Nikolas Sacchi
Orcid: 0000-0002-7600-1136
  According to our database1,
  Nikolas Sacchi
  authored at least 15 papers
  between 2021 and 2025.
  
  
Collaborative distances:
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Bibliography
  2025
    Eur. J. Control, 2025
    
  
A Flexible Clinician-Friendly Interface for Upper Limb Rehabilitation with Industrial Robots.
    
  
    Proceedings of the International Conference On Rehabilitation Robotics, 2025
    
  
    Proceedings of the Artificial Intelligence in Medicine - 23rd International Conference, 2025
    
  
  2024
    J. Frankl. Inst., 2024
    
  
    Proceedings of the 17th International Workshop on Variable Structure Systems, 2024
    
  
    Proceedings of the European Control Conference, 2024
    
  
Human-Robot Ergonomic Handover via Deep Neural Network Based Adaptive Integral Sliding Mode Control.
    
  
    Proceedings of the European Control Conference, 2024
    
  
  2023
Design of a Deep Neural Network-Based Integral Sliding Mode Control for Nonlinear Systems Under Fully Unknown Dynamics.
    
  
    IEEE Control. Syst. Lett., 2023
    
  
Neural network based integral sliding mode control of systems with time-varying state constraints.
    
  
    Proceedings of the 31st Mediterranean Conference on Control and Automatio, 2023
    
  
Integral Sliding Modes Generation via DRL-Assisted Lyapunov-Based Control for Robot Manipulators.
    
  
    Proceedings of the European Control Conference, 2023
    
  
  2022
    IEEE Control. Syst. Lett., 2022
    
  
    Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2022
    
  
  2021
Deep Reinforcement Learning of Robotic Prosthesis for Gait Symmetry in Trans-Femoral Amputated Patients.
    
  
    Proceedings of the 29th Mediterranean Conference on Control and Automation, 2021
    
  
A Configuration Space Reference Generation Approach for Real-Time Collision Avoidance of Industrial Robot Manipulators.
    
  
    Proceedings of the 2021 European Control Conference, 2021
    
  
Scenario-Based Collision Avoidance Control with Deep Q-Networks for Industrial Robot Manipulators.
    
  
    Proceedings of the 2021 60th IEEE Conference on Decision and Control (CDC), 2021