# Doing Right by Not Doing Wrong in Human-Robot Collaboration

arXiv: https://arxiv.org/abs/2202.02654 · HTML: https://mlsanity.com/paper/2202.02654/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 35 references that are in the map.
- First posted on arXiv 5 Feb 2022.

## Builds on (most cited first, 7 of 7)

- [Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms](https://mlsanity.com/paper/1911.10635/) (2019)
- [Fairness in Machine Learning: A Survey](https://mlsanity.com/paper/2010.04053/) (2020)
- [The age of secrecy and unfairness in recidivism prediction](https://mlsanity.com/paper/1811.00731/) (2018)
- [Learning Kinematic Feasibility for Mobile Manipulation through Deep Reinforcement Learning](https://mlsanity.com/paper/2101.05325/) (2021)
- [Correct Me if I am Wrong: Interactive Learning for Robotic Manipulation](https://mlsanity.com/paper/2110.03316/) (2021)
- [Vision-Based Autonomous UAV Navigation and Landing for Urban Search and Rescue](https://mlsanity.com/paper/1906.01304/) (2019)
- [From Learning to Relearning: A Framework for Diminishing Bias in Social Robot Navigation](https://mlsanity.com/paper/2101.02647/) (2021)

## Built on by (most cited first, 4 of 4)

- [CenterGrasp: Object-Aware Implicit Representation Learning for Simultaneous Shape Reconstruction and 6-DoF Grasp Estimation](https://mlsanity.com/paper/2312.08240/) (2023)
- [Fairness and Bias in Robot Learning](https://mlsanity.com/paper/2207.03444/) (2022)
- [Designing for Difference: How Human Characteristics Shape Perceptions of Collaborative Robots](https://mlsanity.com/paper/2507.16480/) (2025)
- [Human-Centered Reflections on Care Robots: A Comparative Study of Caregiver Perspectives](https://mlsanity.com/paper/2608.02411/) (2026)

## Abstract (arXiv)

As robotic systems become more and more capable of assisting humans in their everyday lives, we must consider the opportunities for these artificial agents to make their human collaborators feel unsafe or to treat them unfairly. Robots can exhibit antisocial behavior causing physical harm to people or reproduce unfair behavior replicating and even amplifying historical and societal biases which are detrimental to humans they interact with. In this paper, we discuss these issues considering sociable robotic manipulation and fair robotic decision making. We propose a novel approach to learning fair and sociable behavior, not by reproducing positive behavior, but rather by avoiding negative behavior. In this study, we highlight the importance of incorporating sociability in robot manipulation, as well as the need to consider fairness in human-robot interactions.