Vision got robots looking. Language got them reasoning. But the moment a robot has to actually do something with its hands such threading a cable, screw a nut on a bolt, or manipulate a deformable object it can't fully see, touch becomes the missing piece. And touch is one of the hardest unsolved problems in physical AI.

In this episode of The Thinking Machine, I sit down with Tao Yu, Director of Dexterous AI Group at Analog Devices, to unpack why ADI is now building a humanoid hand platform with tactile sensing at its core.

Here are just a few topics get into:

- Why dexterous manipulation is the "crown jewel" of robotics, and why it's a hardware + sensor + data + AI problem all at once

- What tactile sensing actually involves: pressure, vibration, temperature, and the multimodal fusion problem

- Taxels (tactile pixels) and how human skin's ~1mm resolution sets the bar for fingertip sensors

- Why collecting good manipulation data is so hard when the human operator can't feel what the robot feels

- Real2Sim2Real with physics as the interface: how ADI thinks about modeling sensor non-idealities so policies trained in simulation actually transfer

About Tao Yu:
Tao Yu is the Director of Dexterous AI Group at Analog Devices, Inc. (ADI), where he leads research on multimodal tactile sensing for robotics and Physical AI. His work sits at the intersection of ADI's 60-year analog semiconductor heritage and the emerging demand for production-grade tactile sensors in dexterous manipulation and humanoid robotics.

Tao holds a PhD from MIT and has published research spanning tactile sensing, wireless sensor platforms, and signal processing. At ADI, he and his team are pioneering industrial-grade tactile sensors that capture force, vibration, and temperature at sub-millimeter resolution, as well as Physical AI research on tactile-enabled dexterous manipulation.

Tao on LinkedIn: linkedin.com/in/taoyumit/

Thanks to Lightwheel for making this episode possible.
Learn about how Lightwheel is making physical AI successful at https://lightwheel.ai

Diana Wolf Torres joins The Thinking Machine podcast to break down everything we saw at GTC 2026. Diana is the author of the DROIDS robotics newsletter and the Deep Learning with the Wolf AI newsletter, and we're both NVIDIA-invited creators who have been covering this space together since GTC 2025.

We start with what stood out from Jensen's keynote — the shift to measuring tokens per watt, the Vera Rubin chip and why it might make your Blackwell obsolete before you install it, and NVIDIA's trillion-dollar hardware number.

Then we get into OpenClaw, the DGX Spark, and why agentic AI is something you want to be experimenting with now.

From there we hit the show floor and go company by company through the robotics that impressed us most — from robots filling real labor gaps to humanoids working in tandem. We also dig into robot safety, why it matters more than most people realize, and the role simulation plays in getting there.

Timestamps:
00:00 - Intro
03:20 - Keynote Overview
09:29 - Build-a-Claw and OpenClaw
17:52 - Caterpillar's Autonomous Vehicles
21:48 - Unitree's H2 Robot
25:33 - Disney's Olaf Robot
35:13 - WORKR's industrial robots
41:55 - Psyonic's robotic hand and tactile sensing
43:30 - Humanoid and KinectIQ
49:08 - Noble Machines
52:15 - Generalist and GEN-1
57:11 - OpenMind
1:03:21 - Syncere - Lume
1:16:04 - Fauna Robotics and RIVR
1:20:19 - Agibot and Agibot World 2026
1:22:45 - Asimov

About Diana Wolf Torres:
Diana Wolf Torres is a Silicon Valley-based AI and robotics writer who publishes two Substack newsletters: Deep Learning with the Wolf, covering AI developments and ethics, and DROIDS!, focused on robotics news, founder interviews, and field reporting. She produces on-the-ground content from conferences like RoboBusiness and maker events across the Bay Area, translating complex technical developments for broad audiences.

Diana on LinkedIn: https://www.linkedin.com/in/diana-wolf-torres/

DROIDS! Newsletter: droids.substack.com/

Deep Learning with the Wolf: https://dianawolftorres.substack.com/

Website: www.droidsnewsletter.com/

Thanks to Lightwheel for making this episode possible.
Learn about how Lightwheel is making physical AI successful at https://lightwheel.ai

Steve Xie spent years leading simulation at Cruise and NVIDIA before founding Lightwheel — and in that time he watched simulation go from a tool that was "great for showcasing to investors" to what he believes will become the core infrastructure layer for all of physical AI.

In this episode, we sit down with Steve to break down Lightwheel's three-pillar framework for simulation infrastructure: World, Behavior, and Evaluation — and why getting all three right is what separates serious simulation from everything else. We also get into the physical measurement factory, the data scale that Lightwheel is hitting in 2025, and why RoboFinals may become the industry-standard benchmark for frontier robotics models.

In this episode we discuss:
- Why simulation started as a "toy" at Cruise and how Steve changed that.
- The difference between a visually realistic asset and a physically accurate one.
- Why Lightwheel operates one of the world's largest robotics arm factories.
- How egocentric data and simulation data work together in the behavior layer.
- The data pyramid: why real teleoperation is just the tip of the iceberg.
- Why academic benchmarks are maxing out and what RoboFinals does differently.
- How World, Behavior, and Eval form a flywheel — not just a stack.
- The agentic core Steve sees sitting at the center of it all.
- Why robotics data collection may eventually require a billion people.

About Steve Xie:
Steve Xie is the Co-Founder and CEO of Lightwheel. He brings over a decade of experience building simulation infrastructure across some of the most demanding environments in physical AI. Steve led the simulation department at Cruise during the early days of autonomous vehicles, then joined NVIDIA where he worked closely with the Omniverse team and developed his vision for simulation as next-generation physical AI infrastructure. He founded Lightwheel to build that infrastructure from the ground up.

Follow Steve on LinkedIn: https://www.linkedin.com/in/stevexiecbs/

About Lightwheel:
Lightwheel is building the simulation infrastructure that physical AI needs to succeed — spanning world generation, behavior data, and evaluation. Their products include SimReady Assets, EgoSuite for egocentric data collection, and RoboFinals, an industrial-grade robotics evaluation platform co-developed with NVIDIA.

SimReady Assets: https://simready.com/
Learn more at: https://lightwheel.ai/
Resources mentioned in this episode:
LW-BenchHub: https://github.com/LightwheelAI/LW-BenchHub
LeIsaac: https://github.com/LightwheelAI/leisaac
IsaacLab-Arena: http://github.com/isaac-sim/IsaacLab-Arena

Thanks to Lightwheel for making this episode possible. Learn about how Lightwheel is making physical AI successful at https://lightwheel.ai