We explore how humanoid robots can be repurposed as haptic media, extending beyond their conventional role as social, assistive, collaborative agents. To illustrate this approach, we implemented HumanoidTurk, taking a first step toward a humanoid-based haptic system that translates in-game g-force signals into synchronized motion feedback in VR driving. A pilot study involving six participants compared two synthesis methods, leading us to adopt a filter-based approach for smoother and more realistic feedback. A subsequent study with sixteen participants evaluated four conditions: no-feedback, controller, humanoid+controller, and human+controller. Results showed that humanoid feedback enhanced immersion, realism, and enjoyment, while introducing moderate costs in terms of comfort and simulation sickness. Interviews further highlighted the robot’s consistency and predictability in contrast to the adaptability of human feedback. From these findings, we identify fidelity, adaptability, and versatility as emerging themes, positioning humanoids as a distinct haptic modality for immersive VR.

Due to advances in deep learning, gestures have become a more common tool for human-computer interaction. When implementing a large amount of training data, deep learning models show remarkable performance in gesture recognition. Since it is expensive and time consuming to collect gesture data from people, we are often confronted with a practicality issue when managing the quantity and quality of training data. It is a well-known fact that increasing training data variability can help to improve the generalization performance of machine learning models. Thus, we directly intervene in the collection of gesture data to increase human gesture variability by adding some words (called styling words) into the data collection instructions, e.g., giving the instruction "perform gesture #1 faster" as opposed to "perform gesture #1." Through an in-depth analysis of gesture features and video-based gesture recognition, we have confirmed the advantageous use of styling words in gesture training data collection.