Generating Natural and Expressive Robot Gestures through Iterative Reinforcement Learning with Human Feedback using LLMs
arXiv:2606.18747v1 Announce Type: cross Abstract: Expressive gestures are essential for natural and effective communication, complementing speech when verbal cues alone are insufficient (e.g., pointing). For social robots such as the humanoid Pepper, producing natural and expressive movements is critical for improving human-robot interaction (HRI) and long-term acceptance. However, generating gestures remains challenging due to reliance on expert-authored animations, resulting in rigid behaviors that are impractical for dynamic and diverse environments. Alternatively, machine learning approach
The combination of advanced AI (LLMs) and the increasing focus on practical humanoid robotics makes the generation of more natural robot communication a timely and critical development.
Improving robot gestural communication through AI is essential for enhancing human-robot interaction, pushing robots closer to general acceptance and utility in diverse social and work environments.
Robot gestures will move beyond pre-programmed, rigid movements to become more dynamic, expressive, and contextually appropriate, significantly altering how humans perceive and interact with them.
- · Humanoid robotics manufacturers
- · AI developers specializing in HRI
- · Service industries deploying social robots
- · Users interacting with social robots
- · Developers reliant solely on expert-authored robot animations
- · Companies producing robots with limited expressive capabilities
More fluid and natural human-robot interactions become possible, accelerating robot integration into daily life.
Public perception of robots shifts towards viewing them as more capable and less threatening companions or colleagues.
The definition of 'social presence' for AI and robotics expands, leading to new ethical and regulatory considerations for robot-human relationships.
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Read at arXiv cs.AI