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Machine learning

Advances in Robotics: New Algorithms Improve Gripper Performance

Strategic Insights

— 1 min read

Italian researchers have made significant strides in robotic grippers, tackling the limitations of their performance in various industrial scenarios. With financial support from Camozzi Automation S.p.A., a team from the Italian Institute of Technology proposed novel algorithms that aim to enhance gripper functionality. The algorithms enable safe gripping of items with diverse sizes and hardness at high finger velocities, as well as stiffness estimation within a wide range. The experiments conducted on bench trials and a fully integrated robotic platform demonstrate a substantial improvement in gripper performance when exploiting these algorithms.

Key Takeaways:

  • The researchers proposed specific algorithms to improve the performance of robotic grippers, allowing for safe gripping of items with diverse sizes and hardness at high finger velocities.
  • The algorithms enable the implementation of functions not available on commercial end-effectors, such as estimating stiffness in a wide range (up to ca. 92 N/mm).
  • Experiments on two different setups demonstrate a clear enhancement of gripper performance when using the proposed algorithms.
  • The study concluded that the novel algorithms can be implemented using solely intrinsic sensors, eliminating the need for mechanical adjustments or additional sensors.
  • The research is a peer-reviewed study, showcasing the quality and reliability of the findings.
  • Additional authors of the study include Marco Maggiali and Rocco A. Romeo.

Statistics:

  • The grippers can safely grip items at high finger velocities, up to 16.5 mm/s.
  • The proposed algorithms allow for stiffness estimation within a wide range, up to ca. 92 N/mm.
  • The experiments were conducted on two different setups: bench trials and a fully integrated robotic platform.
  • The study demonstrated a clear enhancement of gripper performance when using the proposed algorithms.
  • The research was financially supported by Camozzi Automation S.p.A.

Sources:

  • Stiffness Estimation and Finger-object Impact Detection With a Robotic Gripper Using Intrinsic Sensors. Sensors and Actuators A-physical, 2025;388.
  • Italian Institute of Technology, iCub Tech Facil, Via San Quir 19D, I-16163 Genoa, Italy.
  • Camozzi Automation S.p.A.

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