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From Learning to Relearning: A Framework for Diminishing Bias in Social Robot Navigation
Juana Valeria Hurtado1, Laura Londoño1, Abhinav Valada1
1Department of Computer Science, University of Freiburg, Freiburg im Breisgau, Germany.
Frontiers in Robotics and AI
|April 12, 2021
Summary
Robots are learning social navigation from humans, but this can lead to unfairness. This study proposes a framework to reduce bias in robot navigation, ensuring fairer human-robot interactions and promoting equitable societal dynamics.
Area of Science:
- Robotics and Machine Learning
- Human-Robot Interaction
- Artificial Intelligence Ethics
Background:
- Robots are moving into domestic and urban environments, requiring social navigation skills for human safety and comfort.
- Current methods for teaching robots social navigation often rely on human observation, which can inadvertently perpetuate societal biases.
- Unfairness in robot navigation can lead to discrimination and segregation, hindering robot acceptance.
Purpose of the Study:
- To investigate a framework for diminishing bias in social robot navigation models.
- To enable robots to plan and adapt paths considering both physical and social demands.
- To promote fairness in human-robot interactions for more equitable societal outcomes.
Main Methods:
- Developed a two-component framework: 'learning' to integrate social context and 'relearning' to detect and correct harmful outcomes.
- Incorporated social context into the robot learning process for safety and comfort.
- Analyzed technological and societal implications through three diverse case studies.
Main Results:
- The proposed framework aims to diminish bias in social robot navigation.
- Robots can be equipped to plan and adapt paths based on physical and social demands.
- Identified ethical implications and proposed solutions for deploying robots in social environments.
Conclusions:
- Fairness in human-robot interaction is crucial for equitable social relationships and dynamics.
- Advocating for fairness promotes positive societal influence through robot integration.
- The study highlights the need for bias mitigation in socially compliant robot navigation.
Keywords:
algorithmic fairnessethicsfairness-aware learningresponsible innovationrobot learningsocial robot navigationMore Related Videos
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