Artificial Cognition for Social Human-Robot Interaction: An Implementation

© 2017 The Authors Human–Robot Interaction challenges Artificial Intelligence in many regards: dynamic, partially unknown environments that were not originally designed for robots; a broad variety of situations with rich semantics to understand and interpret; physical interactions with humans that requires fine, low-latency yet socially acceptable control strategies; natural and multi-modal communication which mandates common-sense knowledge and the representation of possibly divergent mental models. This article is an attempt to characterise these challenges and to exhibit a set of key decisional issues that need to be addressed for a cognitive robot to successfully share space and tasks with a human. We identify first the needed individual and collaborative cognitive skills: geometric reasoning and situation assessment based on perspective-taking and affordance analysis; acquisition and representation of knowledge models for multiple agents (humans and robots, with their specificities); situated, natural and multi-modal dialogue; human-aware task planning; human–robot joint task achievement. The article discusses each of these abilities, presents working implementations, and shows how they combine in a coherent and original deliberative architecture for human–robot interaction. Supported by experimental results, we eventually show how explicit knowledge management, both symbolic and geometric, proves to be instrumental to richer and more natural human–robot interactions by pushing for pervasive, human-level semantics within the robot's deliberative system

Human and robot beliefs management and architecture for collaborative task planning and control

Ce travail de thèse a eu pour objectif de définir et mettre en oeuvre l'architecture décisionnelle d'un robot réalisant une tâche en collaboration avec un homme pour atteindre un but commun. Un certain nombre de fonctionnalités existaient déjà ou ont été développées conjointement avec ce travail au sein de l'équipe. Ce travail a d'abord consisté en l'étude puis à la formalisation des différentes capacités nécessaires. Il s'est traduit concrètement par l'approfondissement de certains des modules fonctionnels existants par l'auteur ou par d'autres membres de l'équipe en lien étroit avec l'auteur. La première contribution principale de l'auteur a consisté à développer une couche de contrôle de haut niveau qui a permis l'intégration et la mise en oeuvre des différentes capacités du robot découpée en 3 activités : la construction et la mise à jour de l'état du monde ; la gestion des buts et des plans de haut niveau ; l'exécution et le suivi des mouvements de manipulation. La deuxième contribution principale a consisté à améliorer les raisonnements géométriques et temporels pour d'abord permettre au robot de mieux suivre l'évolution de l'état du monde puis lui donner la capacité à inférer quand l'homme a des croyances distinctes de celles du robot.Goal of this thesis was to formalize and to implement a decisional layer for a robot achieving tasks collaboratively with a human to achieve a shared goal. Some functionalities were already there or were built during my own thesis by other inside the team. My first task was to study and formalize the skills needed by the robot. Consequently some of the existing functional modules were improved by some other members of the team or myself. My first main contribution was to develop a new high level control component to integrate and manage the different robot skills according to 3 main activities : state of the world build and update; goals and plans management; manipulation motions execution and monitoring. My second main contribution was to improve geometric and temporal reasoning skills so that first, the robot could better understand and track changes in the world and second, infer when the human had some beliefs about the world that were distinct from its own beliefs

When the robot puts itself in your shoes. Managing and exploiting human and robot beliefs

International audienceWe have designed and implemented new spatio-temporal reasoning skills for a cognitive robot, which explicitly reasons about human beliefs on object positions. It enables the robot to build symbolic models reflecting each agent's perspective on the world. Using these models, the robot has a better understanding of what humans say and do, and is able to reason on what human should know to achieve a given goal. These new capabilities are also demonstrated experimentally

Human-Robot Interaction: Tackling the AI Challenges

Human-Robot interaction is an area full of challenges for artificial intelligence: dynamic, partially unknown environments that are not originally designed for autonomous machines; a large variety of situations and objects to deal with, with possibly complex semantics; physical interactions with humans that requires fine, low-latency control, representation and management of several mental models, pertinent situation assessment skills...the list goes on. This article sheds light on some key decisional issues that are to be tackled for a cognitive robot to share space and tasks with a human, and present our take on these challenges. We adopt a constructive approach based on the identification and the effective implementation of individual and collaborative skills. These cognitive abilities cover geometric reasoning and situation assessment mainly based on perspective-taking and affordances, management and exploitation of each agent (human and robot) knowledge in separate cognitive models, natural multi-modal communication, "human-aware" task planning, and human and robot interleaved plan achievement. We present our design choices, the articulations between the diverse deliberative components of the robot, experimental results, and eventually discuss the strengths and weaknesses of our approach. It appears that explicit knowledge management, both symbolic and geometric, proves to be key as it pushes for a different, more semantic way to address the decision-making issue in human-robot interactions

When the robot considers the human

This paper addresses some key decisional issues that are necessary for a cognitive robot which shares space and tasks with a human. We adopt a constructive approach based on the identification and the effective implementation of individual and collaborative skills. The system is comprehensive since it aims at dealing with a complete set of abilities articulated so that the robot controller is effectively able to conduct in a flexible manner a collaborative task with a human partner. These abilities include geometric reasoning and situation assessment based essentially on perspective-taking and affordances, management and exploitation of each agent (hu-man and robot) knowledge in a separate cognitive model, human-aware task planning and human and robot interleaved plan achievement

Targeting chemoresistant senescent pancreatic cancer cells improves conventional treatment efficacy

Abstract Pancreatic cancer is one of the deadliest cancers owing to its late diagnosis and of the strong resistance to available treatments. Despite a better understanding of the disease in the last two decades, no significant improvement in patient care has been made. Senescent cells are characterized by a stable proliferation arrest and some resistance to cell death. Increasing evidence suggests that multiple lines of antitumor therapy can induce a senescent-like phenotype in cancer cells, which may participate in treatment resistance. In this study, we describe that gemcitabine, a clinically-used drug against pancreatic cancer, induces a senescent-like phenotype in highly chemoresistant pancreatic cancer cells in vitro and in xenografted tumors in vivo. The use of ABT-263, a well-described senolytic compound targeting Bcl2 anti-apoptotic proteins, killed pancreatic gemcitabine-treated senescent-like cancer cells in vitro. In vivo, the combination of gemcitabine and ABT-263 decreased tumor growth, whereas their individual administration had no effect. Together these data highlight the possibility of improving the efficacy of conventional chemotherapies against pancreatic cancer by eliminating senescent-like cancer cells through senolytic intervention. Further studies testing different senolytics or their combination with available treatments will be necessary to optimize preclinical data in mouse models before transferring these findings to clinical trials