Conditional Task and Motion Planning through an Effort-based Approach

This paper proposes a preliminary work on a Conditional Task and Motion Planning algorithm able to find a plan that minimizes robot efforts while solving assigned tasks. Unlike most of the existing approaches that replan a path only when it becomes unfeasible (e.g., no collision-free paths exist), the proposed algorithm takes into consideration a replanning procedure whenever an effort-saving is possible. The effort is here considered as the execution time, but it is extensible to the robot energy consumption. The computed plan is both conditional and dynamically adaptable to the unexpected environmental changes. Based on the theoretical analysis of the algorithm, authors expect their proposal to be complete and scalable. In progress experiments aim to prove this investigation

Chemotactic Cues for NOTCH1-Dependent Leukemia

The NOTCH signaling pathway is a conserved signaling cascade that regulates many aspects of development and homeostasis in multiple organ systems. Aberrant activity of this signaling pathway is linked to the initiation and progression of several hematological malignancies, exemplified by T-cell acute lymphoblastic leukemia (T-ALL). Interestingly, frequent non-mutational activation of NOTCH1 signaling has recently been demonstrated in B-cell chronic lymphocytic leukemia (B-CLL), significantly extending the pathogenic significance of this pathway in B-CLL. Leukemia patients often present with high-blood cell counts, diffuse disease with infiltration of the bone marrow, secondary lymphoid organs, and diffusion to the central nervous system (CNS). Chemokines are chemotactic cytokines that regulate migration of cells between tissues and the positioning and interactions of cells within tissue. Homeostatic chemokines and their receptors have been implicated in regulating organ-specific infiltration, but may also directly and indirectly modulate tumor growth. Recently, oncogenic NOTCH1 has been shown to regulate infiltration of leukemic cells into the CNS hijacking the CC-chemokine ligand 19/CC-chemokine receptor 7 chemokine axis. In addition, a crucial role for the homing receptor axis CXC-chemokine ligand 12/CXC-chemokine receptor 4 has been demonstrated in leukemia maintenance and progression. Moreover, the CCL25/CCR9 axis has been implicated in the homing of leukemic cells into the gut, particularly in the presence of phosphatase and tensin homolog tumor suppressor loss. In this review, we summarize the latest developments regarding the role of NOTCH signaling in regulating the chemotactic microenvironmental cues involved in the generation and progression of T-ALL and compare these findings to B-CLL

Teaching humanoid robotics by means of human teleoperation through RGB-D sensors

This paper presents a graduate course project on humanoid robotics offered by the University of Padova. The target is to safely lift an object by teleoperating a small humanoid. Students have to map human limbs into robot joints, guarantee the robot stability during the motion, and teleoperate the robot to perform the correct movement. We introduce the following innovative aspects with respect to classical robotic classes: i) the use of humanoid robots as teaching tools; ii) the simplification of the stable locomotion problem by exploiting the potential of teleoperation; iii) the adoption of a Project-Based Learning constructivist approach as teaching methodology. The learning objectives of both course and project are introduced and compared with the students\u2019 background. Design and constraints students have to deal with are reported, together with the amount of time they and their instructors dedicated to solve tasks. A set of evaluation results are provided in order to validate the authors\u2019 purpose, including the students\u2019 personal feedback. A discussion about possible future improvements is reported, hoping to encourage further spread of educational robotics in schools at all levels

The ALICE Inner Tracking System

The design characteristics of the ALICE inner tracking system are presented together with the performances measured in beam tests and expected from Monte Carlo simulations

Cognitive Task Planning for Smart Industrial Robots

This research work presents a novel Cognitive Task Planning framework for Smart Industrial Robots. The framework makes an industrial mobile manipulator robot Cognitive by applying Semantic Web Technologies. It also introduces a novel Navigation Among Movable Obstacles algorithm for robots navigating and manipulating inside a firm. The objective of Industrie 4.0 is the creation of Smart Factories: modular firms provided with cyber-physical systems able to strong customize products under the condition of highly flexible mass-production. Such systems should real-time communicate and cooperate with each other and with humans via the Internet of Things. They should intelligently adapt to the changing surroundings and autonomously navigate inside a firm while moving obstacles that occlude free paths, even if seen for the first time. At the end, in order to accomplish all these tasks while being efficient, they should learn from their actions and from that of other agents. Most of existing industrial mobile robots navigate along pre-generated trajectories. They follow ectrified wires embedded in the ground or lines painted on th efloor. When there is no expectation of environment changes and cycle times are critical, this planning is functional. When workspaces and tasks change frequently, it is better to plan dynamically: robots should autonomously navigate without relying on modifications of their environments. Consider the human behavior: humans reason about the environment and consider the possibility of moving obstacles if a certain goal cannot be reached or if moving objects may significantly shorten the path to it. This problem is named Navigation Among Movable Obstacles and is mostly known in rescue robotics. This work transposes the problem on an industrial scenario and tries to deal with its two challenges: the high dimensionality of the state space and the treatment of uncertainty. The proposed NAMO algorithm aims to focus exploration on less explored areas. For this reason it extends the Kinodynamic Motion Planning by Interior-Exterior Cell Exploration algorithm. The extension does not impose obstacles avoidance: it assigns an importance to each cell by combining the efforts necessary to reach it and that needed to free it from obstacles. The obtained algorithm is scalable because of its independence from the size of the map and from the number, shape, and pose of obstacles. It does not impose restrictions on actions to be performed: the robot can both push and grasp every object. Currently, the algorithm assumes full world knowledge but the environment is reconfigurable and the algorithm can be easily extended in order to solve NAMO problems in unknown environments. The algorithm handles sensor feedbacks and corrects uncertainties. Usually Robotics separates Motion Planning and Manipulation problems. NAMO forces their combined processing by introducing the need of manipulating multiple objects, often unknown, while navigating. Adopting standard precomputed grasps is not sufficient to deal with the big amount of existing different objects. A Semantic Knowledge Framework is proposed in support of the proposed algorithm by giving robots the ability to learn to manipulate objects and disseminate the information gained during the fulfillment of tasks. The Framework is composed by an Ontology and an Engine. The Ontology extends the IEEE Standard Ontologies for Robotics and Automation and contains descriptions of learned manipulation tasks and detected objects. It is accessible from any robot connected to the Cloud. It can be considered a data store for the efficient and reliable execution of repetitive tasks; and a Web-based repository for the exchange of information between robots and for the speed up of the learning phase. No other manipulation ontology exists respecting the IEEE Standard and, regardless the standard, the proposed ontology differs from the existing ones because of the type of features saved and the efficient way in which they can be accessed: through a super fast Cascade Hashing algorithm. The Engine lets compute and store the manipulation actions when not present in the Ontology. It is based on Reinforcement Learning techniques that avoid massive trainings on large-scale databases and favors human-robot interactions. The overall system is flexible and easily adaptable to different robots operating in different industrial environments. It is characterized by a modular structure where each software block is completely reusable. Every block is based on the open-source Robot Operating System. Not all industrial robot controllers are designed to be ROS-compliant. This thesis presents the method adopted during this research in order to Open Industrial Robot Controllers and create a ROS-Industrial interface for them

Micro/Nano manufacturing

Micro- and nano-scale manufacturing has been the subject of an increasing amount of interest and research effort worldwide in both academia and industry over the past 10 years.[...

The experience of inhabiting Interactive Virtual Spaces

The purpose of this project is to propose a reflection about the characteristics that interactive virtual spaces or interface-spaces must have, to be considered inhabiting places. This reflection is based on the analysis, design, construction and experimentation of different interface-spaces that respond to this premise, which are developed for different users-inhabitants in different contexts, and it will enable to elaborate design and evaluation guidelines based on orientation, accessibility, performativity, heuristics and usability criteria; related to the ways of interaction and communication that the virtual environment proposes, and the hypermedia language and narrative, under an open access-open code perspective.Facultad de Arquitectura, Diseño y Urbanismo. Universidad Nacional del Litora

Proprioceptive Robot Collision Detection through Gaussian Process Regression

This paper proposes a proprioceptive collision detection algorithm based on Gaussian Regression. Compared to sensor-based collision detection and other proprioceptive algorithms, the proposed approach has minimal sensing requirements, since only the currents and the joint configurations are needed. The algorithm extends the standard Gaussian Process models adopted in learning the robot inverse dynamics, using a more rich set of input locations and an ad-hoc kernel structure to model the complex and non-linear behaviors due to frictions in quasi-static configurations. Tests performed on a Universal Robots UR10 show the effectiveness of the proposed algorithm to detect when a collision has occurred.Comment: Published at ACC 201