Sensory substitution for force feedback recovery: A perception experimental study

Robotic-assisted surgeries are commonly used today as a more efficient alternative to traditional surgical options. Both surgeons and patients benefit from those systems, as they offer many advantages, including less trauma and blood loss, fewer complications, and better ergonomics. However, a remaining limitation of currently available surgical systems is the lack of force feedback due to the teleoperation setting, which prevents direct interaction with the patient. Once the force information is obtained by either a sensing device or indirectly through vision-based force estimation, a concern arises on how to transmit this information to the surgeon. An attractive alternative is sensory substitution, which allows transcoding information from one sensory modality to present it in a different sensory modality. In the current work, we used visual feedback to convey interaction forces to the surgeon. Our overarching goal was to address the following question: How should interaction forces be displayed to support efficient comprehension by the surgeon without interfering with the surgeon’s perception and workflow during surgery? Until now, the use the visual modality for force feedback has not been carefully evaluated. For this reason, we conducted an experimental study with two aims: (1) to demonstrate the potential benefits of using this modality and (2) to understand the surgeons’ perceptual preferences. The results derived from our study of 28 surgeons revealed a strong positive acceptance of the users (96%) using this modality. Moreover, we found that for surgeons to easily interpret the information, their mental model must be considered, meaning that the design of the visualizations should fit the perceptual and cognitive abilities of the end user. To our knowledge, this is the first time that these principles have been analyzed for exploring sensory substitution in medical robotics. Finally, we provide user-centered recommendations for the design of visual displays for robotic surgical systems.Peer ReviewedPostprint (author's final draft

Haptic-Based Shared-Control Methods for a Dual-Arm System

We propose novel haptic guidance methods for a dual-arm telerobotic manipulation system, which are able to deal with several different constraints, such as collisions, joint limits, and singularities. We combine the haptic guidance with shared-control algorithms for autonomous orientation control and collision avoidance meant to further simplify the execution of grasping tasks. The stability of the overall system in various control modalities is presented and analyzed via passivity arguments. In addition, a human subject study is carried out to assess the effectiveness and applicability of the proposed control approaches both in simulated and real scenarios. Results show that the proposed haptic-enabled shared-control methods significantly improve the performance of grasping tasks with respect to the use of classic teleoperation with neither haptic guidance nor shared control

Increasing Transparency and Presence of Teleoperation Systems Through Human-Centered Design

Teleoperation allows a human to control a robot to perform dexterous tasks in remote, dangerous, or unreachable environments. A perfect teleoperation system would enable the operator to complete such tasks at least as easily as if he or she was to complete them by hand. This ideal teleoperator must be perceptually transparent, meaning that the interface appears to be nearly nonexistent to the operator, allowing him or her to focus solely on the task environment, rather than on the teleoperation system itself. Furthermore, the ideal teleoperation system must give the operator a high sense of presence, meaning that the operator feels as though he or she is physically immersed in the remote task environment. This dissertation seeks to improve the transparency and presence of robot-arm-based teleoperation systems through a human-centered design approach, specifically by leveraging scientific knowledge about the human motor and sensory systems. First, this dissertation aims to improve the forward (efferent) teleoperation control channel, which carries information from the human operator to the robot. The traditional method of calculating the desired position of the robot\u27s hand simply scales the measured position of the human\u27s hand. This commonly used motion mapping erroneously assumes that the human\u27s produced motion identically matches his or her intended movement. Given that humans make systematic directional errors when moving the hand under conditions similar to those imposed by teleoperation, I propose a new paradigm of data-driven human-robot motion mappings for teleoperation. The mappings are determined by having the human operator mimic the target robot as it autonomously moves its arm through a variety of trajectories in the horizontal plane. Three data-driven motion mapping models are described and evaluated for their ability to correct for the systematic motion errors made in the mimicking task. Individually-fit and population-fit versions of the most promising motion mapping model are then tested in a teleoperation system that allows the operator to control a virtual robot. Results of a user study involving nine subjects indicate that the newly developed motion mapping model significantly increases the transparency of the teleoperation system. Second, this dissertation seeks to improve the feedback (afferent) teleoperation control channel, which carries information from the robot to the human operator. We aim to improve a teleoperation system a teleoperation system by providing the operator with multiple novel modalities of haptic (touch-based) feedback. We describe the design and control of a wearable haptic device that provides kinesthetic grip-force feedback through a geared DC motor and tactile fingertip-contact-and-pressure and high-frequency acceleration feedback through a pair of voice-coil actuators mounted at the tips of the thumb and index finger. Each included haptic feedback modality is known to be fundamental to direct task completion and can be implemented without great cost or complexity. A user study involving thirty subjects investigated how these three modalities of haptic feedback affect an operator\u27s ability to control a real remote robot in a teleoperated pick-and-place task. This study\u27s results strongly support the utility of grip-force and high-frequency acceleration feedback in teleoperation systems and show more mixed effects of fingertip-contact-and-pressure feedback

Evaluation of Haptic and Visual Cues for Repulsive or Attractive Guidance in Nonholonomic Steering Tasks.

Remote control of vehicles is a difficult task for operators. Support systems that present additional task information may assist operators, but their usefulness is expected to depend on several factors such as 1) the nature of conveyed information, 2) what modality it is conveyed through, and 3) the task difficulty. In an exploratory experiment, these three factors were manipulated to quantify their effects on operator behavior. Subjects ( $n = {{15}}$) used a haptic manipulator to steer a virtual nonholonomic vehicle through abstract environments, in which obstacles needed to be avoided. Both a simple support conveying near-future predictions of the trajectory of the vehicle and a more elaborate support that continuously suggests the path to be taken were designed (factor 1). These types of information were offered either with visual or haptic cues (factor 2). These four support systems were tested in four different abstracted environments with decreasing amount of allowed variability in realized trajectories (factor 3). The results show improvements for the simple support only when this information was presented visually, but not when offered haptically. For the elaborate support, equally large improvements for both modalities were found. This suggests that the elaborate support is better: additional information is key in improving performance in nonholonomic steering tasks

Deep Imitation Learning for Humanoid Loco-manipulation through Human Teleoperation

We tackle the problem of developing humanoid loco-manipulation skills with deep imitation learning. The difficulty of collecting task demonstrations and training policies for humanoids with a high degree of freedom presents substantial challenges. We introduce TRILL, a data-efficient framework for training humanoid loco-manipulation policies from human demonstrations. In this framework, we collect human demonstration data through an intuitive Virtual Reality (VR) interface. We employ the whole-body control formulation to transform task-space commands by human operators into the robot's joint-torque actuation while stabilizing its dynamics. By employing high-level action abstractions tailored for humanoid loco-manipulation, our method can efficiently learn complex sensorimotor skills. We demonstrate the effectiveness of TRILL in simulation and on a real-world robot for performing various loco-manipulation tasks. Videos and additional materials can be found on the project page: https://ut-austin-rpl.github.io/TRILL.Comment: Submitted to Humanoids 202

SARSCEST (human factors)

People interact with the processes and products of contemporary technology. Individuals are affected by these in various ways and individuals shape them. Such interactions come under the label 'human factors'. To expand the understanding of those to whom the term is relatively unfamiliar, its domain includes both an applied science and applications of knowledge. It means both research and development, with implications of research both for basic science and for development. It encompasses not only design and testing but also training and personnel requirements, even though some unwisely try to split these apart both by name and institutionally. The territory includes more than performance at work, though concentration on that aspect, epitomized in the derivation of the term ergonomics, has overshadowed human factors interest in interactions between technology and the home, health, safety, consumers, children and later life, the handicapped, sports and recreation education, and travel. Two aspects of technology considered most significant for work performance, systems and automation, and several approaches to these, are discussed