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Learning to Plan in Human-Robot Collaboration: Multimodal Reinforcement Learning for Adaptive Interaction

Summary

A new arXiv paper proposes a reinforcement learning approach to automatically generate multimodal interaction policies for robot assistants, using a simulator trained on human data and a simple high-level reward. A real-world human study found high usability and effective task completion, suggesting a scalable and interpretable alternative to hand-crafted interaction managers.

SourcearXiv RoboticsAuthor: Afagh Mehri Shervedani, Siyu Li, Natawut Monaikul, Bahareh Abbasi, Barbara Di Eugenio, Milo\v{s} \v{Z}efran
Learning to Plan in Human-Robot Collaboration: Multimodal Reinforcement Learning for Adaptive Interaction
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[Submitted on 21 Sep 2026]

Title:Learning to Plan in Human-Robot Collaboration: Multimodal Reinforcement Learning for Adaptive Interaction

View a PDF of the paper titled Learning to Plan in Human-Robot Collaboration: Multimodal Reinforcement Learning for Adaptive Interaction, by Afagh Mehri Shervedani and 5 other authors

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Abstract:Robot assistants for older adults and people with disabilities need to perform collaborative tasks with users effectively. The core component of these systems is an interaction manager whose job is to observe and assess the task and infer the state of the human and their intent for the robot to choose the best course of action. Due to the sparseness of the data in this domain, the policy for such multimodal systems is often crafted by hand; as the complexity of interactions grows, this process is not scalable. This paper proposes a reinforcement learning (RL) approach to automatically generate the multimodal policy of the robot. Our system focuses on a realistic scenario where a robot assists a user in locating objects within a home environment, managing multimodal signals, including language and physical actions, to select the best action. In contrast to traditional dialog systems, our agent is trained with a simulator that uses human data and can deal with multiple modalities. We use a simple high-level reward function that needs no fine-tuning and enforce some preconditions to speed up the training process. A human study evaluating the system in a real-world setting demonstrates promising results, indicating high usability and effective task completion. This RL-based approach offers a scalable and interpretable alternative for designing interaction managers in multimodal human-robot collaborations.

Subjects:

Robotics (cs.RO)

Cite as: arXiv:2609.25274 [cs.RO]

(or arXiv:2609.25274v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.25274

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Afagh Mehri Shervedani [view email] [v1] Mon, 21 Sep 2026 18:18:40 UTC (8,819 KB)

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Key points and analysis

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Key points

  • Proposes reinforcement learning to automatically generate multimodal interaction policies for human-robot collaboration
  • Robot assists users in locating objects at home by managing language and physical action signals
  • Uses a simple high-level reward function without fine-tuning and applies preconditions to speed up training
  • A real-world human study shows high usability and effective task completion

Highlights and analysis are generated automatically and may contain errors. Check the original source.

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