surgeon follow a desired path, to avoid dangerous tool excursions, and to provide physiological relief from having to hold the surgical tool for extended periods of time. This approach has been applied to vitreoretinal microsurgery (23). It also being commercialized for tool stabilization in the upper airway where the length of needle drivers and graspers due to the transoral access makes precision manipulation challenging (Galen Robotics Inc.). The system has also been tested for applications involving use of image-guided barrier virtual fixtures for safe bone deburring during mastoidectomy, where the risk of damaging the facial nerve is mitigated by the image-guided robotic system. A third approach to robotic microsurgery is to use teleoperation. In this approach, the clinician does not need to hold the tool at all but rather controls the robotic tool through an input device. This technology provides all of the advantages of cooperative robots with the addition of motion scaling and the potential for reduced inertial and frictional effects. Such a system for intraocular surgery has undergone first-in-human testing (24). What the future holds There are several exciting developments that will enable a new wave of innovation of procedure-specific robotic platforms. In the past decade, we have seen some works in the area of electrode array steering and insertion for cochlear implants. These examples point to the potential of harnessing soft robotics and possibly magnetic actuation for creating new platforms for deep navigation. We also have seen exciting work combining manipulation and diagnostic sensing. We believe that there is still a need for solutions enabling the use of in vivo sensing for improving surgeon performance. Systems that can use intraoperative sensing with adaptive assistive behaviors (virtual fixtures or shared control) will also allow surgeons to achieve rapid clinical deployability and improved perception and performance. Assistive wearable robotics Assistive wearable robotics focuses on the design and control of wearable robotic devices intended to improve the mobility or functionality of individuals with musculoskeletal or neuromuscular impairment. Areas of contribution in this field include the development of robotic limbs (also called powered prostheses) for individuals with upper and lower extremity amputation and the development of exoskeletons (also called powered orthoses) for individuals with neuromuscular impairment, such as those with spinal cord injury, stroke, multiple sclerosis, or cerebral palsy. Although the field has historic roots dating at least to the early 1960s (see, for example, the Proceedings of the International Symposium on External Control of Human Extremities, 1963), the decade between 2010 and 2020 saw the fully realized emergence of it. Although an enumeration of research in the field is beyond the scope of this short summary, three major categories of research include (i) powered lower limb prostheses, (ii) neurally controlled upper limb prostheses, and (iii) lower limb exoskeletons (LLEs). In the area of lower limb prosthetics, the state of the art before (circa) 2010 was energetically passive devices. The past decade saw the introduction of power into prosthetic knee and ankle joints. Because powered devices have volition, new control methods are required that ensure coordination between human and device. Approaches to doing so include piecewise passive impedance control, such as that described in (25), which provides assurances of locally passive behavior, and phase variable control, such as that described in (26), which supplants finite-state structures with a uniform control policy. Further, because powered devices substantially increased the range of activity-specific behaviors of such devices, methods of activity recognition are required to determine a current activity state and intent to change activity state. Pattern recognition structures consisting of data reduction and classification methods were established in which a given movement activity is inferred in real time based on patterns of movement, such as the methods described by (27). Unlike lower limb devices, the state of the art before 2010 in upper limb prosthetics was powered (i.e., myoelectric prostheses). However, these devices typically used single-DOF hands and sequential myoelectric control. The past decade has seen the emergence of several multigrasp hands and the development of corresponding multigrasp and/or multi-DOF hand and arm control methods. Such control methods include electromyography (EMG)–based pattern recognition approaches in which multichannel EMG is used as input to a pattern classifier, which, in turn, selects a corresponding desired grasp posture or arm movement and subsequently executes the corresponding coordinated hand and/or arm movement (28). The decade also saw the use of implanted electrodes for the efferent motor control of a multigrasp arm prosthesis (29) and to provide meaningful neural sensory feedback corresponding to a hand prosthesis, such as the impressive work reported by (30) and (31). Downloaded from https://www.science.org on November 16, 2021 Dupont et al., Sci. Robot. 6, eabi8017 (2021) 10 November 2021 SCIENCE ROBOTICS | REVIEW 7 of 15 Scholarly research and development in the area of LLEs over the decade has seen