arXiv:2507.19684v2 Announce Type: replace-cross Abstract: Socially interactive humanoid robots must engage with humans through their bodies, adapting in real time to a partner's movement, intent, and abilities. This requires models that understand not just how bodies move, but what movement means in a shared social context. Yet evaluation frameworks for interactive motion generation do not measure whether generated follower motion is legible within a shared movement vocabulary, nor whether it is appropriate to the partner's proficiency level. This gap has two causes: existing frameworks rely o
The continuous advancements in AI and robotics, coupled with the increasing demand for more sophisticated human-robot interaction, drive the need for better datasets and benchmarks to accelerate development.
This dataset addresses a critical gap in evaluating interactive motion generation for humanoid robots, which is essential for their social integration and effective functionality in human-centric environments.
The explicit focus on shared movement vocabulary and proficiency levels shifts evaluation beyond mere physical motion to understanding social context, enabling more human-like robot interactions.
- · Robotics research labs
- · Humanoid robot manufacturers
- · AI developers
- · Entertainment industries
- · Developers relying on generic motion datasets
- · Companies with low investment in social robotics
- · Traditional manufacturing processes
Improved human-robot interaction models will lead to more socially intelligent humanoid robots.
The development of these robots could accelerate their adoption in service industries, healthcare, and education.
Widely available and context-aware humanoid robots could fundamentally alter social dynamics and labor markets over the long term.
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