Learning social navigation from demonstrations with conditional neural processes
Sociability is essential for modern robots to increase their acceptability in human environments. Traditional
techniques use manually engineered utility functions inspired by observing pedestrian behaviors to achieve social navigation.
However, social aspects of navigation are diverse, changing across different types of environments, societies, and population
densities, making it unrealistic to use hand-crafted techniques in each domain. This paper presents a data-driven navigation
architecture that uses state-of-the-art neural architectures, namely Conditional Neural Processes, to learn global and local
controllers of the mobile robot from observations. Additionally, we leverage a state-of-the-art, deep prediction mechanism to
detect situations not similar to the trained ones, where reactive controllers step in to ensure safe navigation. Our results
demonstrate that the proposed framework can successfully carry out navigation tasks regarding social norms in the data. Further,
we showed that our system produces fewer personal-zone violations, causing less discomfort.
Article outline
- Introduction
- Related work
- Hybrid path planning
- Global path planning
- Local path planning
- Social navigation
- Our method
- I – data-driven global controller
- Training the data-driven global controller
- Querying the trained data-driven global controller
- II – data-driven local controller
- Training the data-driven local controller
- Querying the trained data-driven local controller
- III – failure prediction module
- IV – hand-crafted reactive controller
- Experiments and results
- Analysis of the generated global trajectories
- Analysis of the local controller: Evasive maneuvers
- Comparison of local controllers
- Performance of the complete system
- Contribution of the Failure Prediction Module
- Scalability of CNP
- Conclusion
- Acknowledgements
-
References
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