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Giving Physical AI A Hand Whitepaper
Why Physical AI Needs More Than Vision
Artificial intelligence has advanced rapidly in robotics. Robots can now see, plan, and make decisions with impressive accuracy. However, real-world interaction remains a major challenge.
In this whitepaper from Robotiq, the focus shifts to what happens at the point of contact—where gripping, force, and adaptability determine success.
Handling objects, adapting to variability, and maintaining consistent performance are still difficult tasks. In many cases, the limitation isn’t the AI model—it’s the hardware.
This resource explores how physical interaction, sensing, and end-of-arm tooling play a critical role in making AI-driven robotics work reliably in real environments.
What’s inside?
This resource highlights the key challenges and solutions shaping the next phase of robotics and automation.
Inside, you’ll learn:
• Why vision alone isn’t enough for reliable robotic manipulation
• How touch, force, and feedback improve performance
• The role of adaptive grippers in handling variability
• How tactile sensing supports better decision-making
• Why hardware is critical for scaling Physical AI
• How to bridge simulation and real-world deployment
Physical AI requires more than algorithms. It depends on systems that can sense, respond, and adapt in real-world conditions.
Who Benefits from this Resource?
This whitepaper is designed for teams evaluating or building real-world automation systems, including engineers developing robotic manipulation applications, manufacturers exploring AI-driven automation, and integrators working with variable or complex processes. It’s especially valuable for organizations looking to bridge the gap between simulation and real-world deployment, where consistent interaction and adaptability are critical to success.
