Safely Imitating Predictive Control Policies for Real-Time Human-Aware Manipulator Motion Planning: A Dataset Aggregation Approach
This paper proposes a dataset-aggregation approach for imitating a nonlinear model predictive control law via deep neural networks, to safely allow a robot manipulator to share its workspace with a human operator. As the robot approaches the human, its speed is gradually reduced using the ȁ...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10819386/ |
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| Summary: | This paper proposes a dataset-aggregation approach for imitating a nonlinear model predictive control law via deep neural networks, to safely allow a robot manipulator to share its workspace with a human operator. As the robot approaches the human, its speed is gradually reduced using the “speed and separation monitoring” framework. Specific time-varying upper bounds are explicitly imposed on the control input generated by the deep neural network through a “safety filter” based on real-time numerical optimization. The proposed method is experimentally tested on a UR5 manipulator, comparing the performance of different neural network structures and types of training. As a result, it is shown that the dataset-aggregation approach provides better performance with respect to a “naive” approach to training, and that the presence of the safety filter is indeed needed to avoid the violation of the speed-and-separation-monitoring constraints. |
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| ISSN: | 2169-3536 |