Physical AI, also called embodied AI, is AI paired with a robot that perceives, decides and acts in the real world. It lets machines interpret their environment, make decisions and carry out tasks autonomously — reaching, grasping, manipulating and interacting with objects. Because those actions depend on real physical contact, hardware and sensing play a central role in making Physical AI work.

Why is data the main bottleneck in Physical AI?

Physical AI needs far more real-world interaction data than exists today. Unlike text, that data cannot be scraped — it has to be generated one trajectory at a time, on physical robots. The rate at which reliable, contact-rich data is collected now limits how fast generalist robot policies improve.

Why isn't vision enough for robot manipulation?

Vision shows what an object looks like, not what it feels like to grasp. Forces, torques, pressure and vibration only register on contact — and those signals decide whether a grasp holds, an insertion succeeds, or a fragile part survives. The strongest manipulation models combine vision with force-torque and tactile data at the end-effector, which is why instrumented grippers and wrist sensors are becoming standard in Physical AI pipelines.

How does Robotiq support Physical AI foundation model training?

Robotiq provides the physical interaction and feedback layer Physical AI models need. Adaptive grippers, the FT-300-S force-torque sensor and the TSF-85 tactile fingertips generate synchronised, multimodal data — force, torque, pressure, vibration and proprioception — for policy learning. Standardised ROS packages and NVIDIA Isaac Sim assets bridge simulation and reality, while 17 years of industrial-grade hardware design makes the resulting data reliable at scale.

Which Robotiq products anchor a Physical AI stack?

Three product families cover the perception-to-action loop. Adaptive grippers: 2F-85 (85 mm stroke, 5 kg payload), 2F-140 (140 mm, 2.5 kg), and Hand-E (50 mm, 7 kg). Tactile fingertips: the TSF-85, which swaps onto the 2F-85 and 2F-140. Force-torque: the FT-300-S, a 6-DOF sensor with ±300 N range and 500% overload capacity. All integrate with ROS, ROS 2 and NVIDIA Isaac Sim.

How reliable is Robotiq hardware?

Robotiq grippers are built for industrial duty cycles. The 2F-85 and 2F-140 carry a 2-million-cycle warranty at IP40, and the Hand-E a 5-million-cycle warranty at IP67. In practice, most units run well beyond those numbers. The TSF-85 is rated for over 1.5 million cycles. That durability matters in Physical AI because every hour a robot stays running is an hour it can collect training data.

What does the TSF-85 tactile sensor measure?

The TSF-85 captures three signals at the fingertip. Pressure is read across a 28-taxel capacitive array for contact shape and intensity. Vibration is sampled at 1000 Hz to detect slip. An IMU tracks fingertip orientation. It swaps onto the 2F-85 and 2F-140 without reducing gripper stroke.

Software for Physical AI

Contact Core - Build models with contact-rich data

Contact Core is Robotiq's software for Physical AI. It gives model builders the simulation assets, control packages, and embedded intelligence to train manipulation models on Robotiq components and deploy them on real robots.

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Simulation

NVIDIA Isaac Sim Assets

Accurate physical models of Robotiq grippers and tactile sensors. Train and iterate on contact-rich manipulation in simulation.

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Access Tactile Sensors Extension

Control

Packages and Drivers

Control and data collection package for compatible Robotiq components.

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Skills

Embedded Intelligence

With compute built into the gripper, the 2FC runs grasp skills locally and in real time:

Grasp quality assessment
Incipient slip detection
Object pose estimation
Object centering
Advanced grip check

Adaptive Grippers

2F-85 & 2F-140

Simulation and Control Packages

Tactile Sensor Fingertips

TSF-85

Simulation and Control Packages

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Physical AI Grippers

2FC-85 & 2FC-140

Simulation, Control Packages and Embedded Intelligence.

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01 · SENSE

Capture contact

Timestamped and synchronized gripper, tactile, robot-state, and contact data from Contact Ready components

02 · TRAIN

Build the model

Use simulation assets, control packages and embedded tactile intelligence to generate, label, and feed contact-rich data into your Physical AI training pipelines.

03 · DEPLOY

Run on real components

Deploy on the same components you trained on. Reduce the translation work between model building and production hardware.

04 · IMPROVE

Close the loop

Capture new contact data from deployment, refine the model, and roll out improvements through the same toolchain.

Robot-agnostic

Contact Core is not robot dependent. Robotiq's Contact Ready components work across multiple robot brands — you choose the arm, we cover the contact layer.

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real time control installation basic usage API reference Robotiq support

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Tools

Contact Core - Build models with contact-rich data

Simulation

NVIDIA Isaac Sim Assets

Control

Packages and Drivers

Skills

Embedded Intelligence

Skills

Grasp quality assessment and incipient slip detection

Skills

Object pose estimation

Skills

Object centering

Adaptive Grippers

2F-85 & 2F-140

Tactile Sensor Fingertips

TSF-85

Physical AI Grippers

2FC-85 & 2FC-140

01 · SENSE

Capture contact

02 · TRAIN

Build the model

03 · DEPLOY

Run on real components

04 · IMPROVE

Close the loop

Robot-agnostic

Repositories

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