767 research outputs found
DOP: Deep Optimistic Planning with Approximate Value Function Evaluation
Research on reinforcement learning has demonstrated promising results in manifold applications and domains. Still, efficiently learning effective robot behaviors is very difficult, due to unstructured scenarios, high uncertainties, and large state dimensionality (e.g. multi-agent systems or hyper-redundant robots). To alleviate this problem, we present DOP, a deep model-based reinforcement learning algorithm, which exploits action values to both (1) guide the exploration of the state space and (2) plan effective policies. Specifically, we exploit deep neural networks to learn Q-functions that are used to attack the curse of dimensionality during a Monte-Carlo tree search. Our algorithm, in fact, constructs upper confidence bounds on the learned value function to select actions optimistically. We implement and evaluate DOP on different scenarios: (1) a cooperative navigation problem, (2) a fetching task for a 7-DOF KUKA robot, and (3) a human-robot handover with a humanoid robot (both in simulation and real). The obtained results show the effectiveness of DOP in the chosen applications, where action values drive the exploration and reduce the computational demand of the planning process while achieving good performance
Q-CP: Learning Action Values for Cooperative Planning
Research on multi-robot systems has demonstrated promising results in manifold applications and domains. Still, efficiently learning an effective robot behaviors is very difficult, due to unstructured scenarios, high uncertainties, and large state dimensionality (e.g. hyper-redundant and groups of robot). To alleviate this problem, we present Q-CP a cooperative model-based reinforcement learning algorithm, which exploits action values to both (1) guide the exploration of the state space and (2) generate effective policies. Specifically, we exploit Q-learning to attack the curse-of-dimensionality in the iterations of a Monte-Carlo Tree Search. We implement and evaluate Q-CP on different stochastic cooperative (general-sum) games: (1) a simple cooperative navigation problem among 3 robots, (2) a cooperation scenario between a pair of KUKA YouBots performing hand-overs, and (3) a coordination task between two mobile robots entering a door. The obtained results show the effectiveness of Q-CP in the chosen applications, where action values drive the exploration and reduce the computational demand of the planning process while achieving good performance
S-AVE Semantic Active Vision Exploration and Mapping of Indoor Environments for Mobile Robots
Semantic mapping is fundamental to enable cognition and high-level planning in robotics. It is a difficult task due to generalization to different scenarios and sensory data types. Hence, most techniques do not obtain a rich and accurate semantic map of the environment and of the objects therein. To tackle this issue we present a novel approach that exploits active vision and drives environment exploration aiming at improving the quality of the semantic map
Teachers’ Professional Development on Media and Intercultural Education. Results from some participatory research in Europe
Media and intercultural education are being increasingly recognised as a fundamental competence for teachers of the 21st century. Digital literacy and civic competence are facing several new challenges in response to the intensification of migratory phenomena and the unprecedented spread of fake news, especially among adolescents at risk of social exclusion, but teachers’ professional development is still far from coping with this emerging need. Intercultural understanding and a critical use of media among adolescents have now become primary goals for the promotion of active citizenship. This article intends to provide some recommendations on how to support teachers’ professional development in the field of media and intercultural education. To this purpose, it presents and discusses the results of an action-research project aimed at teachers’ improvement of teaching skills about the media in multicultural public schools. The results are part of a larger European project “Media Education for Equity and Tolerance” (MEET) (Erasmus Plus, KA3), an initiative promoted in 2016–2018 by the University of Florence (Italy)
DOP: Deep Optimistic Planning with Approximate Value Function Evaluation
Research on reinforcement learning has demonstrated promising results in
manifold applications and domains. Still, efficiently learning effective robot
behaviors is very difficult, due to unstructured scenarios, high uncertainties,
and large state dimensionality (e.g. multi-agent systems or hyper-redundant
robots). To alleviate this problem, we present DOP, a deep model-based
reinforcement learning algorithm, which exploits action values to both (1)
guide the exploration of the state space and (2) plan effective policies.
Specifically, we exploit deep neural networks to learn Q-functions that are
used to attack the curse of dimensionality during a Monte-Carlo tree search.
Our algorithm, in fact, constructs upper confidence bounds on the learned value
function to select actions optimistically. We implement and evaluate DOP on
different scenarios: (1) a cooperative navigation problem, (2) a fetching task
for a 7-DOF KUKA robot, and (3) a human-robot handover with a humanoid robot
(both in simulation and real). The obtained results show the effectiveness of
DOP in the chosen applications, where action values drive the exploration and
reduce the computational demand of the planning process while achieving good
performance.Comment: to appear as an extended abstract paper in the Proc. of the 17th
International Conference on Autonomous Agents and Multiagent Systems (AAMAS
2018), Stockholm, Sweden, July 10-15, 2018, IFAAMAS. arXiv admin note: text
overlap with arXiv:1803.0029
Enhancing Graph Representation of the Environment through Local and Cloud Computation
Enriching the robot representation of the operational environment is a
challenging task that aims at bridging the gap between low-level sensor
readings and high-level semantic understanding. Having a rich representation
often requires computationally demanding architectures and pure point cloud
based detection systems that struggle when dealing with everyday objects that
have to be handled by the robot. To overcome these issues, we propose a
graph-based representation that addresses this gap by providing a semantic
representation of robot environments from multiple sources. In fact, to acquire
information from the environment, the framework combines classical computer
vision tools with modern computer vision cloud services, ensuring computational
feasibility on onboard hardware. By incorporating an ontology hierarchy with
over 800 object classes, the framework achieves cross-domain adaptability,
eliminating the need for environment-specific tools. The proposed approach
allows us to handle also small objects and integrate them into the semantic
representation of the environment. The approach is implemented in the Robot
Operating System (ROS) using the RViz visualizer for environment
representation. This work is a first step towards the development of a
general-purpose framework, to facilitate intuitive interaction and navigation
across different domains.Comment: 5 pages, 4 figure
The Oriental Fruit Fly, Bactrocera dorsalis, in China: Origin and Gradual Inland Range Expansion Associated with Population Growth
The oriental fruit fly, Bactrocera dorsalis, expanded throughout mainland China in the last century to become one of the most serious pests in the area, yet information on this process are fragmentary. Three mitochondrial genes (nad1, cytb and nad5) were used to infer the genetic diversity, population structure and demographic history of the oriental fruit fly from its entire distribution range in China. High levels of genetic diversity, as well as a significant correspondence between genetic and geographic distances, suggest that the invasion process might have been gradual, with no associated genetic bottlenecks. Three population groups could be identified, nevertheless the overall genetic structure was weak. The effective number of migrants between populations, estimated using the coalescent method, suggested asymmetric gene flow from the costal region of Guangdong to most inland regions. The demographic analysis indicates the oriental fruit fly underwent a recent population expansion in the Central China. We suggest the species originated in the costal region facing the South China Sea and gradually expanded to colonize mainland China, expanding here to high population numbers
- …