# From Learning to Relearning: A Framework for Diminishing Bias in Social Robot Navigation

arXiv: https://arxiv.org/abs/2101.02647 · HTML: https://mlsanity.com/paper/2101.02647/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 117 references that are in the map.
- First posted on arXiv 7 Jan 2021.

## Builds on (most cited first, 28 of 28)

- [Shortcut Learning in Deep Neural Networks](https://mlsanity.com/paper/2004.07780/) (2020)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [A Reductions Approach to Fair Classification](https://mlsanity.com/paper/1803.02453/) (2018)
- [European Union regulations on algorithmic decision-making and a "right to explanation"](https://mlsanity.com/paper/1606.08813/) (2016)
- [Large image datasets: A pyrrhic win for computer vision?](https://mlsanity.com/paper/2006.16923/) (2020)
- [Gender Bias in Neural Natural Language Processing](https://mlsanity.com/paper/1807.11714/) (2018)
- [Fairness in Machine Learning: Lessons from Political Philosophy](https://mlsanity.com/paper/1712.03586/) (2017)
- [The Frontiers of Fairness in Machine Learning](https://mlsanity.com/paper/1810.08810/) (2018)
- [Socially Aware Motion Planning with Deep Reinforcement Learning](https://mlsanity.com/paper/1703.08862/) (2017)
- [The Ethics of AI Ethics -- An Evaluation of Guidelines](https://mlsanity.com/paper/1903.03425/) (2019)
- [Predictive Inequity in Object Detection](https://mlsanity.com/paper/1902.11097/) (2019)
- [A causal framework for discovering and removing direct and indirect discrimination](https://mlsanity.com/paper/1611.07509/) (2016)
- [MOPT: Multi-Object Panoptic Tracking](https://mlsanity.com/paper/2004.08189/) (2020)
- [Socially Compliant Navigation through Raw Depth Inputs with Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1710.02543/) (2017)
- [Racial categories in machine learning](https://mlsanity.com/paper/1811.11668/) (2018)
- [Clustering with Deep Learning: Taxonomy and New Methods](https://mlsanity.com/paper/1801.07648/) (2018)
- [Learning Generalized Reactive Policies using Deep Neural Networks](https://mlsanity.com/paper/1708.07280/) (2017)
- [Age and gender bias in pedestrian detection algorithms](https://mlsanity.com/paper/1906.10490/) (2019)
- [Deep Inverse Q-learning with Constraints](https://mlsanity.com/paper/2008.01712/) (2020)
- [Visual Analysis of Discrimination in Machine Learning](https://mlsanity.com/paper/2007.15182/) (2020)
- [Vision-Based Autonomous UAV Navigation and Landing for Urban Search and Rescue](https://mlsanity.com/paper/1906.01304/) (2019)
- [Algorithmic Injustices: Towards a Relational Ethics](https://mlsanity.com/paper/1912.07376/) (2019)
- [Machine Learning Meets Quantitative Planning: Enabling Self-Adaptation in Autonomous Robots](https://mlsanity.com/paper/1903.03920/) (2019)
- [DeepMoTIon: Learning to Navigate Like Humans](https://mlsanity.com/paper/1803.03719/) (2018)
- [Viewing Robot Navigation in Human Environment as a Cooperative Activity](https://mlsanity.com/paper/1708.01267/) (2017)
- [Multi-Armed Bandits with Fairness Constraints for Distributing Resources to Human Teammates](https://mlsanity.com/paper/1907.00313/) (2019)
- [Deep Constrained Q-learning](https://mlsanity.com/paper/2003.09398/) (2020)
- [A comparative evaluation of machine learning methods for robot navigation through human crowds](https://mlsanity.com/paper/2012.08822/) (2020)

## Built on by (most cited first, 11 of 11)

- [Catch Me If You Hear Me: Audio-Visual Navigation in Complex Unmapped Environments with Moving Sounds](https://mlsanity.com/paper/2111.14843/) (2021)
- [Amodal Panoptic Segmentation](https://mlsanity.com/paper/2202.11542/) (2022)
- [SkyEye: Self-Supervised Bird's-Eye-View Semantic Mapping Using Monocular Frontal View Images](https://mlsanity.com/paper/2302.04233/) (2023)
- [LLM-Driven Robots Risk Enacting Discrimination, Violence, and Unlawful Actions](https://mlsanity.com/paper/2406.08824/) (2024)
- [N2M2: Learning Navigation for Arbitrary Mobile Manipulation Motions in Unseen and Dynamic Environments](https://mlsanity.com/paper/2206.08737/) (2022)
- [Fairness and Bias in Robot Learning](https://mlsanity.com/paper/2207.03444/) (2022)
- [Doing Right by Not Doing Wrong in Human-Robot Collaboration](https://mlsanity.com/paper/2202.02654/) (2022)
- [Designing for Difference: How Human Characteristics Shape Perceptions of Collaborative Robots](https://mlsanity.com/paper/2507.16480/) (2025)
- [MIRACLE: Inverse Reinforcement and Curriculum Learning Model for Human-inspired Mobile Robot Navigation](https://mlsanity.com/paper/2312.03651/) (2023)
- [Auditing LLM-Governed Social Robots with Culture-Specific Moral Gradients](https://mlsanity.com/paper/2606.28345/) (2026)
- [Human-Centered Reflections on Care Robots: A Comparative Study of Caregiver Perspectives](https://mlsanity.com/paper/2608.02411/) (2026)

## Abstract (arXiv)

The exponentially increasing advances in robotics and machine learning are facilitating the transition of robots from being confined to controlled industrial spaces to performing novel everyday tasks in domestic and urban environments. In order to make the presence of robots safe as well as comfortable for humans, and to facilitate their acceptance in public environments, they are often equipped with social abilities for navigation and interaction. Socially compliant robot navigation is increasingly being learned from human observations or demonstrations. We argue that these techniques that typically aim to mimic human behavior do not guarantee fair behavior. As a consequence, social navigation models can replicate, promote, and amplify societal unfairness such as discrimination and segregation. In this work, we investigate a framework for diminishing bias in social robot navigation models so that robots are equipped with the capability to plan as well as adapt their paths based on both physical and social demands. Our proposed framework consists of two components: textitlearning which incorporates social context into the learning process to account for safety and comfort, and textitrelearning to detect and correct potentially harmful outcomes before the onset. We provide both technological and societal analysis using three diverse case studies in different social scenarios of interaction. Moreover, we present ethical implications of deploying robots in social environments and propose potential solutions. Through this study, we highlight the importance and advocate for fairness in human-robot interactions in order to promote more equitable social relationships, roles, and dynamics and consequently positively influence our society.