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May 31, 2026Open Access

Real-World Human-Robot Interaction Behavior Generation using Latent Diffusion Models

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Authors

SSSergej Stanovcic

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Overview

Randomized trial demonstrates diverse motion generation in human-robot interaction, suggesting enhanced social responsiveness.

Key Points

  • The aim is to create a system for generating diverse and socially aligned motions in human-robot interactions using generative modeling techniques.
  • Integrates visual perception, context-aware motion generation, and hardware execution into a coherent system.
  • Develops a latent diffusion framework for generating joint social interactions based on past contexts and high-level descriptions.
  • Demonstrates real-time implementation with robots Tiago++ and Unitree G1 using a continuous streaming pipeline.
  • Successfully generates diverse and aligned motion segments in real time, reducing computational overhead.
  • Demonstrates effective integration into robotic platforms during both simulation and real-world testing.

Cite This Study

Sergej Stanovcic (2026) studied this question.

synapsesocial.com/papers/6a1bd2845783ba022b6fdf02https://doi.org/10.34726/hss.2026.123206
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