mlsanityData to 7 Oct 2026

Multi-Agent Strategy Explanations for Human-Robot Collaboration

arXiv 2311.11955 ↗First posted 20 Nov 2023cs.ROcited by 2

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

As robots are deployed in human spaces, it is important that they are able to coordinate their actions with the people around them. Part of such coordination involves ensuring that people have a good understanding of how a robot will act in the environment. This can be achieved through explanations of the robot's policy. Much prior work in explainable AI and RL focuses on generating explanations for single-agent policies, but little has been explored in generating explanations for collaborative policies. In this work, we investigate how to generate multi-agent strategy explanations for human-robot collaboration. We formulate the problem using a generic multi-agent planner, show how to generate visual explanations through strategy-conditioned landmark states and generate textual explanations by giving the landmarks to an LLM. Through a user study, we find that when presented with explanations from our proposed framework, users are able to better explore the full space of strategies and collaborate more efficiently with new robot partners.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 15 of its 42 references that are in the map. First posted on arXiv 20 Nov 2023.

Builds on

15 of 42 references.

References · 15

  1. 2018VirtualHome: Simulating Household Activities via Programscited by 411
  2. 2023Habitat 3.0: A Co-Habitat for Humans, Avatars and Robotscited by 220
  3. 2020Learning Latent Representations to Influence Multi-Agent Interactioncited by 92
  4. 2017Coordinated Multi-Agent Imitation Learningcited by 70
  5. 2018An Overview of Machine Teachingcited by 63
  6. 2018Establishing Appropriate Trust via Critical Statescited by 47
  7. 2019Efficient Iterative Linear-Quadratic Approximations for Nonlinear Multi-Player General-Sum Differential Gamescited by 37
  8. 2017Enabling Robots to Communicate their Objectivescited by 34
  9. 2018Probabilistically Safe Robot Planning with Confidence-Based Human Predictionscited by 27
  10. 2021Influencing Towards Stable Multi-Agent Interactionscited by 24
  11. 2021Co-GAIL: Learning Diverse Strategies for Human-Robot Collaborationcited by 16
  12. 2020Inference-Based Strategy Alignment for General-Sum Differential Gamescited by 10
3 more
  1. 2019ALGAMES: A Fast Solver for Constrained Dynamic Gamescited by 9
  2. 2020iLQGames.jl: Rapidly Designing and Solving Differential Games in Juliacited by 1
  3. 2022Generating Explanations from Deep Reinforcement Learning Using Episodic Memorycited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Adaptive Collaboration with Humans: Metacognitive Policy Optimization for Multi-Agent LLMs with Continual Learningcited by 9
  2. 2026Robots Influencing Humans to Reveal their Goals during Collaboration and Competitioncited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2023Understanding Your Agent: Leveraging Large Language Models for Behavior Explanation
  2. 2020Order Matters: Generating Progressive Explanations for Planning Tasks in Human-Robot Teaming
  3. 2026Exploring Plan Space through Conversation: An Agentic Framework for LLM-Mediated Explanations in Planning
  4. 2023Explaining Agent Behavior with Large Language Models
  5. 2023A Closer Look at Reward Decomposition for High-Level Robotic Explanations
  6. 2022Coordination with Humans via Strategy Matching
  7. 2023Diffusion Co-Policy for Synergistic Human-Robot Collaborative Tasks
  8. 2026Distilling LLM Reasoning into an Interpretable Policy Tree for Human-AI Collaboration