mlsanityData to 7 Oct 2026

Sample Efficient Social Navigation Using Inverse Reinforcement Learning

arXiv 2106.10318 ↗First posted 18 Jun 2021cs.RO cs.AI cs.LGcited by 1

On the map: Offline RL and imitation learning from data → Social robot navigation

Abstract (arXiv)

In this paper, we present an algorithm to efficiently learn socially-compliant navigation policies from observations of human trajectories. As mobile robots come to inhabit and traffic social spaces, they must account for social cues and behave in a socially compliant manner. We focus on learning such cues from examples. We describe an inverse reinforcement learning based algorithm which learns from human trajectory observations without knowing their specific actions. We increase the sample-efficiency of our approach over alternative methods by leveraging the notion of a replay buffer (found in many off-policy reinforcement learning methods) to eliminate the additional sample complexity associated with inverse reinforcement learning. We evaluate our method by training agents using publicly available pedestrian motion data sets and compare it to related methods. We show that our approach yields better performance while also decreasing training time and sample complexity.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Social robot navigation, one of 13 directions in Offline RL and imitation learning from data. Builds on 11 of its 48 references that are in the map. First posted on arXiv 18 Jun 2021.

Builds on

11 of 48 references.

References · 11

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  2. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,179
  3. 2017Learning Robust Rewards with Adversarial Inverse Reinforcement Learningcited by 485
  4. 2016Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimizationcited by 400
  5. 2018Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learningcited by 203
  6. 2015Maximum Entropy Deep Inverse Reinforcement Learningcited by 173
  7. 2016A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Modelscited by 167
  8. 2017Socially Aware Motion Planning with Deep Reinforcement Learningcited by 128
  9. 2016Decentralized Non-communicating Multiagent Collision Avoidance with Deep Reinforcement Learningcited by 104
  10. 2018Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learningcited by 101
  11. 2019Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreementcited by 46

Built on by

Cited by year

Citing papers per year: 2024: 1.

1 paper.

  1. 2024Towards Sample-Efficiency and Generalization of Transfer and Inverse Reinforcement Learning: A Comprehensive Literature Reviewcited by 3

Similar, not cited

Close in wording, no citation either way

  1. 2025Learning Implicit Social Navigation Behavior using Deep Inverse Reinforcement Learning
  2. 2022Socially Compliant Navigation Dataset (SCAND): A Large-Scale Dataset of Demonstrations for Social Navigation
  3. 2026Trajectory Learning with Graph Representations for Social Robot Navigation
  4. 2024Multi-Agent Inverse Reinforcement Learning in Real World Unstructured Pedestrian Crowds
  5. 2024Learning Early Social Maneuvers for Enhanced Social Navigation ↗
  6. 2025COLSON: Controllable Learning-Based Social Navigation via Diffusion-Based Reinforcement Learning
  7. 2022SoLo T-DIRL: Socially-Aware Dynamic Local Planner based on Trajectory-Ranked Deep Inverse Reinforcement Learning
  8. 2022Feedback-efficient Active Preference Learning for Socially Aware Robot Navigation