Learning Temporal Dynamics of Human-Robot Interactions from Demonstrations

The presence of robots in society is becoming increasingly common, triggering the need to learn reliable policies to automate human-robot interactions (HRI). Manually developing policies for HRI is particularly challenging due to the complexity introduced by the human component. The aim of this thesis is to explore the benefits of leveraging temporal reasoning to learn policies for HRIs from demonstrations. This thesis proposes and evaluates two distinct temporal reasoning approaches. The first one consists of a temporal-reasoning-based learning from demonstration (TR-LfD) framework that employs a variant of an Interval Temporal Bayesian Network to learn the temporal dynamics of an interaction. TR-LfD exploits Allen’s interval algebra (IA) and Bayesian networks to effectively learn complex temporal structures. The second approach consists of a novel temporal reasoning model, the Temporal Context Graph (TCG). TCGs combine IA, n-grams models, and directed graphs to model interactions with cyclical atomic actions and temporal structures with sequential and parallel relationships. The proposed temporal reasoning models are evaluated using two experiments consisting of autonomous robot-mediated behavioral interventions. Results indicate that leveraging temporal reasoning can improve policy generation and execution in LfD frameworks. Specifically, these models can be used to limit the action space of a robot during an interaction, thus simplifying policy selection and effectively addressing the issue of perceptual aliasing

Appearance and Geometry Assisted Visual Navigation in Urban Areas

Navigation is a fundamental task for mobile robots in applications such as exploration, surveillance, and search and rescue. The task involves solving the simultaneous localization and mapping (SLAM) problem, where a map of the environment is constructed. In order for this map to be useful for a given application, a suitable scene representation needs to be defined that allows spatial information sharing between robots and also between humans and robots. High-level scene representations have the benefit of being more robust and having higher exchangeability for interpretation. With the aim of higher level scene representation, in this work we explore high-level landmarks and their usage using geometric and appearance information to assist mobile robot navigation in urban areas. In visual SLAM, image registration is a key problem. While feature-based methods such as scale-invariant feature transform (SIFT) matching are popular, they do not utilize appearance information as a whole and will suffer from low-resolution images. We study appearance-based methods and propose a scale-space integrated Lucas-Kanade’s method that can estimate geometric transformations and also take into account image appearance with different resolutions. We compare our method against state-of-the-art methods and show that our method can register images efficiently with high accuracy. In urban areas, planar building facades (PBFs) are basic components of the quasirectilinear environment. Hence, segmentation and mapping of PBFs can increase a robot’s abilities of scene understanding and localization. We propose a vision-based PBF segmentation and mapping technique that combines both appearance and geometric constraints to segment out planar regions. Then, geometric constraints such as reprojection errors, orientation constraints, and coplanarity constraints are used in an optimization process to improve the mapping of PBFs. A major issue in monocular visual SLAM is scale drift. While depth sensors, such as lidar, are free from scale drift, this type of sensors are usually more expensive compared to cameras. To enable low-cost mobile robots equipped with monocular cameras to obtain accurate position information, we use a 2D lidar map to rectify imprecise visual SLAM results using planar structures. We propose a two-step optimization approach assisted by a penalty function to improve on low-quality local minima results. Robot paths for navigation can be either automatically generated by a motion planning algorithm or provided by a human. In both cases, a scene representation of the environment, i.e., a map, is useful to specify meaningful tasks for the robot. However, SLAM results usually produce a sparse scene representation that consists of low-level landmarks, such as point clouds, which are neither convenient nor intuitive to use for task specification. We present a system that allows users to program mobile robots using high-level landmarks from appearance data

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

Learning to sequence movement primitives from demonstrations

Abstract—We present an approach for learning sequential robot skills through kinesthetic teaching. The demonstrations are represented by a sequence graph. Finding the transitions between consecutive basic movements is treated as classification problem where both Support Vector Machines and Gaussian Mixture Models are evaluated as classifiers. We show how the observed primitive order of all demonstrations can help to improve the movement reproduction by restricting the classification outcome to the currently executed primitive and its possible successors in the graph. The approach is validated with an experiment in which a 7-DOF Barrett WAM robot learns to unscrew a light bulb. I

Learning to sequence movement primitives from demonstrations

Abstract—We present an approach for learning sequential robot skills through kinesthetic teaching. The demonstrations are represented by a sequence graph. Finding the transitions between consecutive basic movements is treated as classification problem where both Support Vector Machines and Gaussian Mixture Models are evaluated as classifiers. We show how the observed primitive order of all demonstrations can help to improve the movement reproduction by restricting the classification outcome to the currently executed primitive and its possible successors in the graph. The approach is validated with an experiment in which a 7-DOF Barrett WAM robot learns to unscrew a light bulb. I