IROS 2023 Papers

Imitation learning has been actively studied in recent years. In particular, skill acquisition by a robot with a fixed body, whose root link position and posture and camera angle of view do not change, has been realized in many cases. On the other hand, imitation of the behavior of robots with floating links, such as humanoid robots, is still a difficult task. In this study, we develop an imitation learning system using a biped robot with a floating link. There are two main problems in developing such a system. The first is a teleoperation device for humanoids, and the second is a control system that can withstand heavy workloads and long-term data collection. For the first point, we use the whole body control device TABLIS. It can control not only the arms but also the legs and can perform bilateral control with the robot. By connecting this TABLIS with the high-power humanoid robot JAXON, we construct a control system for imi-tation learning. For the second point, we will build a system that can collect long-term data based on posture optimization, and can simultaneously move the robot's limbs. We combine high-cycle posture generation with posture optimization methods, including whole-body joint torque minimization and contact force optimization. We designed an integrated system with the above two features to achieve various tasks through imitation learning. Finally, we demonstrate the effectiveness of this system by experiments of manipulating flexible fabrics such that not only the hands but also the head and waist move simultaneously, manipulating objects using legs characteristic of humanoids, and lifting heavy objects that require large forces.

Robot hands that imitate the shape of the human body have been actively studied, and various materials and mechanisms have been proposed to imitate the human body. Although the use of soft materials is advantageous in that it can imitate the characteristics of the human body's epidermis, it increases the number of parts and makes assembly difficult in order to perform complex movements. In this study, we propose a skin-skeleton integrated robot hand that has 15 degrees of freedom and consists of four parts. The developed robotic hand is mostly composed of a single flexible part produced by a 3D printer, and while it can be easily assembled, it can perform adduction, flexion, and opposition of the thumb, as well as flexion of four fingers.

To facilitate the study of how passive leg stiffness influences locomotion dynamics and performance, we have developed an affordable and accessible 400 g quadruped robot driven by tunable compliant laminate legs, whose series and parallel stiffness can be easily adjusted; fabrication only takes 2.5 hours for all four legs. The robot can trot at 0.52 m/s or 4.4 body lengths per second with a 3.2 cost of transport (COT). Through locomotion experiments in both the real world and simulation we demonstrate that legs with different stiffness have an obvious impact on the robot's average speed, COT, and pronking height. When the robot is trotting at 4 Hz in the real world, changing the leg stiffness yields a maximum improvement of 37.1% in speed and 62.0% in COT, showing its great potential for future research on locomotion controller designs and leg stiffness optimizations.

Robotic prosthetic legs have the potential to significantly improve the quality of life for lower limb amputees to perform locomotion in various environments and task conditions. However, these devices lack the capability to recover from internal intrinsic control faults, which can lead to harmful consequences affecting the user's gait performance and eroding trust in these robotic devices. Therefore, a reliable fault detection system is necessary to detect intrinsic faults in a timely manner and provide a compensatory response to mitigate their effects. This paper focuses on designing an active fault detector for a robotic knee prosthesis and demonstrates its effectiveness in real time. The developed system utilizes a Gaussian Process model to estimate knee angular velocity, which is sensitive to intrinsic faults and relies on the difference between estimated velocity and the actual measurement to detect internal control faults. In an offline analysis, the developed detector demonstrated a higher detection rate, lower false alarm ratio, and faster detection time compared with the two approaches reported previously. An online demonstration was also conducted with a unilateral amputee participant and showed performance similar to that of offline analysis. We expect that this detector can be integrated into a fault tolerance strategy to enhance the reliability and safety of robotic prosthetic legs, enabling users to perform their everyday tasks with greater confidence.

This article introduces a novel suture managing device (SMD) and new suture management controller to enable single-arm suture management during autonomous suturing with the Smart Tissue Autonomous Robot (STAR). The primary function of the SMD is to tension and manage the suture thread, a task that was previously carried out by a second manipulator or a human assistant. The SMD and its controller are integrated into STAR's autonomous suturing workflow. Experiments were conducted to quantify the tensioning force of SMD and to evaluate the suture quality of the new single-arm system. The prototype of SMD achieves 1.67N tensioning force with suturing time of 29.1±0.42 seconds per stitch. Our study results demonstrate that the single-arm STAR system with SMD achieves equivalent performance to our previous works in suturing efficiency where suture management was performed with either a dual-armed robotic system or by a human surgical assistant. The study's findings contribute to the field of medical robotics and to our knowledge represent the first known instance of single-arm suturing with suture management during autonomous anastomosis.

This paper introduces a novel interactive approach —Exploratory Experiences— that aims to improve the ability of people to reason about the capabilities and limitations of robotic technology. We focus on two areas: robot navigation and object detection. We evaluate the Exploratory Experiences with a novel approach that measures the participant's ability to predict when the robot will fail, following up with asking the reason and a possible fix. We show that our approach is effective at improving participants' understanding of potential robot navigation failures and that they already have the skills to detect potential object detection failures when presented with the correct stimuli.

The axes of robots and robot-like systems (RLS) usually include e-motor-gearbox-arrangements for optimal connection of the elements. The characteristics of the drive system and thus also of the robot depend strongly on the gears. Different gearbox designs are available which differ in stiffness, efficiency and further properties. For an application-optimal design of RLS a uniform documentation and a comparability of gearbox concepts is a decisive factor. The application-optimal design is supported by an interdisciplinary approach between mechanical engineering and software design, guided by adequate product development methodology. The quite heterogeneous characterization of gearboxes for RLS which is currently the state of the art is a relevant obstacle in the flexible and optimal design of RLS. The paper shows the analysis of the gear structure with unified symbols for specific machine elements and contact types. The introduced method gives insight into the mechanical structure of the gearboxes. Similarities between gear types can thus be revealed. This also enables the classification of new developments in the state of the art. Moreover, the developed method for analyzing the gear structure can be used to determine the characteristics of gears. Examples for these characteristics are backlash, efficiency or stiffness. Specifically, the stiffness of gears can be synthesized by the force action of individual contacts and the individual phenomena that occur with them. The representation by individual phenomena also makes it possible to extend the calculation to include influencing parameters such as temperature that have not been sufficiently taken into account so far.

Modern robotics has enabled the advancement in yield estimation for precision agriculture. However, when applied to the olive industry, the high variation of olive colors and their similarity to the background leaf canopy presents a challenge. Labeling several thousands of very dense olive grove images for segmentation is a labor-intensive task. This paper presents a novel approach to detecting olives without the need to manually label data. In this work, we present the world's first olive detection dataset comprised of synthetic and real olive tree images. This is accomplished by generating an auto-labeled photorealistic 3D model of an olive tree. Its geometry is then simplified for lightweight rendering purposes. In addition, experiments are conducted with a mix of synthetically generated and real images, yielding an improvement of up to 66% compared to when only using a small sample of real data. When access to real, human-labeled data is limited, a combination of mostly synthetic data and a small amount of real data can enhance olive detection.

Gait analysis can provide relevant information about the physical and neurological conditions of individuals. For this reason, several studies have recently been carried out in an attempt to monitor people's gait and automatically detect gait anomalies. Among the various monitoring systems available for gait analysis, wearable sensors are considered the gold standard due to their wide capture range and low cost. However, in the case of people that require assistive devices for walking, some studies have proposed the use of sensorized devices in order to minimize invasiveness. Nevertheless, there is still a lack of comparative works that evaluate the performance of sensorized assistive devices for walking with widely used wearable sensors. Hence, this paper presents a comparison between the performance of accelerometer-based wearable sensors and a sensorized tip developed by the authors to detect gait anomalies. The comparative study has been carried out in a controlled environment with five healthy subjects, in which three different physical states have been simulated. A machine-learning based anomaly detection approach has been implemented based on the data captured by a set of wearable sensors and the sensorized tip, and the overall performance of both monitoring systems has been evaluated. Results show that even if both devices can provide an average accuracy of more than 80% in gait anomaly detection, the sensorized tip provides better performance.

Tree fruit growers around the world are facing labor shortages for critical operations, including harvest and pruning. There is a great interest in developing robotic solutions for these labor-intensive tasks, but current efforts have been prohibitively costly, slow, or require a reconfiguration of the orchard in order to function. In this paper, we introduce an alternative approach to robotics using a novel and low-cost soft-growing robotic platform. Our platform features the ability to extend up to 1.2 m linearly at a maximum speed of 0.27 m/s. The soft-growing robotic arm can operate with a terminal payload of up to 1.4 kg (4.4 N), more than sufficient for carrying an apple. This platform decouples linear and steering motions to simplify path planning and the controller design for targeting. We anticipate our platform being relatively simple to maintain compared to rigid robotic arms. Herein we also describe and experimentally verify the platform's kinematic model, including the prediction of the relationship between the steering angle and the angular positions of the three steering motors. Information from the model enables the position controller to guide the end effector to the targeted positions faster and with higher stability than without this information. Overall, our research show promise for using soft-growing robotic platforms in orchard operations.

Flying robots can exploit perching abilities to position themselves on strategically-chosen locations and monitor the areas of interest from a critical vantage point. Moreover, they can significantly extend their battery life by turning off the propulsion systems when carrying out a surveillance mission. However, unknown disturbances arise from the physical interactions between the robot and the object, making it challenging to stabilize the robot during perching. In this paper, we present a Whole-body Grasping and Perching (WHOPPEr) Drone, which is capable of fast and robust perching by utilizing its entire body as the grasper in lieu of an add-on grasper. We first present the design concept, parameter selection and characterization of the novel whole-body grasping drone. Next, we analyze the grasping ability of the morphing chassis and present an aerodynamic analysis for the effect of motor thrust on the compliant arm. We finally demonstrate, via real-time experiments, the performance of WHOPPEr in autonomous perching and payload delivery tasks.

Generating dynamic jumping motions on legged robots remains a challenging control problem as the full flight phase and large landing impact are expected. Compared to quadrupedal robots or other multi-legged robots, bipedal robots place higher requirements for the control strategy given a much smaller support polygon. To solve this problem, a novel heuristic landing planner is proposed in this paper. With the momentum feedback during the flight phase, landing locations can be updated to minimize the influence of uncertainties from tracking errors or external disturbances when landing. To the best of our knowledge, this is the first approach to take advantage of the flight phase to reduce the impact of the jump landing which is implemented in the actual robot. By integrating it with a modified kino-dynamics motion planner with centroidal momentum and a low-level controller which explores the whole-body dynamics to hierarchically handle multiple tasks, a complete and versatile jumping control framework is designed in this paper. Extensive results of simulation and hardware jumping experiments on a miniature bipedal robot with proprioceptive actuation are provided to demonstrate that the proposed framework is able to achieve human-like efficient and robust jumping tasks, including directional jump, twisting jump, step jump, and somersaults.

To provide an alternative to conventional large-scale fitness equipment, we previously developed a soft passive wearable device for upper limb resistance exercises that utilized elastic exercise bands. However, the user was required to manually adjust the level of strength. In this paper, we introduce a novel wearable fitness device for upper limb exercise that constitutes cable-driven actuation to control the resistance profiles. Our proposed device allows for the generation of isotonic force trajectories, similar to those produced during dumbbell lifting, utilizing custom cable-driven actuators. The cable path of the device was determined based on the results of user testing aimed at optimizing muscle stimulation levels. In addition, linear resonant actuators were installed at customized haptic handles and upper arm modules to enhance proprioceptive sensitivity and exercise efficacy. The immersive “exer-tainment” user display interface was also redesigned to increase user motivation. The efficacy of our device was assessed by comparing the surface electromyography (sEMG) activities of upper limb muscles during chest press and triceps extension exercises performed with our device versus those using traditional weight training machines and rubber bands. It was found that our device could effectively strengthen during arm exercise, such as triceps extension.

Legged animals have developed a variety of modes of locomotion to adapt to the diverse and unknown terrain challenges posed in the natural world. Legged robots, however, have been largely limited to specializing in one domain, with few that have endeavored to bridge the gap between two. In this work we present the Scansorial, Terrestrial, and Aquatic Robot Quadruped (STARQ), a novel legged robot capable of bridging three different domains with three modes of locomotion: walking, climbing, and swimming. In this study we describe model-based design techniques as well as design innovations that have made multimodal locomotion possible including waterproof hips for 2-DOF high torque legs, legs capable of effective power transmission in three modes, and bi-directionally compliant feet for walking and attaching to vertical surfaces. To demonstrate the robot's capabilities we present locomotion test data including speed and cost of transport in each of these domains. We also demonstrate the capability to transition from walking to swimming in a natural environment.

The rotation axis of human knee joint varies according to knee flexion angles. That is, human knee movement is spatial with dominant flexional rotation. Knee joints of most lower-limb exoskeletons were, however, realized with a simple revolute pair for design simplicity. Wearing the knee joint with a simple revolute pair constrains inevitably natural parasitic motion of human knee joint. Rigid constraints imposed due to the simple knee joint lowers wearability and comfortability. In addition, it may act as potential risks to harm the knee joint structure of the wearer. The concept of polycentric knee is a well-known approach to mimic the variation of knee rotation center. Polycentric knees, however, realize only the planar trace of projected points of rotation axis. That is, parasitic knee rotations of varus and internal rotation occuring naturally during knee flexion cannot be realized by polycentric knees. In order to resolve this, a novel spherical knee joint of exoskeleton is introduced in this paper to realize knee spherical movements. For the design, change in instantaneous rotation axes during knee flexion is formulated to a spherical trace as a function of knee flexion angles. A spherical four-bar linkage is suggested to realize the axis trajectory, not the point trace. A method to find instantaneous rotation axis and rotation matrix of coupler link is derived, when the angle of input link is given. Using the kinematic relations, kinematic parameters of the mechanism are optimized to minimize angle difference between the instantaneous axis of coupler and the required axis of knee rotation. Finally, based on the synthesized kinematic parameters, a prototype design was introduced in this paper.

Numerous attempts have been made to reduce metabolic energy while running with the help of assistive devices. A majority of studies on the assistive devices have focused on the assisting torque in the sagittal plane. In the case of running, however, the abduction torque in the frontal plane at the hip joint is greater than the flexion/extension torque in the sagittal plane. During running, as does an elastic body, the abduction torque and the motion of the hip joint have a linear relationship, but are opposite in direction. It is expected that the hip abduction torque can be assisted with a simple passive method by using an elastic body that reflects the movement characteristics of the hip joint. In this study, therefore, a system to assist hip abduction torque using a leaf spring was proposed with a prototype testing. While running with the assist system proposed, the leaf spring aids the abduction torque on the stance phase, and the torque is not generated due to the passive revolute joint on the swing phase. The joint angle is changed with respective to the rotation in the flexion/extension direction to prevent discomfort torque during swing phase and to increase the duration of the torque action during stance phase. A preliminary test was conducted on one subject using the prototype of the hip joint abduction torque assistive device. The participant with the assistive device reduced metabolic energy by 5% compared to the case without abduction torque assist while running at 2.5m/s. In order to increase the amount of metabolic reduction, the device shall be supplemented by system mass reduction and hip joint position optimization.

The flexure-based XYZ micropositioner with hybrid configuration has become more prevalent due to the characteristics of less mechanism decoupling and high motion precision. However, traditional mechanism design suffers from a large plane occupation with Z stage stacking, which leads to a low space-utilization efficiency. To address this issue, a novel conceptual design is proposed in this paper by integrating a spatially structured XY stage and an embedded Z stage together. After completing the design of the mechanism, the driving stiffness of the stage in three axes is evaluated by the mechanics analysis. Then, the model is verified by performing finite element analysis simulation study and experimental test. The theoretical model, simulation results, and experimental data indicate a good agreement. Experimental results show that the proposed flexure-based XYZ micropositioner can deliver a stroke of \boldsymbol{4.15 \text{mm} \times 4.06 \text{mm} \times 0.04}\boldsymbol{4.15 \text{mm} \times 4.06 \text{mm} \times 0.04} mm with a physical size of \boldsymbol{116 \text{mm} \times 116 \text{mm} \times 45 \text{mm}. }\boldsymbol{116 \text{mm} \times 116 \text{mm} \times 45 \text{mm}. } The performance comparison reveals that it has a superior space-utilization efficiency. In consideration of the feasibility of the proposed conceptual design, it provides a reference for diversified and refined design of XYZ micropositioners.

This paper presents the design of a new multi-arm robotic system with mobile magnetic actuation and extracorpo-real ultrasound guidance dedicated to magnetic catheterization. The kinematic model of the external mobile actuation arm (EMAA) and extracorporeal ultrasound-integrated tracking arm (EUTA) are derived based on Denavit-Hartenberg (DH) parameters, including specially designed end-effectors. The synchronous control scheme for the mobile magnet and mobile ultrasound probe is introduced with polar coordinate-based magnetic actuation and visual servo-based ultrasound tracking method. Meanwhile, a denoising algorithm based on Speckle Reduction Anisotropic Diffusion (SRAD) is implemented. The effectiveness of the proposed robotic system has been verified by conducting several experimental studies, e.g., ex-vivo tests of catheter steering in endovascular phantom and soft tissue-imitating phantom with the average error of 0.32 mm and signal-to-noise-ratio (SNR) of 12.2 for the ultrasound imaging.

Modulating the stiffness of soft actuators is crucial for improving the efficiency of interaction with the environment. However, current stiffness modulation mechanisms are hard to achieve high lateral stiffness and a wide range of bending stiffness simultaneously. Here, we draw inspiration from the anatomical structure of the finger and propose a bi-directional tunable stiffness actuator (BTSA). BTSA is a soft-rigid hybrid structure that combines air-tendon hybrid actuation (ATA) and bone-like structures (BLS). We develop a corresponding fabrication method and a stiffness analysis model to support the design of BLS. The results show that the influence of the BLS on bending deformation is negligible, with a distal point distance error of less than 1.5 mm. Moreover, the bi-directional tunable stiffness is proved to be functional. The bending stiffness can be tuned by ATA from 0.23 N/mm to 0.70 N/mm, with a magnification of 3 times. The addition of BLS improves lateral stiffness up to 4.2 times compared with the one without BLS, and the lateral stiffness can be tuned decoupling within 1.2 to 2.1 times (e.g. from 0.35 N/mm to 0.46 N/mm when the bending angle is 45 deg). Finally, a four-BTSA gripper is developed to conduct horizontal lifting and grasping tasks to demonstrate the advantages of BTSA.