A procedure concept for local reflex control of grasping

An architecture is proposed for the control of robotic devices, and in particular of anthropomorphic hands, characterized by a hierarchical structure in which every level of the architecture contains data and control function with varying degree of abstraction. Bottom levels of the hierarchy interface directly with sensors and actuators, and process raw data and motor commands. Higher levels perform more symbolic types of tasks, such as application of boolean rules and general planning operations. Layers implementation has to be consistent with the type of operation and its requirements for real time control. It is proposed to implement the rule level with a Boolean Artificial Neural Network characterized by a response time sufficient for producing reflex corrective action at the actuator level

Parametrical study of miniature generators for large motion applications

Designing a generator for large amplitude motion has lead to an energy-consistent model that also considers the finite dimensions of the device. Using SPICE software we have studied the influence of several design parameters on the output of the generator, including the limited motion of the seismic mass imposed by small system dimensions. Three different types of load circuits are presented, as well as their optimization towards output voltage and power

Man-machine cooperation in advanced teleoperation

Teleoperation experiments at JPL have shown that advanced features in a telerobotic system are a necessary condition for good results, but that they are not sufficient to assure consistently good performance by the operators. Two or three operators are normally used during training and experiments to maintain the desired performance. An alternative to this multi-operator control station is a man-machine interface embedding computer programs that can perform some of the operator's functions. In this paper we present our first experiments with these concepts, in which we focused on the areas of real-time task monitoring and interactive path planning. In the first case, when performing a known task, the operator has an automatic aid for setting control parameters and camera views. In the second case, an interactive path planner will rank different path alternatives so that the operator will make the correct control decision. The monitoring function has been implemented with a neural network doing the real-time task segmentation. The interactive path planner was implemented for redundant manipulators to specify arm configurations across the desired path and satisfy geometric, task, and performance constraints

Generalization of Auto-Regressive Hidden Markov Models to Non-Linear Dynamics and Unit Quaternion Observation Space

Latent variable models are widely used to perform unsupervised segmentation of time series in different context such as robotics, speech recognition, and economics. One of the most widely used latent variable model is the Auto-Regressive Hidden Markov Model (ARHMM), which combines a latent mode governed by a Markov chain dynamics with a linear Auto-Regressive dynamics of the observed state. In this work, we propose two generalizations of the ARHMM. First, we propose a more general AR dynamics in Cartesian space, described as a linear combination of non-linear basis functions. Second, we propose a linear dynamics in unit quaternion space, in order to properly describe orientations. These extensions allow to describe more complex dynamics of the observed state. Although this extension is proposed for the ARHMM, it can be easily extended to other latent variable models with AR dynamics in the observed space, such as Auto-Regressive Hidden semi-Markov Models

Learning of Surgical Gestures for Robotic Minimally Invasive Surgery Using Dynamic Movement Primitives and Latent Variable Models

Full and partial automation of Robotic Minimally Invasive Surgery holds significant promise to improve patient treatment, reduce recovery time, and reduce the fatigue of the surgeons. However, to accomplish this ambitious goal, a mathematical model of the intervention is needed. In this thesis, we propose to use Dynamic Movement Primitives (DMPs) to encode the gestures a surgeon has to perform to achieve a task. DMPs allow to learn a trajectory, thus imitating the dexterity of the surgeon, and to execute it while allowing to generalize it both spatially (to new starting and goal positions) and temporally (to different speeds of executions). Moreover, they have other desirable properties that make them well suited for surgical applications, such as online adaptability, robustness to perturbations, and the possibility to implement obstacle avoidance. We propose various modifications to improve the state-of-the-art of the framework, as well as novel methods to handle obstacles. Moreover, we validate the usage of DMPs to model gestures by automating a surgical-related task and using DMPs as the low-level trajectory generator. In the second part of the thesis, we introduce the problem of unsupervised segmentation of tasks' execution in gestures. We will introduce latent variable models to tackle the problem, proposing further developments to combine such models with the DMP theory. We will review the Auto-Regressive Hidden Markov Model (AR-HMM) and test it on surgical-related datasets. Then, we will propose a generalization of the AR-HMM to general, non-linear, dynamics, showing that this results in a more accurate segmentation, with a less severe over-segmentation. Finally, we propose a further generalization of the AR-HMM that aims at integrating a DMP-like dynamic into the latent variable model

Design for Interpretability: Meeting the Certification Challenge for Surgical Robots

This paper presents a perspective on some issues related to safety in the context of autonomous surgical robots. To meet the challenge of safety certification and bring about acceptance of the technology by the public, we propose principles for a design paradigm that goes in the direction of safety by construction: design with certification in mind, clearly distinguish the notion of safety from that of responsibility, view the human component as scaffolding in the progressive transfer of decision-making to the machine, preserve interpretability by renouncing black-box approaches, leverage interpretability to assign responsibility, and take corrective action only when the semantic of the human-machine interface is violated

Deformable surface registration for breast tumors tracking: A phantom study

A phantom study for breast tumor registration based on the deformation of the external surface is proposed. This study aims at the integration into an image guided system for breast cancer biopsy or ablation. To compensate potentially large breast displacements, due to different positions of the breast during biopsy or ablation compared with preoperative data, where the diagnosis was made, an initial linear alignment using visible landmarks is involved, followed by thin-plate spline (TPS) registration of the linearly aligned surfaces. Subsequently, the TPS deformation will be applied to the tumors. The results were validated using a multi modal phantom of the breast, while the tumors and the surface were segmented on four different positions of the phantom: prone, supine, vertical and on a side. The use of computed tomography (CT) dataset allowed us to obtain a very precise segmentation of the external surface, of the tumors and the landmarks. Despite large variation among the different positions of the phantom due to the gravitational force, the accuracy of the method at the target point was under 5 millimeters. These results allow us to conclude that, using our prototype image registration system, we are able to align acquisition of the breast in different positions with clinically relevant accuracy

Retrospective study on phantom for the application of medical image registration in the operating room scenario

This paper presents a phantom study to asses the feasibility of the medical image registration algorithms in the operating room (OR) scenario. The main issues of the registration algorithms in an OR application are, on one hand, the lack of the initial guess of the registration transformation - the images to be registered may be completely independentand, on the other hand, the multimodality of the data. Other requirements to be addressed by the OR registration algorithms are: real-time execution and the necessity of the validation of the results. This work analyzes how, under these requirements, the current state of the art algorithms in medical image registration may be used and shows which direction should be taken when designing a OR navigation system that includes registration as a component

Stochastic resonance in a suspension of magnetic dipoles under shear flow

We show that a magnetic dipole in a shear flow under the action of an oscillating magnetic field displays stochastic resonance in the linear response regime. To this end, we compute the classical quantifiers of stochastic resonance, i.e. the signal to noise ratio, the escape time distribution, and the mean first passage time. We also discuss limitations and role of the linear response theory in its applications to the theory of stochastic resonance.Comment: 17 pages, 5 figures, approved for publication in PR

Surgicberta: a pre-trained language model for procedural surgical language

Pre-trained language models are now ubiquitous in natural language processing, being successfully applied for many different tasks and in several real-world applications. However, even though there is a wealth of high-quality written materials on surgery, and the scientific community has shown a growing interest in the application of natural language processing techniques in surgery, a pre-trained language model specific to the surgical domain is still missing. The creation and public release of such a model would serve numerous useful clinical applications. For example, it could enhance existing surgical knowledge bases employed for task automation, or assist medical students in summarizing complex surgical descriptions. For this reason, in this paper, we introduce SurgicBERTa, a pre-trained language model specific for the English surgical language, i.e., the language used in the surgical domain. SurgicBERTa has been obtained from RoBERTa through continued pre-training with the Masked language modeling objective on 300 k sentences taken from English surgical books and papers, for a total of 7 million words. By publicly releasing SurgicBERTa, we make available a resource built from the content collected in many high-quality surgical books, online textual resources, and academic papers. We performed several assessments in order to evaluate SurgicBERTa, comparing it with the general domain RoBERTa. First, we intrinsically assessed the model in terms of perplexity, accuracy, and evaluation loss resulting from the continual training according to the masked language modeling task. Then, we extrinsically evaluated SurgicBERTa on several downstream tasks, namely (i) procedural sentence detection, (ii) procedural knowledge extraction, (iii) ontological information discovery, and (iv) surgical terminology acquisition. Finally, we conducted some qualitative analysis on SurgicBERTa, showing that it contains a lot of surgical knowledge that could be useful to enrich existing state-of-the-art surgical knowledge bases or to extract surgical knowledge. All the assessments show that SurgicBERTa better deals with surgical language than a general-purpose pre-trained language model such as RoBERTa, and therefore can be effectively exploited in many computer-assisted applications in the surgical domain