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

How force perception changes in different refresh rate conditions

n this work we consider the role of different refresh rates of the force feedback physical engine for haptics environments, such as robotic surgery and virtual reality surgical training systems. Two experimental force feedback tasks are evaluated in a virtual environment. Experiment I is a passive contact task, where the hand-grip is held waiting for the force feedback perception given by the contact with virtual objects. Experiment II is an active contact task, where a tool is moved in a direction until the contact perception with a pliable object. Different stiffnesses and refresh rates are factorially manipulated. To evaluate differences in the two tasks, we account for latency time inside the wall, penetration depth, and maximum force exerted against the object surface. The overall result of these experiments shows an improved sensitivity in almost all variables considered with refresh rates of 500 and 1,000 Hz compared with a refresh rate of 250 Hz, but no improved sensitivity is showed among them

Molecular Signature in Human and Animal Prion Disorders

In human and animal transmissible spongiform encephalopathies (TSEs) or prion disorders, biochemical analysis of disease-associated prion protein (PrPTSE) is a first-line approach for large scale routine testing and for a rapid molecular typing. This characterization is based on conformational properties of PrPTSE enciphered in its secondary and tertiary structures and on glycosylation profile. Several biochemical approaches are helpful in distinguishing PrPTSE forms in human prion diseases. In particular, in sporadic Creutzfedlt-Jakob disease (CJD), PrPTSE is characterized by two main glycotypes conventionally named PrPTSE type 1 and PrPTSE type 2 based on the apparent gel migration at 19 kDa and 17.5 kDa and glycofrom ratio. Further, there are PrPTSE low molecular weight fragments which correlate to distinct phenotypes of sCJD. Finally, by using two-dimensional PAGE analysis, which separates PrPTSE on both isoelectric point and molecular size, we were able to detect two distinct migration pattern in PrPTSE type 2, one in subjects with MM at codon 129 and another in MV, VV. We here provide an extensive PrPTSE biochemical analysis in humans and animals affected with prion disorders. Further, we showed that PrPTSE glycotypes observed in CJD shared similarities with PrPTSE in bovine spongiform encephalopathies (BSEs). These signature similarities obtained by a biochemical analysis had been further confirmed by experimental transmission

Overcoming some drawbacks of Dynamic Movement Primitives

Dynamic Movement Primitives (DMPs) is a framework for learning a point-to-point trajectory from a demonstration. Despite being widely used, DMPs still present some shortcomings that may limit their usage in real robotic applications. Firstly, at the state of the art, mainly Gaussian basis functions have been used to perform function approximation. Secondly, the adaptation of the trajectory generated by the DMP heavily depends on the choice of hyperparameters and the new desired goal position. Lastly, DMPs are a framework for ‘one-shot learning’, meaning that they are constrained to learn from a unique demonstration. In this work, we present and motivate a new set of basis functions to be used in the learning process, showing their ability to accurately approximate functions while having both analytical and numerical advantages w.r.t. Gaussian basis functions. Then, we show how to use the invariance of DMPs w.r.t. affine transformations to make the generalization of the trajectory robust against both the choice of hyperparameters and new goal position, performing both synthetic tests and experiments with real robots to show this increased robustness. Finally, we propose an algorithm to extract a common behavior from multiple observations, validating it both on a synthetic dataset and on a dataset obtained by performing a task on a real robot

How force perception changes in different refresh rate conditions

n this work we consider the role of different refresh rates of the force feedback physical engine for haptics environments, such as robotic surgery and virtual reality surgical training systems. Two experimental force feedback tasks are evaluated in a virtual environment. Experiment I is a passive contact task, where the hand-grip is held waiting for the force feedback perception given by the contact with virtual objects. Experiment II is an active contact task, where a tool is moved in a direction until the contact perception with a pliable object. Different stiffnesses and refresh rates are factorially manipulated. To evaluate differences in the two tasks, we account for latency time inside the wall, penetration depth, and maximum force exerted against the object surface. The overall result of these experiments shows an improved sensitivity in almost all variables considered with refresh rates of 500 and 1,000 Hz compared with a refresh rate of 250 Hz, but no improved sensitivity is showed among them

On the adoption of e-moped sharing systems

AbstractRecent years have witnessed the emerging of novel shared mobility solutions that provide diffused on-demand access to transportation. The widespread adoption of these solutions, particularly electric mopeds (e-mopeds), is expected to bring important benefits such as the reduction of noise and atmospheric pollution, and road congestion, with extensive repercussions on liveability and quality of life in urban areas. Currently, almost no effort has been devoted to exploring the adoption patterns of e-moped sharing services, therefore, optimal management and allocation of vehicles appears to be a problem for service managers. In this study, we tried to demonstrate the validity of the hypothesis that the adoption of electric mopeds depends on the built environment and demographic aspects of each neighbourhood. In detail, we singled out three features concerning the area characteristics (distance from centre, walkability, concentration of places) and one about the population (education index). The results obtained on a real world case study show the strong impact these factors have in determining the adoption of e-moped sharing services. Finally, an analysis was conducted on the possible role that the electric moped sharing can play in social equalization by studying the interactions between rich and poor neighbourhoods. The results of the analyses conducted indicate that communities within a city tend to aggregate by wealth and isolate themselves from one another (social isolation): very few interactions, in terms of trajectories, have been observed between the richest and poorest areas of the city under study

Dynamic Movement Primitives: Volumetric Obstacle Avoidance Using Dynamic Potential Functions

Obstacle avoidance for DMPs is still a challenging problem. In our previous work, we proposed a framework for obstacle avoidance based on superquadric potential functions to represent volumes. In this work, we extend our previous work to include the velocity of the trajectory in the definition of the potential. Our formulations guarantee smoother behavior with respect to state-of-the-art point-like methods. Moreover, our new formulation allows to obtain a smoother behavior in proximity of the obstacle than when using a static (i.e. velocity independent) potential. We validate our framework for obstacle avoidance in a simulated multi-robot scenario and with different real robots: a pick-and-place task for an industrial manipulator and a surgical robot to show scalability; and navigation with a mobile robot in dynamic environment.Comment: Preprint for Journal of Intelligent and Robotic System

Autonomous task planning and situation awareness in robotic surgery

The use of robots in minimally invasive surgery has improved the quality of standard surgical procedures. So far, only the automation of simple surgical actions has been investigated by researchers, while the execution of structured tasks requiring reasoning on the environment and the choice among multiple actions is still managed by human surgeons. In this paper, we propose a framework to implement surgical task automation. The framework consists of a task-level reasoning module based on answer set programming, a low-level motion planning module based on dynamic movement primitives, and a situation awareness module. The logic-based reasoning module generates explainable plans and is able to recover from failure conditions, which are identified and explained by the situation awareness module interfacing to a human supervisor, for enhanced safety. Dynamic Movement Primitives allow to replicate the dexterity of surgeons and to adapt to obstacles and changes in the environment. The framework is validated on different versions of the standard surgical training peg-and-ring task.Comment: Submitted to IROS 2020 conferenc