Home Business & Research SliceIt!: Simulation Based Reinforcement Learning for Robotic Food Slicing

SliceIt!: Simulation Based Reinforcement Learning for Robotic Food Slicing

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Teaching robots how to slice food ingredients is easier said than done. SliceIt! is a simulation based approach for training food slicing skills. The approach consists of:

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collecting a small dataset of real food-cutting examples, then calibrating high-fidelity simulations of knife-food cutting interactions and robot motion control. Reinforcement learning agents are trained in this calibrated simulation environment to learn optimal compliance control policies that modulate knife forces.

SliceIt!: Simulation-Based Reinforcement Learning for Compliant Robotic Food Slicing

Once the info is transferred to the robot, it will be able to perform food slicing tasks efficiently and safely.

[HT]

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