# Learning Social Navigation from Demonstrations with Conditional Neural Processes

arXiv: https://arxiv.org/abs/2210.03582 · HTML: https://mlsanity.com/paper/2210.03582/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 74 references that are in the map.
- First posted on arXiv 7 Oct 2022.

## Builds on (most cited first, 21 of 21)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Exploration by Random Network Distillation](https://mlsanity.com/paper/1810.12894/) (2018)
- [Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks](https://mlsanity.com/paper/1803.10892/) (2018)
- [Wasserstein GAN](https://mlsanity.com/paper/1701.07875/) (2017)
- [Conditional Neural Processes](https://mlsanity.com/paper/1807.01613/) (2018)
- [NIPS 2016 Tutorial: Generative Adversarial Networks](https://mlsanity.com/paper/1701.00160/) (2016)
- [Social Attention: Modeling Attention in Human Crowds](https://mlsanity.com/paper/1710.04689/) (2017)
- [Maximum Entropy Deep Inverse Reinforcement Learning](https://mlsanity.com/paper/1507.04888/) (2015)
- [Convolutional Conditional Neural Processes](https://mlsanity.com/paper/1910.13556/) (2019)
- [Socially Aware Motion Planning with Deep Reinforcement Learning](https://mlsanity.com/paper/1703.08862/) (2017)
- [Human Trajectory Forecasting in Crowds: A Deep Learning Perspective](https://mlsanity.com/paper/2007.03639/) (2020)
- [Socially Compliant Navigation Dataset (SCAND): A Large-Scale Dataset of Demonstrations for Social Navigation](https://mlsanity.com/paper/2203.15041/) (2022)
- [Core Challenges of Social Robot Navigation: A Survey](https://mlsanity.com/paper/2103.05668/) (2021)
- [Socially Compliant Navigation through Raw Depth Inputs with Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1710.02543/) (2017)
- [SocNavBench: A Grounded Simulation Testing Framework for Evaluating Social Navigation](https://mlsanity.com/paper/2103.00047/) (2021)
- [SEAN: Social Environment for Autonomous Navigation](https://mlsanity.com/paper/2009.04300/) (2020)
- [Efficient and Trustworthy Social Navigation Via Explicit and Implicit Robot-Human Communication](https://mlsanity.com/paper/1810.11556/) (2018)
- [Learning Human-Aware Path Planning with Fully Convolutional Networks](https://mlsanity.com/paper/1803.00429/) (2018)
- [SocNav1: A Dataset to Benchmark and Learn Social Navigation Conventions](https://mlsanity.com/paper/1909.02993/) (2019)
- [SOCIALGYM: A Framework for Benchmarking Social Robot Navigation](https://mlsanity.com/paper/2109.11011/) (2021)
- [Mobile Robot Path Planning in Dynamic Environments: A Survey](https://mlsanity.com/paper/2006.14195/) (2020)

## Built on by (most cited first, 3 of 3)

- [DR-MPC: Deep Residual Model Predictive Control for Real-world Social Navigation](https://mlsanity.com/paper/2410.10646/) (2024)
- [ViLAM: Distilling Vision-Language Reasoning into Attention Maps for Social Robot Navigation](https://mlsanity.com/paper/2503.09820/) (2025)
- [Conditional Neural Expert Processes for Learning Movement Primitives from Demonstration](https://mlsanity.com/paper/2402.08424/) (2024)

## Abstract (arXiv)

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.