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Bioinspired trajectory modulation for effective slip control in robot manipulation.

Kiyanoush Nazari1, Willow Mandil1,2, Marco Santello3

  • 1School of Computer Science and LIAT, University of Lincoln, Lincoln, UK.

Nature Machine Intelligence
|July 25, 2025
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Summary
This summary is machine-generated.

Robotic grasp stability can be improved using trajectory modulation instead of traditional grip force control. This new method, optimized with a predictive control framework, enhances robotic adaptability in challenging environments.

Keywords:
Electrical and electronic engineeringMathematics and computing

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Area of Science:

  • Robotics
  • Control Systems
  • Artificial Intelligence

Background:

  • Stable robotic grasping is crucial for dexterous manipulation.
  • Traditional slip control relies on grip force modulation.
  • Limitations exist in grip force control for certain tasks.

Purpose of the Study:

  • To investigate trajectory modulation as an alternative slip control strategy for robotic manipulation.
  • To compare the effectiveness of trajectory modulation against grip force control.
  • To develop an optimized trajectory modulation policy using a predictive control framework.

Main Methods:

  • Development of a slip control policy based on trajectory modulation.
  • Comparison with a conventional grip-force-based control approach.
  • Integration of a data-driven action-conditioned forward model within a model predictive control (MPC) framework.

Main Results:

  • Trajectory modulation significantly outperformed grip force control in specific robotic manipulation scenarios.
  • The predictive control framework, incorporating a forward model, was key to optimizing trajectory modulation.
  • The proposed approach enhances grasp stability in dynamic and unstructured environments.

Conclusions:

  • Trajectory modulation offers a robust and effective alternative for slip prevention in robotic manipulation.
  • Model predictive control with data-driven forward models is essential for optimizing this strategy.
  • This approach improves robotic system adaptability and performance in challenging conditions.