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 synchronized contact-rich data interfaces they need 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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Control & data

ROS 2 packages

Control and data collection package for compatible Robotiq components.

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Data interfaces

Contact-rich data API

Synchronized gripper, tactile, robot-state, and contact data streams. One time-aligned source of truth for training and deployment.

Adaptive Grippers

2F-85 & 2F-140

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

Tactile Sensor Fingertips

TSF-85

Pressure, vibration, and proprioception delivered at the fingertips. Richer multimodal data for more capable manipulation models.

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Unified API

AI Coupling - Preview

Synchronizes gripper and tactile data and delivers it to AI pipelines and deployment environments with low latency.

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01 · SENSE

Capture contact

Stream synchronized gripper, tactile, robot-state, and contact data from Contact Ready components — in simulation or on real hardware.

02 · TRAIN

Build the model

Use Isaac Sim assets and ROS 2 packages to generate, label, and feed contact-rich data into your Physical AI training pipelines.

03 · DEPLOY

Run on real components

Deploy contact-based skills on the same Contact Ready 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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Future capability

Embedded Tactile Intelligence is coming to Contact Core.

Future releases are expected to add Embedded Tactile Intelligence — Robotiq's proprietary intelligence embedded at the wrist, extracting higher-level information about what is happening during contact.

The result: more efficient, more precise manipulation, plus more accurate labelling for model training.

Expected to include

Grip detection

Slip detection

Object orientation

Texture recognition

Object recognition

Stability prediction

Fast local responses

Automatic labeling

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