Ultrasound (US) is one of the most widely used modalities for clinical intervention and diagnosis due to the merits of providing non-invasive, radiation-free, and real-time images. However, free-hand US examinations are highly operator-dependent. Robotic US System (RUSS) aims at overcoming this shortcoming by offering reproducibility, while also aiming at improving dexterity, and intelligent anatomy and disease-aware imaging. In addition to enhancing diagnostic outcomes, RUSS also holds the potential to provide medical interventions for populations suffering from the shortage of experienced sonographers. In this paper, we categorize RUSS as teleoperated or autonomous. Regarding teleoperated RUSS, we summarize their technical developments, and clinical evaluations, respectively. This survey then focuses on the review of recent work on autonomous robotic US imaging. We demonstrate that machine learning and artificial intelligence present the key techniques, which enable intelligent patient and process-specific, motion and deformation-aware robotic image acquisition. We also show that the research on artificial intelligence for autonomous RUSS has directed the research community toward understanding and modeling expert sonographers’ semantic reasoning and action. Here, we call this process, the recovery of the “language of sonography”. This side result of research on autonomous robotic US acquisitions could be considered as valuable and essential as the progress made in the robotic US examination itself. This article will provide both engineers and clinicians with a comprehensive understanding of RUSS by surveying underlying techniques. Additionally, we present the challenges that the scientific community needs to face in the coming years in order to achieve its ultimate goal of developing intelligent robotic sonographer colleagues. These colleagues are expected to be capable of collaborating with human sonographers in dynamic environments to enhance both diagnostic and intraoperative imaging. 

pdf/robotic-ultrasound-imaging-state-of-the-art-and-future-14mobyv4lw.pdf

Robotic ultrasound imaging: State-of-the-art and future perspectives

Vision-based teleoperation offers the possibility to endow robots with human-level intelligence to physically interact with the environment, while only requiring low-cost camera sensors. However, current vision-based teleoperation systems are designed and engineered towards a particular robot model and deploy environment, which scales poorly as the pool of the robot models expands and the variety of the operating environment increases. In this paper, we propose AnyTeleop, a unified and general teleoperation system to support multiple different arms, hands, realities, and camera configurations within a single system. Although being designed to provide great flexibility to the choice of simulators and real hardware, our system can still achieve great performance. For real-world experiments, AnyTeleop can outperform a previous system that was designed for a specific robot hardware with a higher success rate, using the same robot. For teleoperation in simulation, AnyTeleop leads to better imitation learning performance, compared with a previous system that is particularly designed for that simulator. Project page: http://anyteleop.com/.

pdf/anyteleop-a-general-vision-based-dexterous-robot-arm-hand-2qsdj7up.pdf

AnyTeleop: A General Vision-Based Dexterous Robot Arm-Hand Teleoperation System

Car-following is a fundamental operation for vehicle control for both ADAS on modern vehicles and vehilce control on autonomous vehicles. Most existing car following mechanisms react to the observations of nearby vehicles in real-time. Unfortunately, lack of capability of taking into account multiple constraints and objectives, these mechanisms lead to poor efficiency, discomfort, and unsafe operations. In this paper, we design and implement a proactive car-following model to take into account safety regulation, efficiency, and comfort using deep reinforcement learning. The evaluation results show that the proactive model not only reduces the number of inefficient and unsafe headway but also eliminates the traffic jerk, compared to human drivers. The model outperformed 79% human drivers in public data set and the road efficiency is only 2% less than the optimal bound. Compared to ACC model, the DDPG model allows 4.4% more vehicles to finish the simulation than ACC model does, and increases the average speed for 28.4%.

Proactive Car-Following Using Deep-Reinforcement Learning

Highly automated vehicles operating at SAE automation level 4 and 5 will not require the occupants’ attention to be on the road at all. They will be free to amuse themselves as passengers. This will have the side effect of making them more vulnerable to motion sickness. Automated vehicles must plan paths which are feasible for the vehicle and comfortable for its occupants. In railway and highway design, paths with clothoid based transitions provide feasibility and comfort. This paper proposes a method for generating such a path using constrained non-linear optimization and compares it to an existing method based on root finding.

Optimal path planning with clothoid curves for passenger comfort

Optimal path planning method based on epsilon-greedy Q-learning algorithm

The current crisis surrounding the COVID-19 pandemic demonstrates the amount of responsibility and the workload on our healthcare system and, above all, on the medical staff around the world. In this work, we propose a promising approach to overcome this problem using robot-assisted telediagnostics, which allows medical experts to examine patients from distance. The designed telediagnostic system consists of two robotic arms. Each robot is located at the doctor and patient sites. Such a system enables the doctor to have a direct conversation via telepresence and to examine patients through robot-assisted inspection (guided tactile and audiovisual contact). The proposed bilateral teleoperation system is redundant in terms of teleoperation control algorithms and visual feedback. Specifically, we implemented two main control modes: joint-based and displacement-based teleoperation. The joint-based mode was implemented due to its high transparency and ease of mapping between Leader and Follower whereas the displacement-based is highly flexible in terms of relative pose mapping and null-space control. Tracking tests between Leader and Follower were conducted on our system using both wired and wireless connections. Moreover, our system was tested by seven medical doctors in two experiments. User studies demonstrated the system's usability and it was successfully validated by the medical experts.

/pdf/tactile-robotic-telemedicine-for-safe-remote-diagnostics-in-157386t7.pdf

Tactile Robotic Telemedicine for Safe Remote Diagnostics in Times of Corona: System Design, Feasibility and Usability Study

Current advances in the development of autonomous cars suggest that driverless cars may see wide-scale deployment in the near future. Research by both industry and academia is driven by potential benefits of this new technology, including reductions in fatalities and improvements in traffic and fuel efficiency as well as greater mobility for people who will or cannot drive cars themselves. A deciding factor for the adoption of self-driving cars besides safety will be the comfort of the passengers. This report looks at cost functions currently used in motion planning methods for autonomous on-road driving. Specifically, how the human perception of how comfortable a trajectory is can be formulated within cost functions.

Optimizing Passenger Comfort in Cost Functions for Trajectory Planning.

In this paper, we introduce a tele-robotic station called “PARTI” that leverages state-of-the-art robotics and virtual reality technology to enable immersive haptic interaction with simulated and remote environments through robotic avatars. Our hardware-in-the-loop framework integrates accurate multibody system dynamics and frictional contacts with digital twins of our robots in a virtual environment with real-time computational capabilities. This model mediated hardware-in-the-loop approach to robotic control allows a teleoperator to use the PARTI system to teach, evaluate, and control various robotic applications. In the current contribution, we focus on the general system description, integrated simulation and control framework, and a series of experiments highlighting the advantages of our approach.

/pdf/parti-a-haptic-virtual-reality-control-station-for-model-2mup0snk.pdf

PARTI-A Haptic Virtual Reality Control Station for Model-Mediated Robotic Applications

Model-Mediated Teleoperation with 3D Dynamic Environment Tracking (MMT-DET): A Comparative Study of Task Performance with Time-Domain Passivity Control

Franka Emika robots have gained significant popularity in research and education due to their exceptional versatility and advanced capabilities. This work introduces panda-py - a Python interface and framework designed to empower Franka Emika robotics with accessible and efficient programming. The panda-py interface enhances the usability of Franka Emika robots, enabling researchers and educators to interact with them more effectively. By leveraging Python's simplicity and readability, users can quickly grasp the necessary programming concepts for robot control and manipulation. Moreover, integrating panda-py with other widely used Python packages in domains such as computer vision and machine learning amplifies the robot's capabilities. Researchers can seamlessly leverage the vast ecosystem of Python libraries, thereby enabling advanced perception, decision-making, and control functionalities. This compatibility facilitates the efficient development of sophisticated robotic applications, integrating state-of-the-art techniques from diverse domains without the added complexity of ROS.

Taming the Panda with Python: A Powerful Duo for Seamless Robotics Programming and Integration

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