Adexis
Adexis is building the somatosensory cortex for Physical AI, giving robots the power of touch through advanced tactile sensing technologies and processing algorithms. The company develops sensor hardware and machine learning models that translate pressure, texture, and force feedback into real-time motor commands, enabling dexterous manipulation beyond what vision-based systems can achieve. Their integration framework is designed to work with existing robotic platforms and control systems.
- Robotics
- Artificial Intelligence
- Industrial Automation
- Hardware
- Manufacturing / Industry 4.0
Funding
Founders
Founder details are not available yet.
Product
Problem
Current robotic systems rely primarily on vision and proprioception, leaving them "numb" to the physical properties of objects they handle. This sensory gap prevents machines from performing dexterous manipulation tasks that require nuanced touch feedback, such as grasping fragile items or assembling intricate components. Without tactile intelligence, robots cannot achieve the fine motor control needed for advanced physical tasks.
Solution
Adexis is building the somatosensory cortex for Physical AI, a specialized layer that gives machines the power of touch. The company develops tactile sensing technologies and processing algorithms that enable robots to interpret physical contact, pressure, texture, and force feedback in real time. By integrating this touch-based intelligence into robotic systems, Adexis aims to solve the dexterity problem that currently limits physical AI applications. Their approach combines advanced sensor hardware with machine learning models that translate tactile signals into actionable motor commands, bringing machines closer to physical general intelligence.
Target Audience
Adexis targets robotics manufacturers, industrial automation companies, and research institutions developing advanced physical AI systems that require dexterous manipulation capabilities.
Features
- Proprietary tactile sensor technology that captures high-resolution pressure and texture data
- Machine learning algorithms that process touch signals into real-time motor control commands
- Integration framework designed to work with existing robotic platforms and control systems
- Focus on dexterous manipulation capabilities beyond current vision-based approaches
- Research-driven development targeting physical general intelligence applications