Haptic feedback can significantly enhance the realism and immersiveness of virtual reality (VR) systems. In this paper, we propose MoveVR, a technique that enables realistic, multiform force feedback in VR leveraging commonplace cleaning robots. MoveVR can generate tension, resistance, impact and material rigidity force feedback with multiple levels of force intensity and directions. This is achieved by changing the robot's moving speed, rotation, position as well as the carried proxies. We demonstrated the feasibility and effectiveness of MoveVR through interactive VR gaming. In our quantitative and qualitative evaluation studies, participants found that MoveVR provides more realistic and enjoyable user experience when compared to commercially available haptic solutions such as vibrotactile haptic systems.

We describe techniques that allow inexpensive, ultra-thin, battery-free Radio Frequency Identification (RFID) tags to be turned into simple paper input devices. We use sensing and signal processing techniques that determine how a tag is being manipulated by the user via an RFID reader and show how tags may be enhanced with a simple set of conductive traces that can be printed on paper, stencil-traced, or even hand-drawn. These traces modify the behavior of contiguous tags to serve as input devices. Our techniques provide the capability to use off-the-shelf RFID tags to sense touch, cover, overlap of tags by conductive or dielectric (insulating) materials, and tag movement trajectories. Paper prototypes can be made functional in seconds. Due to the rapid deployability and low cost of the tags used, we can create a new class of interactive paper devices that are drawn on demand for simple tasks. These capabilities allow new interactive possibilities for pop-up books and other papercraft objects.

Technologies that allow autonomous robots and computer systems to quickly recognize and interact with individuals in a group setting has the potential to enable a wide range of personalized experiences. However, existing solutions fail to both identify and locate individuals with enough speed to enable seamless interactions in very dynamic environments that require fast, implicit, non-intrusive, and ubiquitous recognition of users. In this work, we present a hybrid computer vision and RFID system that uses a novel reverse synthetic aperture technique to recover the relative motion paths of an RFID tags worn by people and correlate that to physical motion paths of individuals as measured with a 3D depth camera. Results show that our real-time system is capable of simultaneously recognizing and correctly assigning IDs to individuals within 4 seconds with 96.6% accuracy and groups of five people in 7 seconds with 95% accuracy. In order to test the effectiveness of this approach in realistic scenarios, groups of five participants play an interactive quiz game with an autonomous robot, resulting in an ID assignment accuracy of 93.3%.

In order to enable unobtrusive human object interaction detection, we propose a minimalistic approach to instrumenting everyday objects with passive (i.e. battery-free) UHF RFID tags. By measuring the changes in the physical layer of the communication channel between the RFID tag and reader (such as RSSI, RF phase, and read rate) we are able to classify, in real time, tag/object motion events along with two types of touch events. Through a user study, we demonstrate that our real-time classification engine is able to simultaneously track 20 objects and identify four movement classes with 93% accuracy. To demonstrate how robust this general-purpose interaction mechanism is, we investigate three usage scenarios 1) interactive storytelling with toys 2) inference of daily activities in the home 3) identification of customer browsing habits in a retail setting.