Behaviour-driven motion synthesis

Heightened demand for alternatives to human exposure to strenuous and repetitive labour, as well as to hazardous environments, has led to an increased interest in real-world deployment of robotic agents. Targeted applications require robots to be adept at synthesising complex motions rapidly across a wide range of tasks and environments. To this end, this thesis proposes leveraging abstractions of the problem at hand to ease and speed up the solving. We formalise abstractions to hint relevant robotic behaviour to a family of planning problems, and integrate them tightly into the motion synthesis process to make real-world deployment in complex environments practical. We investigate three principal challenges of this proposition. Firstly, we argue that behavioural samples in form of trajectories are of particular interest to guide robotic motion synthesis. We formalise a framework with behavioural semantic annotation that enables the storage and bootstrap of sets of problem-relevant trajectories. Secondly, in the core of this thesis, we study strategies to exploit behavioural samples in task instantiations that differ significantly from those stored in the framework. We present two novel strategies to efficiently leverage offline-computed problem behavioural samples: (i) online modulation based on geometry-tuned potential fields, and (ii) experience-guided exploration based on trajectory segmentation and malleability. Thirdly, we demonstrate that behavioural hints can be extracted on-the-fly to tackle highlyconstrained, ever-changing complex problems, from which there is no prior knowledge. We propose a multi-layer planner that first solves a simplified version of the problem at hand, to then inform the search for a solution in the constrained space. Our contributions on efficient motion synthesis via behaviour guidance augment the robots’ capabilities to deal with more complex planning problems, and do so more effectively than related approaches in the literature by computing better quality paths in lower response time. We demonstrate our contributions, in both laboratory experiments and field trials, on a spectrum of planning problems and robotic platforms ranging from high-dimensional humanoids and robotic arms with a focus on autonomous manipulation in resembling environments, to high-dimensional kinematic motion planning with a focus on autonomous safe navigation in unknown environments. While this thesis was motivated by challenges on motion synthesis, we have explored the applicability of our findings on disparate robotic fields, such as grasp and task planning. We have made some of our contributions open-source hoping they will be of use to the robotics community at large.The CDT in Robotics and Autonomous Systems at Heriot-Watt University and The University of EdinburghThe ORCA Hub EPSRC project (EP/R026173/1)The Scottish Informatics and Computer Science Alliance (SICSA

Incident Traffic Management Respone

The North Carolina State Highway Patrol (NCSHP) and the North Carolina Department of Transportation (NCDOT) are often called upon to assist in traffic incidents. Yet little systematic research has examined the extent to which these two agencies collaborate. This gap in understanding is problematic, as a lack of collaboration may result in significant delays in the clearing of traffic incidents. The purpose of this correlational study was to investigate circumstances when the two agencies collaborated in clearing major traffic incidents, and the efficiency of the clearance of the incidents, through the measurement of normal traffic flow. The theory of the convergence of resources from divergent organizations framed the study. The research questions addressed the extent of collaboration between the NCSHP and the NCDOT, the conditions under which this collaboration took place, and the efficiency of the clearance of these incidents. Data were obtained from the NCSHP and the NCDOT on characteristics of 1,580 traffic incidents that occurred on the North Carolina portion of Interstate 95 during the year 2014. The data were analyzed using chi-square tests, analyses of variance, and Z-tests for proportions. Collaboration between the two agencies occurred in 7.2% of all of the incidents and in 21.6% of incidents of major severity (p \u3c .001), which indicated a low level of interagency collaboration. The mean clearance time for incidents in which collaboration took place was 115.92 minutes compared to a national goal of 90 minutes. It is hoped that these results can contribute to policy dialogue relevant to the state\u27s Strategic Plan, leading to safer highways and less financial loss due to congestion caused by traffic incidents

LIPIcs, Volume 258, SoCG 2023, Complete Volume

LIPIcs, Volume 258, SoCG 2023, Complete Volum

Eye on Collaborative Creativity : Insights From Multiple-Person Mobile Gaze Tracking in the Context of Collaborative Design

Early Career WorkshopNon peer reviewe

LIPIcs, Volume 244, ESA 2022, Complete Volume

LIPIcs, Volume 244, ESA 2022, Complete Volum

Democratizing the Data Stream: Creating an Equitable Transfer of Research Vessel Data from Scientist to Student

With the emergence of big data and the Open Data Movement, and the wide availability to the public of large databases, Data Literacy is a necessary learning goal for students. Understanding the data process in its entirety is now a vital skillset required across industry, government, and scientific disciplines. The newest ships in the U.S. Academic Research Fleet, the Regional Class Research Vessels (RCRVs), are being built with the aim to support data literacy through a forthcoming real-time data portal that is intended to foster outreach and engagement. Research for understanding how the new RCRVs may support data literacy occurred in two phases. The first phase research investigated the transfer of real-time oceanographic data from researcher to K-12 classrooms and “The Data Stream” was identified. The second phase research, explained here, expanded upon the first phase through interviewing experts in the field of data literacy and shipboard education. In addition, specialists in diversity, equity, and inclusion in the geosciences were interviewed. The objective was to determine promising practices in data literacy education and shipboard outreach that are also culturally responsive. The expert interviews illuminated numerous educational strategies which may facilitate building a student’s data literacy. One prominent strategy is student-driven community action research, in which students collect and evaluate data to create local change. An eight-week afterschool program, Mar Adentro, was developed where students could examine the presence of microplastics in their local watershed. The pilot program was tested with seven students from Oregon’s Latina/o community. Students ultimately emerged from the program with a deeper understanding of the data process. The program also demonstrated the value of providing second-language students informal learning spaces where they can comfortably utilize linguistic capital and engage with one another in their first language

Learning Probabilistic Generative Models For Fast Sampling-Based Planning

Due to their simplicity and efficiency in high dimensional space, sampling-based motion planners have been gaining interest for robotic manipulation in recent years. We present several new learning approaches using probabilistic generative models for fast sampling-based planning. First, we propose fast collision detection in high dimensional configuration spaces based on Gaussian Mixture Models (GMMs) for Rapidly-exploring Random Trees (RRT). In addition, we introduce a new probabilistically safe local steering primitive based on the probabilistic model. Our local steering procedure is based on a new notion of a convex probabilistically safety corridor that is constructed around a configuration using tangent hyperplanes of confidence ellipsoids of GMMs learned from prior collision history. For efficient sampling, we suggest a sampling method with a learned Q-function with linear function approximation based on feature representations such as Radial Basis Functions. This sampling method chooses the optimal node from which to extend the search tree via the softmax function of learned state values. We also discuss a novel constrained sampling-based motion planning method for grasp and transport tasks with redundant robotic manipulators, which allows the best grasp configuration and approach direction to be automatically determined. Since these approaches with the learned probabilistic models require large size data and time for training, it is essential that they are able to be adapted to environmental change in an online manner. The suggested online learning approach with the Dirichlet Process Mixture Model (DPMM) can adapt the complexity to the data and learn new Gaussian clusters with streaming data in newly explored areas without batch learning. We have applied these approaches in a number of robot arm planning scenarios and have shown their utility and effectiveness in simulation and on a physical 7-DoF robot manipulator

LIPIcs, Volume 274, ESA 2023, Complete Volume

LIPIcs, Volume 274, ESA 2023, Complete Volum