Glucose Availability and AMP-Activated Protein Kinase Link Energy Metabolism and Innate Immunity in the Bovine Endometrium

Defences against the bacteria that usually infect the endometrium of postpartum cattle are impaired when there is metabolic energy stress, leading to endometritis and infertility. The endometrial response to bacteria depends on innate immunity, with recognition of pathogen-associated molecular patterns stimulating inflammation, characterised by secretion of interleukin (IL)-1β, IL-6 and IL-8. How metabolic stress impacts tissue responses to pathogens is unclear, but integration of energy metabolism and innate immunity means that stressing one system might affect the other. Here we tested the hypothesis that homeostatic pathways integrate energy metabolism and innate immunity in bovine endometrial tissue. Glucose deprivation reduced the secretion of IL-1β, IL-6 and IL-8 from ex vivo organ cultures of bovine endometrium challenged with the pathogen-associated molecular patterns lipopolysaccharide and bacterial lipopeptide. Endometrial inflammatory responses to lipopolysaccharide were also reduced by small molecules that activate or inhibit the intracellular sensor of energy, AMP-activated protein kinase (AMPK). However, inhibition of mammalian target of rapamycin, which is a more global metabolic sensor than AMPK, had little effect on inflammation. Similarly, endometrial inflammatory responses to lipopolysaccharide were not affected by insulin-like growth factor-1, which is an endocrine regulator of metabolism. Interestingly, the inflammatory responses to lipopolysaccharide increased endometrial glucose consumption and induced the Warburg effect, which could exacerbate deficits in glucose availability in the tissue. In conclusion, metabolic energy stress perturbed inflammatory responses to pathogen-associated molecular patterns in bovine endometrial tissue, and the most fundamental regulators of cellular energy, glucose availability and AMPK, had the greatest impact on innate immunity

RobotFusion: Grasping with a robotic manipulator via multi-view reconstruction

We propose a complete system for 3D object reconstruction and grasping based on an articulated robotic manipulator. We deploy an RGB-D sensor as an end effector placed directly on the robotic arm, and process the acquired data to perform multi-view 3D reconstruction and object grasping. We leverage the high repeatability of the robotic arm to estimate 3D camera poses with millimeter accuracy and control each of the six sensor\u2019s DOF in a dexterous workspace. Thereby, we can estimate camera poses directly by robot kinematics and deploy a Truncated Signed Distance Function (TSDF) to accurately fuse multiple views into a unified 3D reconstruction of the scene. Then, we propose an efficient approach to segment the sought objects out of a planar workbench as well as a novel algorithm to automatically estimate grasping points

Towards a Task-Aware Proactive Sociable Robot Based on Multi-state Perspective-Taking

International audienceRobots are expected to cooperate with humans in day-to-day interaction. One aspect of such cooperation is behaving proactively. In this paper we will enable our robots, equipped with visuo-spatial perspective-taking capabilities, to behave proactively based on reasoning ‘where’ its human partner might perform a particular task with different effort levels. For this, the robot analyzes the agents’ abilities not only from the current state but also from a set of different states the agent might attain.Depending on the task and the situation, the robot exhibits different types of proactive behaviors, such as, reaching out, suggesting a solution and providing clues by head movement, for two different tasks performed by the human partner: give and make accessible. These proactive behaviors are intended to be informative to reduce confusion of the human partner, to communicate the robot’s ability and intention and to guide the partner for better cooperation.We have validated the behaviors by user studies, which suggest that such proactive behaviors reduce the ‘confusion’ and ‘effort’ of the users. Further, the participants reported the robot to be more ‘supportive and aware’ compared to the situations where the robot was non-proactive.Such proactive behaviors could enrich multi-modal interaction and cooperation capabilities of the robot as well as help in developing more complex socially expected and accepted behaviors in the human centered environment

Towards Human-Level Semantics Understanding of Human-Centered Object Manipulation Tasks for HRI: Reasoning About Effect, Ability, Effort and Perspective Taking

International audienceIn its lifetime, a robot should be able to autonomously understand the semantics of different tasks to effectively perform them in different situations. In this context, it is important to distinguish the meaning (in terms of the desired effect) of a task and the means to achieve that task. Our focus is those tasks in which one agent is required to perform a task for another agent, such as give, show, hide, make-accessible, etc. In this paper, we identify that a high-level human-centered combined reasoning, based on perspective taking, efforts and abilities analyses, is the key to understand semantics of such tasks. By combining these aspects, the robot infers sets of hierarchy of facts, which serve for analyzing the effect of a task. We adapt the explanation based learning approach enabling the task understanding from the very first demonstration and continuous refinement with new demonstrations. We argue that such symbolic level understanding of a task, which is not bound to trajectory, kinematics structure or shape of the robot, facilitates generalization to novel situations as well as ease the transfer of acquired knowledge among heterogeneous robots. Further, the knowledge of tasks at such human understandable level of abstraction will enrich the natural human–robot interaction

Appropriate Feedback in Asymmetric Interactions

Wrede B, Kopp S, Rohlfing K, Lohse M, Muhl C. Appropriate Feedback in Asymmetric Interactions. Journal of Pragmatics. 2010;42(9):2369-2384.Based on the observation that human-robot interaction is often laborious because the robot's interactional abilities fail to meet the user's expectations, we argue that feedback can play a central role in regulating expectations and mitigating unnecessary disruptions in the flow of conversation. For feedback to be appropriate in this sense, it needs to take situational information into account. This idea stems from interviews with persons with hearing and mental impairments who display perceptual limitations similar to a robot. The results of these interviews indicated that, depending on the goals of the situation, people with hearing impairments used either mediation (clarification) or concealment strategies to keep the interaction going. With this idea in mind, we analyzed human-robot interactions in two different situations - more task-oriented interactions versus more socially driven interactions - and we observed different feedback behaviors in users and their reactions to the robot's behavior. We use these results to derive a scaffold for modeling appropriate feedback in asymmetric interactions (i.e., in human-robot interactions) and briefly discuss some consequences for both the design of human-robot interaction and for theories of grounding. (c) 2010 Elsevier B.V. All rights reserved