-- As robot technology advances, collaboration between humans and robots will become more prevalent in industrial tasks. When humans run into issues in such scenarios, a likely future involves relying on artificial agents or robots for aid. This study identifies key aspects for the design of future user-assisting agents. We analyze quantitative and qualitative data from a user study examining the impact of on-demand assistance received from a remote human in a human-robot collaboration (HRC) assembly task. We study scenarios in which users require help and we assess their experiences in requesting and receiving assistance. Additionally, we investigate participants' perceptions of future non-human assisting agents and whether assistance should be on-demand or unsolicited. Through a user study, we analyze the impact that such design decisions (human or artificial assistant, on-demand or unsolicited help) can have on elicited emotional responses, productivity, and preferences of humans engaged in HRC tasks. I. INTRODUCTION The increased availability of robot teammates (e.g., collaborative robots) will create work settings without human teammates, where assistance comes from artificial agents [1], [2]. While this shift can offer benefits like increased efficiency and safety, it also raises concerns. A lack of timely assistance can lead to task stagnation, increasing cognitive load and stress, which harm productivity and mental health [3]. Prolonged stress may even contribute to conditions like anxiety, depression, and gastrointestinal illnesses [4], [5].

The hybridisation of robot-assisted gait training and functional electrical stimulation (FES) can provide numerous physiological benefits to neurological patients. However, the design of an effective hybrid controller poses significant challenges. In this over-actuated system, it is extremely difficult to find the right balance between robotic assistance and FES that will provide personalised assistance, prevent muscle fatigue and encourage the patient's active participation in order to accelerate recovery. In this paper, we present an adaptive hybrid robot-FES controller to do this and enable the triadic collaboration between the patient, the robot and FES. A patient-driven controller is designed where the voluntary movement of the patient is prioritised and assistance is provided using FES and the robot in a hierarchical order depending on the patient's performance and their muscles' fitness. The performance of this hybrid adaptive controller is tested in simulation and on one healthy subject. Our results indicate an increase in tracking performance with lower overall assistance, and less muscle fatigue when the hybrid adaptive controller is used, compared to its non adaptive equivalent. This suggests that our hybrid adaptive controller may be able to adapt to the behaviour of the user to provide assistance as needed and prevent the early termination of physical therapy due to muscle fatigue.

Purpose - Skullbase surgery demands exceptional precision when removing bone in the lateral skull base. Robotic assistance can alleviate the effect of human sensory-motor limitations. However, the stiffness and inertia of the robot can significantly impact the surgeon's perception and control of the tool-to-tissue interaction forces. Methods - We present a situational-aware, force control technique aimed at regulating interaction forces during robot-assisted skullbase drilling. The contextual interaction information derived from the digital twin environment is used to enhance sensory perception and suppress undesired high forces. Results - To validate our approach, we conducted initial feasibility experiments involving a medical and two engineering students. The experiment focused on further drilling around critical structures following cortical mastoidectomy. The experiment results demonstrate that robotic assistance coupled with our proposed control scheme effectively limited undesired interaction forces when compared to robotic assistance without the proposed force control. Conclusions - The proposed force control techniques show promise in significantly reducing undesired interaction forces during robot-assisted skullbase surgery. These findings contribute to the ongoing efforts to enhance surgical precision and safety in complex procedures involving the lateral skull base.

Objective technical skill assessment is crucial for effective training of new surgeons in robot-assisted surgery. With advancements in surgical training programs in both physical and virtual environments, it is imperative to develop generalizable methods for automatically assessing skills. In this paper, we propose a novel approach for skill assessment by transferring domain knowledge from labeled kinematic data to unlabeled data. Our approach leverages labeled data from common surgical training tasks such as Suturing, Needle Passing, and Knot Tying to jointly train a model with both labeled and unlabeled data. Pseudo labels are generated for the unlabeled data through an iterative manner that incorporates uncertainty estimation to ensure accurate labeling. We evaluate our method on a virtual reality simulated training task (Ring Transfer) using data from the da Vinci Research Kit (dVRK). The results show that trainees with robotic assistance have significantly higher expert probability compared to these without any assistance, p < 0.05, which aligns with previous studies showing the benefits of robotic assistance in improving training proficiency. Our method offers a significant advantage over other existing works as it does not require manual labeling or prior knowledge of the surgical training task for robot-assisted surgery.

In this paper, we present AR3n (pronounced as Aaron), an assist-as-needed (AAN) controller that utilizes reinforcement learning to supply adaptive assistance during a robot assisted handwriting rehabilitation task. Unlike previous AAN controllers, our method does not rely on patient specific controller parameters or physical models. We propose the use of a virtual patient model to generalize AR3n across multiple subjects. The system modulates robotic assistance in realtime based on a subject's tracking error, while minimizing the amount of robotic assistance. The controller is experimentally validated through a set of simulations and human subject experiments. Finally, a comparative study with a traditional rule-based controller is conducted to analyze differences in assistance mechanisms of the two controllers.

In this paper, we present an intelligent Assistant for Robotic Therapy (iART), that provides robotic assistance during 3D trajectory tracking tasks. We propose a novel LSTM-based robot learning from demonstration (LfD) paradigm to mimic a therapist's assistance behavior. iART presents a trajectory agnostic LfD routine that can generalize learned behavior from a single trajectory to any 3D shape. Once the therapist's behavior has been learned, iART enables the patient to modify this behavior as per their preference. The system requires only a single demonstration of 2 minutes and exhibits a mean accuracy of 91.41% in predicting, and hence mimicking a therapist's assistance behavior. The system delivers stable assistance in realtime and successfully reproduces different types of assistance behaviors.

Fully robotic surgery may be a couple of decades away. But one thing could speed it up: billionaire engineer Elon Musk's very public mission to Mars. Early this year a fully autonomous robot completed a complex soft tissue surgery for the very first time at Johns Hopkins University. The Smart Tissue Autonomous Robot, or STAR, "excelled at suturing two ends of intestine--one of the most intricate and delicate tasks in abdominal surgery," Johns Hopkins reported. That said, there is a big caveat.

Mixed reality and AI can help make surgeries safer by assisting surgeons during the process. From providing 3D imaging to handling instruments, AI is a vital part of the operating room. Here, we discuss what mixed reality means and how AI is taking surgeries to the next level. Artificial intelligence, machine learning, and computer vision are becoming an essential part of the healthcare industry. AI is helping doctors, nurses, and the hospital administration streamline patients' records, accurately diagnose the medical condition, and provide better treatment.

An AI-powered robotic arm can perform'less painful' colonoscopies to check for bowel cancer by using a magnet to externally steer a camera probe through the gut. The system -- from a team led from Leeds -- could prove to be the first major update in decades to the procedure, which is used some 100,000 times each year in the UK. In a colonoscopy, a long, thin, camera-ended probe is passed through the rectum and colon to hunt for and remove abnormalities and take tissue samples. The examination can be uncomfortable for the patient -- and requires highly skilled doctors to be performed, limiting the availability of the procedure. The artificially intelligent system, however, will aid less experienced doctors and nurses in safely guiding the probe to precise locations within the colon.

Recent advancements in spine surgery have come in the form of robotics, a specialised approach to a complex procedure that allows planning a surgery and facilitates highly accurate and predictable execution of the plan. Robotics is particularly helpful in inserting implants in the spine; for a spine surgeon, robotics is a boon, and has marked the beginning of a new era in spine surgery. Planning is the foundation of surgical robotics; the ability to plan a surgery in an advanced 3D visual environment allows a surgeon to factor any unique anatomy or challenges associated with the patient much ahead of the surgery. The technology allows surgeons to use images from a computerised tomography scan (CT scan) taken before surgery to create a blueprint for each case. These images are loaded into a computerised 3D planning system so that in the operating room, surgeons do the physical surgery while the system guides his or her instruments based on pre-operative planning of spinal implant placement.