# EmoBipedNav: Emotion-aware Social Navigation for Bipedal Robots with Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2503.12538 · HTML: https://mlsanity.com/paper/2503.12538/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 23 of its 55 references that are in the map.
- First posted on arXiv 16 Mar 2025.

## Placed in

- 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, 23 of 23)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning](https://mlsanity.com/paper/1809.08835/) (2018)
- [Decentralized Non-communicating Multiagent Collision Avoidance with Deep Reinforcement Learning](https://mlsanity.com/paper/1609.07845/) (2016)
- [Humanoid Locomotion and Manipulation: Current Progress and Challenges in Control, Planning, and Learning](https://mlsanity.com/paper/2501.02116/) (2025)
- [Relational Graph Learning for Crowd Navigation](https://mlsanity.com/paper/1909.13165/) (2019)
- [A Survey on Socially Aware Robot Navigation: Taxonomy and Future Challenges](https://mlsanity.com/paper/2311.06922/) (2023)
- [DRL-VO: Learning to Navigate Through Crowded Dynamic Scenes Using Velocity Obstacles](https://mlsanity.com/paper/2301.06512/) (2023)
- [ProxEmo: Gait-based Emotion Learning and Multi-view Proxemic Fusion for Socially-Aware Robot Navigation](https://mlsanity.com/paper/2003.01062/) (2020)
- [Mapless Navigation among Dynamics with Social-safety-awareness: a reinforcement learning approach from 2D laser scans](https://mlsanity.com/paper/1911.03074/) (2019)
- [Integrated Task and Motion Planning for Safe Legged Navigation in Partially Observable Environments](https://mlsanity.com/paper/2110.12097/) (2021)
- [EWareNet: Emotion Aware Human Intent Prediction and Adaptive Spatial Profile Fusion for Social Robot Navigation](https://mlsanity.com/paper/2011.09438/) (2020)
- [Autonomous Navigation of Underactuated Bipedal Robots in Height-Constrained Environments](https://mlsanity.com/paper/2109.05714/) (2021)
- [Infer and Adapt: Bipedal Locomotion Reward Learning from Demonstrations via Inverse Reinforcement Learning](https://mlsanity.com/paper/2309.16074/) (2023)
- [Terrain-Adaptive, ALIP-Based Bipedal Locomotion Controller via Model Predictive Control and Virtual Constraints](https://mlsanity.com/paper/2109.14862/) (2021)
- [A Sequential MPC Approach to Reactive Planning for Bipedal Robots](https://mlsanity.com/paper/2205.00156/) (2022)
- [Reactive Task and Motion Planning for Robust Whole-Body Dynamic Locomotion in Constrained Environments](https://mlsanity.com/paper/1811.04333/) (2018)
- [Towards Safe Locomotion Navigation in Partially Observable Environments with Uneven Terrain](https://mlsanity.com/paper/2009.05168/) (2020)
- [A Transferability Metric Using Scene Similarity and Local Map Observation for DRL Navigation](https://mlsanity.com/paper/2306.04910/) (2023)
- [Robust Optimal Planning and Control of Non-Periodic Bipedal Locomotion with A Centroidal Momentum Model](https://mlsanity.com/paper/1708.06345/) (2017)
- [Overtaking Moving Obstacles with Digit: Path Following for Bipedal Robots via Model Predictive Contouring Control](https://mlsanity.com/paper/2308.00119/) (2023)
- [Bipedal Safe Navigation over Uncertain Rough Terrain: Unifying Terrain Mapping and Locomotion Stability](https://mlsanity.com/paper/2403.16356/) (2024)
- [Real-time Model Predictive Control with Zonotope-Based Neural Networks for Bipedal Social Navigation](https://mlsanity.com/paper/2403.16485/) (2024)
- [Socially Acceptable Bipedal Robot Navigation via Social Zonotope Network Model Predictive Control](https://mlsanity.com/paper/2406.17151/) (2024)

## Built on by (most cited first, 1 of 1)

- [Hierarchical Reinforcement Learning for Collision-Free Locomotion of an Underactuated Biped](https://mlsanity.com/paper/2610.05855/) (2026)

## Abstract (arXiv)

This study presents an emotion-aware navigation framework -- EmoBipedNav -- using deep reinforcement learning (DRL) for bipedal robots walking in socially interactive environments. The inherent locomotion constraints of bipedal robots challenge their safe maneuvering capabilities in dynamic environments. When combined with the intricacies of social environments, including pedestrian interactions and social cues, such as emotions, these challenges become even more pronounced. To address these coupled problems, we propose a two-stage pipeline that considers both bipedal locomotion constraints and complex social environments. Specifically, social navigation scenarios are represented using sequential LiDAR grid maps (LGMs), from which we extract latent features, including collision regions, emotion-related discomfort zones, social interactions, and the spatio-temporal dynamics of evolving environments. The extracted features are directly mapped to the actions of reduced-order models (ROMs) through a DRL architecture. Furthermore, the proposed framework incorporates full-order dynamics and locomotion constraints during training, effectively accounting for tracking errors and restrictions of the locomotion controller while planning the trajectory with ROMs. Comprehensive experiments demonstrate that our approach exceeds both model-based planners and DRL-based baselines. The hardware videos and open-source code are available at https://gatech-lidar.github.io/emobipednav.github.io/.