# Intention-Aware Navigation in Crowds with Extended-Space POMDP Planning

arXiv: https://arxiv.org/abs/2206.10028 · HTML: https://mlsanity.com/paper/2206.10028/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 47 references that are in the map.
- First posted on arXiv 20 Jun 2022.

## Builds on (most cited first, 19 of 19)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction](https://mlsanity.com/paper/2002.11927/) (2020)
- [Socially Aware Motion Planning with Deep Reinforcement Learning](https://mlsanity.com/paper/1703.08862/) (2017)
- [Decentralized Non-communicating Multiagent Collision Avoidance with Deep Reinforcement Learning](https://mlsanity.com/paper/1609.07845/) (2016)
- [DESPOT: Online POMDP Planning with Regularization](https://mlsanity.com/paper/1609.03250/) (2016)
- [Online algorithms for POMDPs with continuous state, action, and observation spaces](https://mlsanity.com/paper/1709.06196/) (2017)
- [Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning](https://mlsanity.com/paper/1709.10082/) (2017)
- [PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-based Planning](https://mlsanity.com/paper/1710.03937/) (2017)
- [PORCA: Modeling and Planning for Autonomous Driving among Many Pedestrians](https://mlsanity.com/paper/1805.11833/) (2018)
- [DenseCAvoid: Real-time Navigation in Dense Crowds using Anticipatory Behaviors](https://mlsanity.com/paper/2002.03038/) (2020)
- [RL-RRT: Kinodynamic Motion Planning via Learning Reachability Estimators from RL Policies](https://mlsanity.com/paper/1907.04799/) (2019)
- [HyP-DESPOT: A Hybrid Parallel Algorithm for Online Planning under Uncertainty](https://mlsanity.com/paper/1802.06215/) (2018)
- [MAGIC: Learning Macro-Actions for Online POMDP Planning](https://mlsanity.com/paper/2011.03813/) (2020)
- [The Value of Inferring the Internal State of Traffic Participants for Autonomous Freeway Driving](https://mlsanity.com/paper/1702.00858/) (2017)
- [Belief State Planning for Autonomously Navigating Urban Intersections](https://mlsanity.com/paper/1704.04322/) (2017)
- [Realtime Collision Avoidance for Mobile Robots in Dense Crowds using Implicit Multi-sensor Fusion and Deep Reinforcement Learning](https://mlsanity.com/paper/2004.03089/) (2020)
- [Stochastic Motion Planning under Partial Observability for Mobile Robots with Continuous Range Measurements](https://mlsanity.com/paper/2011.11836/) (2020)
- [Voronoi Progressive Widening: Efficient Online Solvers for Continuous State, Action, and Observation POMDPs](https://mlsanity.com/paper/2012.10140/) (2020)
- [Social Navigation Planning Based on People's Awareness of Robots](https://mlsanity.com/paper/1809.08780/) (2018)

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

- [Learning Logic Specifications for Policy Guidance in POMDPs: an Inductive Logic Programming Approach](https://mlsanity.com/paper/2402.19265/) (2024)
- [A Study on Learning Social Robot Navigation with Multimodal Perception](https://mlsanity.com/paper/2309.12568/) (2023)
- [Learning Top-k Subtask Planning Tree based on Discriminative Representation Pre-training for Decision Making](https://mlsanity.com/paper/2312.11027/) (2023)
- [Robustness Analysis of POMDP Policies to Observation Perturbations](https://mlsanity.com/paper/2604.21256/) (2026)

## Abstract (arXiv)

This paper presents a hybrid online Partially Observable Markov Decision Process (POMDP) planning system that addresses the problem of autonomous navigation in the presence of multi-modal uncertainty introduced by other agents in the environment. As a particular example, we consider the problem of autonomous navigation in dense crowds of pedestrians and among obstacles. Popular approaches to this problem first generate a path using a complete planner (e.g., Hybrid A*) with ad-hoc assumptions about uncertainty, then use online tree-based POMDP solvers to reason about uncertainty with control over a limited aspect of the problem (i.e. speed along the path). We present a more capable and responsive real-time approach enabling the POMDP planner to control more degrees of freedom (e.g., both speed AND heading) to achieve more flexible and efficient solutions. This modification greatly extends the region of the state space that the POMDP planner must reason over, significantly increasing the importance of finding effective roll-out policies within the limited computational budget that real time control affords. Our key insight is to use multi-query motion planning techniques (e.g., Probabilistic Roadmaps or Fast Marching Method) as priors for rapidly generating efficient roll-out policies for every state that the POMDP planning tree might reach during its limited horizon search. Our proposed approach generates trajectories that are safe and significantly more efficient than the previous approach, even in densely crowded dynamic environments with long planning horizons.