Grasping Deformable Objects via Reinforcement Learning with Cross-Modal
Attention to Visuo-Tactile Inputs
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Key Points
Proposed method uses reinforcement learning to control a robotic gripper for grasping deformable objects by integrating visual and tactile data.
The inclusion of cross-modal attention allows the robot to effectively learn from multi-modal sensing data, achieving superior performance in various tasks.
Experimental results demonstrate that this method outperforms traditional data fusion techniques in environments with changing conditions.
The approach addresses the challenges of handling fragile objects while ensuring that they are grasped without damage.
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Implication
This approach improves grasping performance of robotic grippers in dynamic environments, highlighting the benefits of visuo-tactile information.