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.

Although tendon-driven anthropomorphic robot hands have the potential to achieve human-level dexterity, controlling them is a great challenge owing to their mechanical complexities. Therefore, investigating human-hand control strategies is of the utmost importance. An important skill that enables the versatile manipulation ability of humans is visual posturing, i.e. the skill to make arbitrary hand postures based solely on visual observation. Visual posturing facilitates manipulation learning by enabling visual imitation learning and reusing visually similar past experiences. Therefore, this study investigates a method to replicate visual posturing in anthropomorphic robotic hands. Visual posturing in tendon-driven hands is challenging because of the hysteresis in tendon systems, the partial observability of the problem, and the presence of many actuators owing to the antagonistic tendon arrangement. To address these challenges, we propose a method that combines a model predictive path integral, a world model, and bio-inspired muscle synergies. The evaluation in a physical tendon-driven anthropomorphic robot hand showed that the proposed method achieved better visual posturing performance than a naive regression model. We anticipate that our visual posturing method will lay the foundation for versatile manipulation controllers that can adaptively learn manipulation tasks, similar to humans.

The indoor crowd density monitoring system using BLE beacons is one of the effective ways to prevent overcrowded indoor situations. The indoor crowd density monitoring system consists of a mobile application at the user's side and the beacon sensor network as the infrastructure. Since the performance of crowd density monitoring highly depends on how BLE beacons are placed, BLE beacon placement optimization is fundamental research work. This research proposes a beacon deployment method EABeD to incrementally place the beacons adaptively to the latest signal propagation status. Also, EABeD reduces most walking and measurement labor costs by applying Bayesian optimization and the walking distance optimization algorithm. We conducted the placement optimization experiment in the wild environment and compared the results with placements derived by the simulation-based method and people. The result shows that our proposed method can achieve 26.4% higher detection coverage than the simulation-based approach, 23.2% and 5.2% higher detection coverage than the inexperienced person's solution and the expert's solution. As for the labor cost reduction, our proposed method can reduce 90.2% of the walking distance and 74.4% of the optimization time compared with optimization by the dense data gathering method.

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.

Jamming grippers can grasp objects of various shapes with simple control. However, grasping fragile objects is challenging, as a secure grasp requires firm pressing, which can potentially damage delicate objects. Thus, it is important to achieve an appropriate pressing distance for damage-free grasping. Consequently, the detection of the deformation of objects during grasping is required. In this study, we introduced a tactile sensing method using a Permanent Magnet Elastomer (PME) membrane to detect the initial deformation of grasping objects. During pressing, the transition occurs from a phase in which the gripper primarily deforms to a phase in which the object deforms exposing the object to damage. This phase transition can be detected from the inflection point in magnetic field data, and objects, such as roll cakes or potato chips, can be grasped without being damaged. Moreover, we showed that using multiple magnetometers enables the detection of local deformations of the PME membrane, enabling the gripper to determine the parts of the grasping object that are likely to be deformed. This study enables jamming grippers to delicately grasp fragile objects without damaging them, thus extending their use in sectors, such as the food industry.

Bio-inspired tendon-driven manipulators have the potential to achieve human-level dexterity. However, their control is more complex than prevailing robotic hands because the relation between actuation and hand motion (Jacobian) is hard to obtain. On the other hand, humans maneuver their complex hands skillfully and conduct adaptive object grasping and manipulation. We conjecture that the foundation of this ability is a visual posing of hands (i.e., a skill to make arbitrary hand poses with visual and proprioceptive feedback). Children develop this skill before or in parallel with learning grasping and manipulation. Inspired by this developmental process, this study explored a method to equip compliant tendon-driven manipulators with the visual posing. To overcome the complexity of the system, we used a learning-based approach. Specifically, we adopted PlaNet, model-based reinforcement learning that leverages a dynamics model on a compact latent representation. To further accelerate learning, we restricted the control space using the idea of muscle synergy found in the human body control. We validated the effectiveness of the proposed method in a simulation. We also demonstrated that the posing skill acquired using our method is useful for object grasping. This study will contribute to achieving human-level dexterity in manipulations.

Conventional model theories are not suitable to control soft-bodied robots as deformable materials present rapidly changing behaviors. Neuromorphic electronics are now entering the field of robotics, demonstrating that a highly integrated device can mimic the fundamental properties of a sensory synaptic system, including learning and proprioception. This research work focuses on the physical implementation of a reservoir computing-based network to actuate a soft-bodied robot. More specifically, modeling the hysteresis of a shape memory alloy (SMA) using echo state networks (ESN) in real-world situations represents a novel approach to enable soft machines with task-learning. In this work, we show that not only does our ESN model enable our SMA-based robot with locomotion, but it also discovers a successful strategy to do so. Compared to standard control modeling, established either by theoretical frameworks or from experimental data, here, we gained knowledge a posteriori, guided by the physical interactions between the trained model and the controlled actuator, interactions from which striking patterns emerged, and informed us about what type of locomotion would work best for our robot.

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.

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.

In object picking, knowing the state of picked up objects is very important to conduct succeeding tasks surely. Especially, the ability to count objects in hand is crucial to judge whether the previous picking action was successful or not. This work seeks to endow such ability to a robot manipulator. Vision-based methods cannot be relied on to count objects in hand due to the occlusion problem, especially when dealing with objects smaller than one centimeter like small screws. Hence, number estimation should be conducted from tactile sensor information. However, compact pressure-based tactile sensor arrays can not take fine outlines of such small objects because sensor element size is not small enough, meaning that a simple rule-based approach is not feasible. Furthermore, the tactile sensor array fixed on a rigid plane surface can only contact protruding parts of in-hand objects; thus, the simple installation of tactile sensor arrays on a manipulator surface is insufficient for counting objects. Therefore, in this work, we propose 1) a number estimation method which uses a convolution neural network and 2) to cover a tactile sensor array with soft material to enrich tactile information to improve estimation accuracy. We validated the proposed method using data collected by a simple gripper and achieved 89% accuracy in estimating the small screw number in the gripper.

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.

As robots become more complex, small, and sophisticated, the cost and effort necessary for "wiring" become critical; the complex wiring makes the fabrication costly and necessitates care about space and stiffness of wires, which can inhibit the deformation of soft-bodied robots. The concept of power bus, which powers, controls, and monitors multiple slave modules (e.g., actuators, sensors) via a shared bus is one countermeasure for this challenge. However, handling many slave modules in real-time remains an unsolved issue; prior work suffers from a delay corresponding to the number of slaves or requires a rich signal processing unit in each slave module, which makes them unsuitable for controlling numerous actuators. To address this issue, we propose a frequency-multiplexed power bus, which integrates bandpass filters and load-modulation communication; our method enables us to power, control, and monitor all slave modules at once via a single pair of wires. Through analysis and experiments, we showed that eight nodes can be accommodated within a 9 MHz frequency band and can be independently controlled; finally, a caterpillar-like robot with four sensors and actuators was successfully driven by Ramus.

Due to their flexibility, soft-bodied robots can potentially achieve rich and various behaviors within a single body. However, to date, no methodology has effectively harnessed these robots to achieve such diverse desired functionalities. Controllers that accomplish only a limited range of behaviors in such robots have been handcrafted. Moreover, the behaviors of these robots should be determined through body-environment interactions because an appropriate behavior may not always be manifested even if the body dynamics are given. Therefore, we have proposed SenseCPG-PGPE, a method for automatically designing behaviors for caterpillar-like soft-bodied robots. This method optimizes mechanosensory feedback to a central pattern generator (CPG)-based controller, which controls actuators in a robot, using policy gradients with parameter-based exploration (PGPE). In this article, we deeply investigated this method. We found that PGPE can optimize a CPG-based controller for soft-bodied robots that exhibit viscoelasticity and large deformation, whereas other popular policy gradient methods, such as trust region policy optimization and proximal policy optimization, cannot. Scalability of the method was confirmed using simulation as well. Although SenseCPG-PGPE uses a CPG-based controller, it can achieve nonsteady motion such as climbing a step in a simulated robot. The approach also resulted in distinctive behaviors depending on different body-environment conditions. These results demonstrate that the proposed method enables soft robots to explore a variety of behaviors automatically.

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.