mlsanityData to 7 Oct 2026

Human-Inspired Multi-Agent Navigation using Knowledge Distillation

arXiv 2103.10000 ↗First posted 18 Mar 2021cs.RO cs.LGcited by 7

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Abstract (arXiv)

Despite significant advancements in the field of multi-agent navigation, agents still lack the sophistication and intelligence that humans exhibit in multi-agent settings. In this paper, we propose a framework for learning a human-like general collision avoidance policy for agent-agent interactions in fully decentralized, multi-agent environments. Our approach uses knowledge distillation with reinforcement learning to shape the reward function based on expert policies extracted from human trajectory demonstrations through behavior cloning. We show that agents trained with our approach can take human-like trajectories in collision avoidance and goal-directed steering tasks not provided by the demonstrations, outperforming the experts as well as learning-based agents trained without knowledge distillation.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 23 of its 50 references that are in the map. First posted on arXiv 18 Mar 2021.

Builds on

23 of 50 references.

References · 23

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  3. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 1,916
  4. 2016Generative Adversarial Imitation Learningcited by 1,576
  5. 2016End to End Learning for Self-Driving Carscited by 1,000
  6. 2018Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networkscited by 687
  7. 2017Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrationscited by 685
  8. 2015Policy Distillationcited by 414
  9. 2017Overcoming Exploration in Reinforcement Learning with Demonstrationscited by 322
  10. 2018Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learningcited by 162
  11. 2017Emergence of Locomotion Behaviours in Rich Environmentscited by 159
  12. 2017Socially Aware Motion Planning with Deep Reinforcement Learningcited by 128
11 more
  1. 2016Decentralized Non-communicating Multiagent Collision Avoidance with Deep Reinforcement Learningcited by 104
  2. 2018Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learningcited by 101
  3. 2017Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learningcited by 81
  4. 2019Distilling Policy Distillationcited by 81
  5. 2020Social NCE: Contrastive Learning of Socially-aware Motion Representationscited by 58
  6. 2017Socially Compliant Navigation through Raw Depth Inputs with Generative Adversarial Imitation Learningcited by 50
  7. 2016From Perception to Decision: A Data-driven Approach to End-to-end Motion Planning for Autonomous Ground Robotscited by 37
  8. 2020DenseCAvoid: Real-time Navigation in Dense Crowds using Anticipatory Behaviorscited by 25
  9. 2016Deep-Learned Collision Avoidance Policy for Distributed Multi-Agent Navigationcited by 14
  10. 2018Fully Distributed Multi-Robot Collision Avoidance via Deep Reinforcement Learning for Safe and Efficient Navigation in Complex Scenarioscited by 4
  11. 2019NH-TTC: A gradient-based framework for generalized anticipatory collision avoidancecited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 2; 2024: 0; 2025: 1; 2026: 2.

7 papers.

  1. 2022A Survey on Reinforcement Learning Methods in Character Animationcited by 12
  2. 2025ViLAM: Distilling Vision-Language Reasoning into Attention Maps for Social Robot Navigationcited by 5
  3. 2023A Study on Learning Social Robot Navigation with Multimodal Perceptioncited by 4
  4. 2022Understanding reinforcement learned crowdscited by 2
  5. 2023Reward Function Design for Crowd Simulation via Reinforcement Learningcited by 0
  6. 2026Interactive Distillation for Cooperative Multi-Agent Reinforcement Learningcited by 0
  7. 2026Learning-Based Motion Planning for Dynamic Environments: From Foundational Algorithms to Emerging Paradigmscited by 0