Engaging with Robotic Swarms

Abstract

In recent years, researchers have explored human body posture and motion to control robots in more natural ways. These interfaces require the ability to track the body movements of the user in three dimensions. Deploying motion capture systems for tracking tends to be costly and intrusive and requires a clear line of sight, making them ill adapted for applications that need fast deployment. In this article, we use consumer-grade armbands, capturing orientation information and muscle activity, to interact with a robotic system through a state machine controlled by a body motion classifier. To compensate for the low quality of the information of these sensors, and to allow a wider range of dynamic control, our approach relies on machine learning. We train our classifier directly on the user to recognize (within minutes) which physiological state his or her body motion expresses. We demonstrate that on top of guaranteeing faster field deployment, our algorithm performs better than all comparable algorithms, and we detail its configuration and the most significant features extracted. As the use of large groups of robots is growing, we postulate that their interaction with humans can be eased by our approach. We identified the key factors to stimulate engagement using our system on 27 participants, each creating his or her own set of expressive motions to control a swarm of desk robots. The resulting unique dataset is available online together with the classifier and the robot control scripts.

A Low-Cost, Wireless, 3-D-Printed Custom Armband for sEMG Hand Gesture Recognition

“…Information from an IMU system is widely employed in the domain of rehabilitation [27]. Additionally, for dynamic sEMG-based gesture recognition, orientation information from IMU devices can be leveraged to obtain higher performances than with EMG alone [20,28,29]. Furthermore, IMUs have been employed to increase the number of gestures that can be detected by combining the orientation of the forearm with static hand gestures [30,31].…”

Molecular HCI

“…The most interactive systems come from the HCI field, implementing various modalities for both input (e.g., touch [57,91], mid-air gesture [10,101], direct shape deformation [73,106], wearable glove-like controller [114]) and output (e.g., LEDs [10,57,72], GUI displays [28], vibrotactile feedback [114], haptic patterns [52]). However, even though systems coming from the robotics field rather focus on locomotion, and that reconfiguration is not designed to interact with users, prior robotics work embed sensors to interact with their environment (e.g., temperature sensor [29], ambient light sensor [93]).…”