Bioinspired trajectory modulation for effective slip control in robot manipulation

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.