activity
20192021
most citedDeep Reinforcement Learning for the Control of Robotic Manipulation: A Focussed Mini-Review

181 citations · 222 across the 5 of their papers we have counts for

collaborators

5 papers

cs.RO2021181 cited

Deep Reinforcement Learning for the Control of Robotic Manipulation: A Focussed Mini-Review

Rongrong Liu, Florent Nageotte, Philippe Zanne +2

Deep learning has provided new ways of manipulating, processing and analyzing data. It sometimes may achieve results comparable to, or surpassing human expert performance, and has…

cs.RO20211 cited

Wearable Sensors for Spatio-Temporal Grip Force Profiling

Rongrong Liu, Florent Nageotte, Philippe Zanne +2

Wearable biosensor technology enables real-time, convenient, and continuous monitoring of users behavioral signals. Such include signals relative to body motion, body temperature,…

cs.RO20205 cited

Correlating grip force signals from multiple sensors highlights prehensile control strategies in a complex task-user system

Birgitta Dresp-Langley, Florent Nageotte, Philippe Zanne +1

Wearable sensor systems with transmitting capabilities are currently employed for the biometric screening of exercise activities and other performance data. Such technology is gene…

cs.RO202017 cited

Sensors for expert grip force profiling: towards benchmarking manual control of a robotic device for surgical tool movements

Michel de Mathelin, Florent Nageotte, Philippe Zanne +1

STRAS (Single access Transluminal Robotic Assistant for Surgeons) is a new robotic system for application to intraluminal surgical procedures. Preclinical testing of STRAS has rece…

eess.IV201918 cited

An adaptive and fully automatic method for estimating the 3D position of bendable instruments using endoscopic images

Paolo Cabras, Florent Nageotte, Philippe Zanne +1

Background. Flexible bendable instruments are key tools for performing surgical endoscopy. Being able to measure the 3D position of such instruments can be useful for various tasks…