A multi-agent reinforcement learning method that discretizes force feedback into ternary values (-1, 0, 1) makes two-robot cooperative grasping and transport more robust to changes in grasping force, object size, and shape.
The need for combining implicit and explicit communication in cooperative robotic systems,
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Cooperative Grasping and Transportation using Multi-agent Reinforcement Learning with Ternary Force Representation
A multi-agent reinforcement learning method that discretizes force feedback into ternary values (-1, 0, 1) makes two-robot cooperative grasping and transport more robust to changes in grasping force, object size, and shape.