Exploring Word Embeddings and 3D Quantization for Human Hand Motion Prediction in Shared Wordspace with Robot

This research introduces an innovative framework for the prediction of human hand motion in a shared workspace with a robot, fostering safe and efficient human-robot collab-oration. Due to the absence of benchmark datasets for this task, we created a custom dataset of human hand trajectories by orchestrating intentional collisions between humans and robots during data collection. To enable efficient processing and prediction, our framework leverages the quantization of sensitive human hand positions into small 3D cells. These cells are later modeled for learning the embedding for better human hand motion prediction. Notably, our framework outperforms the baseline model. Al-though the enhanced predictive power entails extra computation for finding the Nearest Neighbors (NN) during quantization, we efficiently manage this cost through the integration of off-the-shelf information retrieval frameworks like ANNOY. This strategic approach ensures real-time performance and maintains precise approximate NN results.