# COLSON: Controllable Learning-Based Social Navigation via Diffusion-Based Reinforcement Learning

arXiv: https://arxiv.org/abs/2503.13934 · HTML: https://mlsanity.com/paper/2503.13934/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion and flow policies for robotics, one of 14 directions in Diffusion models.
- Builds on 36 of its 45 references that are in the map.
- First posted on arXiv 18 Mar 2025.

## Placed in

- Diffusion models / [Diffusion and flow policies for robotics](https://mlsanity.com/diffusion/diffusion-and-flow-policies-for-robotics/)
- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 30 of 36)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021)
- [Planning with Diffusion for Flexible Behavior Synthesis](https://mlsanity.com/paper/2205.09991/) (2022)
- [How Attentive are Graph Attention Networks?](https://mlsanity.com/paper/2105.14491/) (2021)
- [Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning](https://mlsanity.com/paper/1910.00177/) (2019)
- [AWAC: Accelerating Online Reinforcement Learning with Offline Datasets](https://mlsanity.com/paper/2006.09359/) (2020)
- [Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning](https://mlsanity.com/paper/2208.06193/) (2022)
- [Is Conditional Generative Modeling all you need for Decision-Making?](https://mlsanity.com/paper/2211.15657/) (2022)
- [IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion Policies](https://mlsanity.com/paper/2304.10573/) (2023)
- [NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration](https://mlsanity.com/paper/2310.07896/) (2023)
- [Reinforcement learning](https://mlsanity.com/paper/2405.10369/) (2024)
- [Goal-Conditioned Imitation Learning using Score-based Diffusion Policies](https://mlsanity.com/paper/2304.02532/) (2023)
- [Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning](https://mlsanity.com/paper/1809.08835/) (2018)
- [Efficient Diffusion Policies for Offline Reinforcement Learning](https://mlsanity.com/paper/2305.20081/) (2023)
- [Trace and Pace: Controllable Pedestrian Animation via Guided Trajectory Diffusion](https://mlsanity.com/paper/2304.01893/) (2023)
- [Learning a Diffusion Model Policy from Rewards via Q-Score Matching](https://mlsanity.com/paper/2312.11752/) (2023)
- [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)
- [Policy Representation via Diffusion Probability Model for Reinforcement Learning](https://mlsanity.com/paper/2305.13122/) (2023)
- [Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning](https://mlsanity.com/paper/1805.01956/) (2018)
- [Diffusion-based Reinforcement Learning via Q-weighted Variational Policy Optimization](https://mlsanity.com/paper/2405.16173/) (2024)
- [Regularizing Action Policies for Smooth Control with Reinforcement Learning](https://mlsanity.com/paper/2012.06644/) (2020)
- [LiDAR Data Synthesis with Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2309.09256/) (2023)
- [Intention Aware Robot Crowd Navigation with Attention-Based Interaction Graph](https://mlsanity.com/paper/2203.01821/) (2022)
- [Relational Graph Learning for Crowd Navigation](https://mlsanity.com/paper/1909.13165/) (2019)
- [Robot Navigation in Crowds by Graph Convolutional Networks with Attention Learned from Human Gaze](https://mlsanity.com/paper/1909.10400/) (2019)
- [DiPPeR: Diffusion-based 2D Path Planner applied on Legged Robots](https://mlsanity.com/paper/2310.07842/) (2023)
- [DR-SPAAM: A Spatial-Attention and Auto-regressive Model for Person Detection in 2D Range Data](https://mlsanity.com/paper/2004.14079/) (2020)

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

- [Socially aware navigation for mobile robots: a survey on deep reinforcement learning approaches](https://mlsanity.com/paper/2512.00049/) (2025)
- [Performance-Preserving Online Adaptation in Social Navigation via Diffusion Steering](https://mlsanity.com/paper/2609.24317/) (2026)
- [SoGuDiff: Socially Guided Diffusion for Steerable, Norm-Grounded Robot Navigation](https://mlsanity.com/paper/2609.30560/) (2026)

## Abstract (arXiv)

Mobile robot navigation in dynamic environments with pedestrian traffic is a key challenge in the development of autonomous mobile service robots. Recently, deep reinforcement learning-based methods have been actively studied and have outperformed traditional rule-based approaches owing to their optimization capabilities. Among these methods, those that assume continuous action spaces typically rely on Gaussian distributions, which limit the flexibility of the generated actions. In contrast, the application of diffusion models to reinforcement learning has advanced, enabling more flexible action distributions than Gaussian policy-based approaches. In this study, we apply a diffusion-based reinforcement learning approach to social navigation and validate its effectiveness. Furthermore, by exploiting the characteristics of diffusion models, we propose extensions that enable adaptation to previously unseen scenarios without additional training. As concrete scenario examples, we demonstrate adaptability to scenarios in which static obstacles exist in the environment that were not present during training, as well as scenarios in which the objective differs from training, such as accompanying target pedestrians while avoiding others to reach the destination.