automotive manufacturing — tasks that reward speed, precision, and repeatability.

Their 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 they are working with NVIDIA to incorporate ADI perception into cuVSLAM via our IMUs, depth sensors and wheel encoders.  They are just getting started. NVIDIA Jetson Thor opens a new chapter in their partnership with NVIDIA. Follow ADI’s website and LinkedIn for updates and releases you can use in your projects.