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Simulation-Aided Handover Prediction From Video Using Recurrent Image-to-Motion Networks
Summary
Deep neural networks enable robots to predict future poses for safer, more dynamic collaboration. This visuomotor learning approach improves cooperative tasks by analyzing motion videos for trajectory prediction.
Area of Science:
- Robotics
- Artificial Intelligence
- Machine Learning
Background:
- Deep neural networks offer advancements in visuomotor robot learning.
- Predicting future poses is crucial for human-robot and robot-robot collaboration dynamics and safety.
Purpose of the Study:
- To propose a novel recurrent neural architecture for visuomotor robot learning.
- To enable accurate prediction of robot trajectories from motion videos for cooperative tasks.
Main Methods:
- Developed a recurrent neural architecture to transform variable-length motion videos into robot trajectory parameters.
- Utilized a simulation environment to expand the training database and enhance network generalization.
- Trained models on both synthetic and real-world data.
Main Results:
- The proposed architecture accurately predicts handover trajectories, even with limited input frames.
- Models trained on combined synthetic and real data outperformed those trained on single data sources.
- Successfully executed handover tasks with uncalibrated robots using computed trajectories.
Conclusions:
- The novel recurrent neural architecture effectively predicts robot trajectories for cooperative tasks.
- Combining synthetic and real data improves model performance and generalization.
- The approach facilitates cooperative tasks like object handover with uncalibrated robots.
