Towards Rapid Multi-robot Learning from Demonstration at the RoboCup Competition

Abstract. We describe our previous and current efforts towards achiev-ing an unusual personal RoboCup goal: to train a full team of robots directly through demonstration, on the field of play at the RoboCup venue, how to collaboratively play soccer, and then use this trained team in the competition itself. Using our method, HiTAB, we can train teams of collaborative agents via demonstration to perform nontrivial joint behaviors in the form of hierarchical finite-state automata. We discuss HiTAB, our previous efforts in using it in RoboCup 2011 and 2012, recent experimental work, and our current efforts for 2014, then suggest a new RoboCup Technical Challenge problem in learning from demonstration. Imagine that you are at an unfamiliar disaster site with a team of robots, and are faced with a previously unseen task for them to do. The robots have only rudimentary but useful utility behaviors implemented. You are not a programmer. Without coding them, you have only a few hours to get your robots doing useful collaborative work in this new environment. How would you do this

Inflammatory mediators in breast cancer: Coordinated expression of TNFα & IL-1β with CCL2 & CCL5 and effects on epithelial-to-mesenchymal transition

<p>Abstract</p> <p>Background</p> <p>The inflammatory chemokines CCL2 (MCP-1) & CCL5 (RANTES) and the inflammatory cytokines TNFα & IL-1β were shown to contribute to breast cancer development and metastasis. In this study, we wished to determine whether there are associations between these factors along stages of breast cancer progression, and to identify the possible implications of these factors to disease course.</p> <p>Methods</p> <p>The expression of CCL2, CCL5, TNFα and IL-1β was determined by immunohistochemistry in patients diagnosed with: (1) Benign breast disorders (=healthy individuals); (2) Ductal Carcinoma <it>In Situ </it>(DCIS); (3) Invasive Ducal Carcinoma without relapse (IDC-no-relapse); (4) IDC-with-relapse. Based on the results obtained, breast tumor cells were stimulated by the inflammatory cytokines, and epithelial-to-mesenchymal transition (EMT) was determined by flow cytometry, confocal analyses and adhesion, migration and invasion experiments.</p> <p>Results</p> <p>CCL2, CCL5, TNFα and IL-1β were expressed at very low incidence in normal breast epithelial cells, but their incidence was significantly elevated in tumor cells of the three groups of cancer patients. Significant associations were found between CCL2 & CCL5 and TNFα & IL-1β in the tumor cells in DCIS and IDC-no-relapse patients. In the IDC-with-relapse group, the expression of CCL2 & CCL5 was accompanied by further elevated incidence of TNFα & IL-1β expression. These results suggest progression-related roles for TNFα and IL-1β in breast cancer, as indeed indicated by the following: (1) Tumors of the IDC-with-relapse group had significantly higher persistence of TNFα and IL-1β compared to tumors of DCIS or IDC-no-relapse; (2) Continuous stimulation of the tumor cells by TNFα (and to some extent IL-1β) has led to EMT in the tumor cells; (3) Combined analyses with relevant clinical parameters suggested that IL-1β acts jointly with other pro-malignancy factors to promote disease relapse.</p> <p>Conclusions</p> <p>Our findings suggest that the coordinated expression of CCL2 & CCL5 and TNFα & IL-1β may be important for disease course, and that TNFα & IL-1β may promote disease relapse. Further <it>in vitro </it>and <it>in vivo </it>studies are needed for determination of the joint powers of the four factors in breast cancer, as well as analyses of their combined targeting in breast cancer.</p

Adapting hippocampus multi-scale place field distributions in cluttered environments optimizes spatial navigation and learning

Extensive studies in rodents show that place cells in the hippocampus have firing patterns that are highly correlated with the animal's location in the environment and are organized in layers of increasing field sizes or scales along its dorsoventral axis. In this study, we use a spatial cognition model to show that different field sizes could be exploited to adapt the place cell representation to different environments according to their size and complexity. Specifically, we provide an in-depth analysis of how to distribute place cell fields according to the obstacles in cluttered environments to optimize learning time and path optimality during goal-oriented spatial navigation tasks. The analysis uses a reinforcement learning (RL) model that assumes that place cells allow encoding the state. While previous studies have suggested exploiting different field sizes to represent areas requiring different spatial resolutions, our work analyzes specific distributions that adapt the representation to the environment, activating larger fields in open areas and smaller fields near goals and subgoals (e.g., obstacle corners). In addition to assessing how the multi-scale representation may be exploited in spatial navigation tasks, our analysis and results suggest place cell representations that can impact the robotics field by reducing the total number of cells for path planning without compromising the quality of the paths learned. Copyright © 2022 Scleidorovich, Fellous and Weitzenfeld.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]