Edge-Native Temporal--Graph Dual-Expert Inference for Dexterous Grasp
Overview
This research proposes a dual-expert architecture, DualExpert, combining a Temporal Convolutional Network (TCN) and a Finger Graph Filter Network (FGFN), to classify grasped objects and predict finger-specific stop events using only joint-angle and actuator-load feedback. The system achieves 93.78% trajectory-level classification accuracy and 95.25% F1 score for stop prediction, with low inference latency suitable for edge deployment.
From arXiv Robotics — research abstracts
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- arXiv Robotics — research abstractsSignalEdge-Native Temporal--Graph Dual-Expert Inference for Dexterous Grasp Classification
This research proposes a dual-expert architecture, DualExpert, combining a Temporal Convolutional Network (TCN) and a Finger Graph Filter Network (FGFN), to classify grasped objects and predict finger-specific stop events using only joint-angle and actuator-load feedback. The system achieves 93.78% trajectory-level classification accuracy and 95.25% F1 score for stop prediction, with low inference latency suitable for edge deployment.
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