# SoLo T-DIRL: Socially-Aware Dynamic Local Planner based on Trajectory-Ranked Deep Inverse Reinforcement Learning

arXiv: https://arxiv.org/abs/2209.07996 · HTML: https://mlsanity.com/paper/2209.07996/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Social robot navigation, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 16 of its 48 references that are in the map.
- First posted on arXiv 16 Sep 2022.

## Placed in

- Offline RL and imitation learning from data / [Social robot navigation](https://mlsanity.com/offlinerl/social-robot-navigation/)

## Builds on (most cited first, 16 of 16)

- [Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks](https://mlsanity.com/paper/1803.10892/) (2018)
- [YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications](https://mlsanity.com/paper/2209.02976/) (2022)
- [Human Motion Trajectory Prediction: A Survey](https://mlsanity.com/paper/1905.06113/) (2019)
- [Hydra: A Real-time Spatial Perception System for 3D Scene Graph Construction and Optimization](https://mlsanity.com/paper/2201.13360/) (2022)
- [Maximum Entropy Deep Inverse Reinforcement Learning](https://mlsanity.com/paper/1507.04888/) (2015)
- [Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning](https://mlsanity.com/paper/1809.08835/) (2018)
- [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)
- [Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning](https://mlsanity.com/paper/1805.01956/) (2018)
- [MotionSC: Data Set and Network for Real-Time Semantic Mapping in Dynamic Environments](https://mlsanity.com/paper/2203.07060/) (2022)
- [Toward Safety-Aware Informative Motion Planning for Legged Robots](https://mlsanity.com/paper/2103.14252/) (2021)
- [Following Social Groups: Socially Compliant Autonomous Navigation in Dense Crowds](https://mlsanity.com/paper/1911.12063/) (2019)
- [An Error-State Model Predictive Control on Connected Matrix Lie Groups for Legged Robot Control](https://mlsanity.com/paper/2203.08728/) (2022)
- [Energy-based Legged Robots Terrain Traversability Modeling via Deep Inverse Reinforcement Learning](https://mlsanity.com/paper/2207.03034/) (2022)
- [Simultaneous Human-robot Matching and Routing for Multi-robot Tour Guiding under Time Uncertainty](https://mlsanity.com/paper/2201.10635/) (2022)
- [Providers-Clients-Robots: Framework for spatial-semantic planning for shared understanding in human-robot interaction](https://mlsanity.com/paper/2206.10767/) (2022)

## Built on by (most cited first, 2 of 2)

- [Learning Implicit Social Navigation Behavior using Deep Inverse Reinforcement Learning](https://mlsanity.com/paper/2501.06946/) (2025)
- [Trajectory Prediction in Dynamic Object Tracking: A Critical Study](https://mlsanity.com/paper/2506.19341/) (2025)

## Abstract (arXiv)

This work proposes a new framework for a socially-aware dynamic local planner in crowded environments by building on the recently proposed Trajectory-ranked Maximum Entropy Deep Inverse Reinforcement Learning (T-MEDIRL). To address the social navigation problem, our multi-modal learning planner explicitly considers social interaction factors, as well as social-awareness factors into T-MEDIRL pipeline to learn a reward function from human demonstrations. Moreover, we propose a novel trajectory ranking score using the sudden velocity change of pedestrians around the robot to address the sub-optimality in human demonstrations. Our evaluation shows that this method can successfully make a robot navigate in a crowded social environment and outperforms the state-of-art social navigation methods in terms of the success rate, navigation time, and invasion rate.