# Demonstration-Enhanced Adaptable Multi-Objective Robot Navigation

arXiv: https://arxiv.org/abs/2404.04857 · HTML: https://mlsanity.com/paper/2404.04857/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods.
- Builds on 15 of its 30 references that are in the map.
- First posted on arXiv 7 Apr 2024.

## Placed in

- Deep reinforcement learning methods / [RL for games and combinatorial optimization](https://mlsanity.com/deeprl/rl-for-games-and-combinatorial-optimization/)

## Builds on (most cited first, 15 of 15)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Hindsight Experience Replay](https://mlsanity.com/paper/1707.01495/) (2017)
- [A Practical Guide to Multi-Objective Reinforcement Learning and Planning](https://mlsanity.com/paper/2103.09568/) (2021)
- [iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Tasks](https://mlsanity.com/paper/2108.03272/) (2021)
- [A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation](https://mlsanity.com/paper/1908.08342/) (2019)
- [Core Challenges of Social Robot Navigation: A Survey](https://mlsanity.com/paper/2103.05668/) (2021)
- [PD-MORL: Preference-Driven Multi-Objective Reinforcement Learning Algorithm](https://mlsanity.com/paper/2208.07914/) (2022)
- [Robot Navigation in Constrained Pedestrian Environments using Reinforcement Learning](https://mlsanity.com/paper/2010.08600/) (2020)
- [Promptable Behaviors: Personalizing Multi-Objective Rewards from Human Preferences](https://mlsanity.com/paper/2312.09337/) (2023)
- [Adaptive and Explainable Deployment of Navigation Skills via Hierarchical Deep Reinforcement Learning](https://mlsanity.com/paper/2305.19746/) (2023)
- [Learning Depth Vision-Based Personalized Robot Navigation From Dynamic Demonstrations in Virtual Reality](https://mlsanity.com/paper/2210.01683/) (2022)
- [Optimizing Algorithms From Pairwise User Preferences](https://mlsanity.com/paper/2308.04571/) (2023)
- [Scalarizing Multi-Objective Robot Planning Problems using Weighted Maximization](https://mlsanity.com/paper/2312.07227/) (2023)
- [Learning Personalized Human-Aware Robot Navigation Using Virtual Reality Demonstrations from a User Study](https://mlsanity.com/paper/2203.14741/) (2022)
- [Variational Meta Reinforcement Learning for Social Robotics](https://mlsanity.com/paper/2206.03211/) (2022)

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

- [Multi-Objective Reinforcement Learning for Adaptable Personalized Autonomous Driving](https://mlsanity.com/paper/2505.05223/) (2025)
- [SoGuDiff: Socially Guided Diffusion for Steerable, Norm-Grounded Robot Navigation](https://mlsanity.com/paper/2609.30560/) (2026)

## Abstract (arXiv)

Preference-aligned robot navigation in human environments is typically achieved through learning-based approaches, utilizing user feedback or demonstrations for personalization. However, personal preferences are subject to change and might even be context-dependent. Yet traditional reinforcement learning (RL) approaches with static reward functions often fall short in adapting to evolving user preferences, inevitably reflecting demonstrations once training is completed. This paper introduces a structured framework that combines demonstration-based learning with multi-objective reinforcement learning (MORL). To ensure real-world applicability, our approach allows for dynamic adaptation of the robot navigation policy to changing user preferences without retraining. It fluently modulates the amount of demonstration data reflection and other preference-related objectives. Through rigorous evaluations, including a baseline comparison and sim-to-real transfer on two robots, we demonstrate our framework's capability to adapt to user preferences accurately while achieving high navigational performance in terms of collision avoidance and goal pursuance.