KUALA LUMPUR, June 16 (Bernama) -- ABB Robotics has announced a collaboration with California-based bionics company PSYONIC to advance robotic gripping and dexterity using real-world manipulation data from human prosthetic use.
By combining the PSYONIC Ability Hand with an ABB GoFa cobot, the collaboration will explore how touch and motion data generated through human prosthetic use can be used to train robots to perform delicate, variable tasks that have traditionally been difficult to automate.
“As we develop the next generation of physical AI, robots will learn and understand the world as we do. This collaboration with PSYONIC will help to close the long-standing gap between human and robot dexterity, opening up new possibilities for a wide range of industries,” said ABB Robotics President, Marc Segura.
ABB Robotics in a statement said the collaboration will explore new applications across numerous industries, including automotive, aerospace, packaging and logistics, and life sciences.
By enabling robots to perform repetitive, ergonomically challenging or difficult-to-scale tasks, the collaboration aims to improve productivity, flexibility and workplace safety while enhancing human-robot collaboration.
PSYONIC is working closely with ABB Robotics' research and development team to explore how touch-enabled manipulation can support next-generation autonomous robotics applications.
The PSYONIC Ability Hand combines myoelectric control, touch sensing and compliant mechanics in a lightweight, multi-articulating design. Its pressure sensors and vibration feedback system enable users to detect contact, grip force and release, while flexible fingers naturally conform to irregular and deformable objects.
ABB Robotics’ GoFa cobot provides the accuracy and repeatability required for industrial-grade deployment, ensuring that subtle variations in grip force, finger positioning and movement can be consistently executed and evaluated.
The collaboration reflects ABB Robotics' efforts to advance Autonomous Versatile Robotics by combining robotics, artificial intelligence and real-world manipulation data to develop more adaptable robotic systems.
-- BERNAMA