Daniele Riva

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About

Daniele Riva is a robotics researcher whose work focuses on advancing intelligent interaction control and sensorless force estimation for industrial manipulators. His key research areas include optimal switching control for physical human-robot interaction, external torque estimation for position-controlled robots, and safe compliant control strategies. Riva’s most notable contribution is the development of a sensorless optimal switching impact/force controller, published in 2021, which has garnered 23 citations. This work addresses the critical challenge of enabling robots to seamlessly switch between different optimized controllers depending on the operative situation, maximizing task performance in diverse interaction scenarios—from delicate assembly to forceful impact. Additionally, his research on external joint torque estimation using an Extended Kalman Filter (5 citations) provides a method for standard position-controlled industrial robots to safely interact with their environment without requiring expensive torque sensors, enabling outer compliance control for tasks like assembly. Riva’s work is particularly valuable for bridging the gap between traditional industrial robotics and the growing demand for flexible, safe, and intelligent automation. His contributions are essential reading for researchers and students working on force control, human-robot collaboration, and sensorless estimation in robotics.