Supernumerary robotic limbs (SRLs) are robotic structures integrated closely with the user's body, which augment human physical capabilities and necessitate seamless, naturalistic human-machine interaction. For effective assistance in physical tasks, enabling SRLs to hand over objects to humans is crucial. Yet, designing heuristic-based policies for robots is time-consuming, difficult to generalize across tasks, and results in less human-like motion. When trained with proper datasets, generative models are powerful alternatives for creating naturalistic handover motions. We introduce 3HANDS, a novel dataset of object handover interactions between a participant performing a daily activity and another participant enacting a hip-mounted SRL in a naturalistic manner. 3HANDS captures the unique characteristics of SRL interactions: operating in intimate personal space with asymmetric object origins, implicit motion synchronization, and the user's engagement in a primary task during the handover. To demonstrate the effectiveness of our dataset, we present three models: one that generates naturalistic handover trajectories, another that determines the appropriate handover endpoints, and a third that predicts the moment to initiate a handover. In a user study (N=10), we compare the handover interaction performed with our method compared to a baseline. The findings show that our method was perceived as significantly more natural, less physically demanding, and more comfortable.

Rehearse 3/10Why are we here, why are we alive?One key reason is that our ancestors on the savannas of Africa were really good at one thing…They weren't bigger than the animals they took down a lot of the time, they weren't faster than the animals they took down a lot of the time, but they were much better at banding together into groups and cooperating.…One key reason… Proactive Gesture Cues Current Slide Hover to Preview and Modify Gestures Presenter Notes with Gesture highlights Edit DeleteWhy were humans at hunting ?• Not bigger or faster than animals • Good at cooperationFigure 1: GestureCoach guides speakers to perform gestures while rehearsing their talk.A gesture recommendation model predicts text segments in the presenter notes that should be emphasized with gestures and retrieves relevant semantic gestures for each segment.During rehearsal, the system highlights the segments and proactively cues a video clip of the gesture by tracking users' speech, allowing them to integrate the gesture smoothly into their talk.Hovering over a segment allows users to preview and modify the associated gesture with alternate suggestions from the model.

Wearable robotic arms (WRA) open up a unique interaction space that closely integrates the user’s body with an embodied robotic collaborator. This space affords diverse interaction styles, including body movement, hand gestures, or gaze. Yet, it is so-far unexplored which commands are desirable from a user perspective. Contributing findings from an elicitation study (N=14), we provide a comprehensive set of interactions for basic robot control, navigation, object manipulation, and emergency situations, performed when hands are free or occupied. Our study provides insights into preferred body parts, input modalities, and the users’ underlying sources of inspiration. Comparing interaction styles between WRAs and off-body robots, we highlight how WRAs enable a range of interactions specific for on-body robots and how users use WRAs both as tools and as collaborators. We conclude by providing guidance on the design of ad-hoc interaction with WRAs informed by user behavior.

Wearable robotic limbs (WRLs) augment human capabilities through robotic structures that attach to the user’s body. While WRLs are intensely researched and various device designs have been presented, it remains difficult for non-roboticists to engage with this exciting field. We aim to empower interaction designers and application domain experts to explore novel designs and applications by rapidly prototyping personalized WRLs that are customized for different tasks, different body locations, or different users. In this paper, we present WRLKit, an interactive computational design approach that enables designers to rapidly prototype a personalized WRL without requiring extensive robotics and ergonomics expertise. The body-aware optimization approach starts by capturing the user’s body dimensions and dynamic body poses. Then, an optimized fabricable structure of the WRL is generated for a desired mounting location and workspace of the WRL, to fit the user’s body and intended task. The results of a user study and several implemented prototypes demonstrate the practical feasibility and versatility of WRLKit.