This paper presents opt-in camera, a concept of privacy-preserving camera systems capable of recording only specific individuals in a crowd who explicitly consent to be recorded. Our system utilizes a mobile wireless communication tag attached to personal belongings as proof of opt-in and as a means of localizing tag carriers in video footage. Specifically, the on-ground positions of the wireless tag are first tracked over time using the unscented Kalman filter (UKF). The tag trajectory is then matched against visual tracking results for pedestrians found in videos to identify the tag carrier. Technically, we devise a dedicated trajectory matching technique based on constrained linear optimization, as well as a novel calibration technique that handles wireless tag-camera calibration and hyperparameter tuning for the UKF, which mitigates the non-lineof-sight (NLoS) issue in wireless localization. We implemented the proposed opt-in camera system using ultra-wideband (UWB) devices and an off-the-shelf webcam. Experimental results demonstrate that our system can perform opt-in recording of individuals in real-time at 10 fps, with reliable identification accuracy in crowds of 8–23 people in a confined space.

In this paper, we propose an occlusion-resilient Ultra-Wideband (UWB) localization system by rethinking anchor placement. Typically, anchors are installed near the ceiling for broader line-of-sight (LOS) coverage. However, in crowded environments, practical ceiling heights often fail to maintain stable LOS. By placing anchors near the floor and leveraging ceiling reflections, we create a virtual anchor that extends LOS coverage. Experiments show our approach outperforms ceiling-mounted solutions in accuracy and resilience, opening new possibilities for practical UWB localization.

Robust indoor positioning systems provide stable location-aware applications, enhancing our daily experiences. Fingerprint-based positioning techniques enable estimation of a user's position in complex indoor environments. While previous studies have used the received signal strength indicator or power spectral density as fingerprints, they typically achieved only submeter accuracy. This paper presents Geometric Sound Profile (GSP) as a novel location fingerprint to elevate the performance ceiling of fingerprint-based positioning. GSP is derived from the cross-correlation of transmitted and received signals based on transmission time, and a user's position is computed using weighted k-nearest neighbors. Our experiments demonstrate a median error of 0.66 m, marking a significant advancement over previous fingerprinting techniques.

With the pandemic of COVID-19, indoor crowd density monitoring has become one of the most critical responsibilities of public space managers. Beacon placement optimization has been tackled as fundamental research work as the performance of crowd density monitoring highly depends on how BLE beacons are allocated. In this research, we propose a novel beacon placement optimization approach to incrementally place the beacon on the updated detection status adaptively in favor of Bayesian optimization, which can help to provide the optimal beacon placement. Our proposed method can optimize the beacon placement effectively to improve the signal coverage quality in the given environment and minimize human workload.

Although picking up objects a few centimeters in size is a common task, achieving such ability in a robot manipulator remains challenging. We take a step toward solving this problem by focusing on the task of picking a 1.0-cm screw from a bulk bin using only tactile information to achieve the task. Inspired by how humans pick up small objects from a bin, we propose a "grasp-separate" strategy for robotic picking, which involves grasping many objects first and then separating a single object through manipulation in the fingers, for robotic picking. Based on this strategy, we developed a tactile-based screw bin-picking system. We trained a convolution neural network to estimate the number of screws in the fingers first and built a controller that generates manipulation behaviors to separate a screw using reinforcement learning. To compensate for the low resolution of off-the-shelf tactile sensor arrays, we adopted active sensing, which uses observations obtained during a predefined simple movement. We show that this approach enhances the estimation accuracy and manipulation performance. Furthermore, to enable flexible finger motion, such as between the thumb and the index finger in a human hand, we propose a soft robot finger structure that leverages compliant materials. A soft actor-critic algorithm successfully found dexterous screw separation behaviors in compliant soft robotic fingers. In the evaluation, the system obtained an average success rate of 80%, which was difficult to achieve without the grasp-separate manipulation technique.

This paper presents a methodology to design mechanosensor feedback to oscillator-based controller for worm-like soft-bodied robots. A reinforcement learning technique, i.e., PEPG, is employed to embed appropriate mechanosensor feedback to harness global entrainment among the controller, the body dynamics, and the environment without explicitly designing the interaction between the oscillators. Another reinforcement learning, actor-critic, was applied to train the controller for the simulation models to analyze the effectiveness of PEPG in the system. Furthermore, the gait controller was trained under different body dynamics, i.e., the physical model of a caterpillar and an earthworm. We found that PEPG is suitable for the system probably because it does not add exploration noise to actions and it conducts episode based parameter updates. The simulation results show the proposed method can acquire distinct behavior, i.e., caterpillars' crawling, inching and earthworms' crawling, under different body dynamics. The outcome implies, that by utilizing appropriate learning method, desired functionality can be achieved in soft-bodied robots without explicitly designing their behavior.