Empowering robots for socially compliant navigation is crucial for their acceptance in human-inhabited environments. The content discusses the challenges faced by traditional geometric navigation systems in adapting to social scenarios and proposes a hybrid paradigm that combines geometric and learning-based methods. Through experiments on real-world datasets and physical robots, the hybrid planner demonstrates improved social compliance over individual approaches.
The study introduces SCAND, a dataset of human tele-operated robot demonstrations designed for learning from demonstration research. It highlights the limitations of purely geometric or learning-based navigation systems in achieving social compliance across diverse scenarios. The proposed hybrid paradigm aims to address these challenges by integrating the strengths of both approaches.
Key points include defining social compliance based on alignment with human demonstrations, benchmarking different geometric navigation systems on SCAND, comparing geometric and learning-based planners' performance, and introducing a hybrid planner that switches between methods based on scenario complexity. The study emphasizes the importance of robust engineering during deployment to ensure stable performance.
Further exploration is needed to optimize the integration of geometric and learning-based methods in the hybrid paradigm for enhanced social robot navigation capabilities. The study serves as an initial step towards rethinking social robot navigation strategies through innovative hybrid approaches.
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by Amir Hossain... lúc arxiv.org 03-12-2024
https://arxiv.org/pdf/2309.13466.pdfYêu cầu sâu hơn