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Found 2 papers, 1 papers with code
Stranger Danger! Identifying and Avoiding Unpredictable Pedestrians in RL-based Social Robot Navigation
Reinforcement learning (RL) methods for social robot navigation show great success navigating robots through large crowds of people, but the performance of these learning-based methods tends to degrade in particularly challenging or unfamiliar situations due to the models' dependency on representative training data.
Context-Adaptive Deep Neural Networks via Bridge-Mode Connectivity
The deployment of machine learning models in safety-critical applications comes with the expectation that such models will perform well over a range of contexts (e. g., a vision model for classifying street signs should work in rural, city, and highway settings under varying lighting/weather conditions).
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