Recovering from External Disturbances in Online Manipulation through State-Dependent Revertive Recovery Policies

Robots are increasingly entering uncertain and unstructured environments. Within these, robots are bound to face unexpected external disturbances like accidental human or tool collisions. Robots must develop the capacity to respond to unexpected events. That is not only identifying the sudden anomaly, but also deciding how to handle it. In this work, we contribute a recovery policy that allows a robot to recovery from various anomalous scenarios across different tasks and conditions in a consistent and robust fashion. The system organizes tasks as a sequence of nodes composed of internal modules such as motion generation and introspection. When an introspection module flags an anomaly, the recovery strategy is triggered and reverts the task execution by selecting a target node as a function of a state dependency chart. The new skill allows the robot to overcome the effects of the external disturbance and conclude the task. Our system recovers from accidental human and tool collisions in a number of tasks. Of particular importance is the fact that we test the robustness of the recovery system by triggering anomalies at each node in the task graph showing robust recovery everywhere in the task. We also trigger multiple and repeated anomalies at each of the nodes of the task showing that the recovery system can consistently recover anywhere in the presence of strong and pervasive anomalous conditions. Robust recovery systems will be key enablers for long-term autonomy in robot systems. Supplemental info including code, data, graphs, and result analysis can be found at [1].Comment: 8 pages, 8 figures, 1 tabl

Biomass-modulated fire dynamics during the last glacial-interglacial transition at the central pyrenees (Spain)

Understanding long-term fire ecology is essential for current day interpretation of ecosystem fire responses. However palaeoecology of fire is still poorly understood, especially at high-altitude mountain environments, despite the fact that these are fire-sensitive ecosystems and their resilience might be affected by changing fire regimes. We reconstruct wildfire occurrence since the Lateglacial (14.7. cal. ka BP) to the Mid-Holocene (6. cal. ka BP) and investigate the climate-fuel-fire relationships in a sedimentary sequence located at the treeline in the Central Spanish Pyrenees. Pollen, macro- and micro-charcoal were analysed for the identification of fire events (FE) in order to detect vegetation post-fire response and to define biomass-fire interactions. mean fire intervals (mfi) reduced since the Lateglacial, peaking at 9-7.7. cal. ka BP while from 7.7 to 6. cal. ka BP no fire is recorded. We hypothesise that Early Holocene maximum summer insolation, as climate forcing, and mesophyte forest expansion, as a fuel-creating factor, were responsible for accelerating fire occurrence in the Central Pyrenees treeline. We also found that fire had long-lasting negative effects on most of the treeline plant communities and that forest contraction from 7.7. cal. ka BP is likely linked to the ecosystem's threshold response to high fire frequencies.This research has been funded by the projects DINAMO (CGL2009-07992) (funding EGPF — grant ref. BES-2010-038593 and MSC), DINAMO2 (CGL2012-33063), ARAFIRE (2012 GA LC 064), GRACCIE-CONSOLIDER (CSD2007-00067). GGR was funded by the Juan de la Cierva Program (grant ref. JCI2009-04345) and JAE-Doc CSIC Program, LLM was supported by a postdoctoral MINT fellowship funded by the Institute for the Environment (Brunel University), AMC is a Ramón y Cajal fellow (ref: RYC-2008-02431), APS holds a grant funded by the Aragon Government (ref. 17030G/5423/480072/14003) and JAE holds a grant funded by the Basque Country Government (BFI-2010-5)

Unified and Distributed QoS-Driven Cell Association Algorithms in Heterogeneous Networks

This paper addresses the cell association problem in the downlink of a multi-tier heterogeneous network (HetNet), where base stations (BSs) have finite number of resource blocks (RBs) available to distribute among their associated users. Two problems are defined and treated in this paper: sum utility of long term rate maximization with long term rate quality of service (QoS) constraints, and global outage probability minimization with outage QoS constraints. The first problem is well-suited for low mobility environments, while the second problem provides a framework to deal with environments with fast fading. The defined optimization problems in this paper are solved in two phases: cell association phase followed by the optional RB distribution phase. We show that the cell association phase of both problems have the same structure. Based on this similarity, we propose a unified distributed algorithm with low levels of message passing to for the cell association phase. This distributed algorithm is derived by relaxing the association constraints and using Lagrange dual decomposition method. In the RB distribution phase, the remaining RBs after the cell association phase are distributed among the users. Simulation results show the superiority of our distributed cell association scheme compared to schemes that are based on maximum signal to interference plus noise ratio (SINR)

MaMiC: Macro and Micro Curriculum for Robotic Reinforcement Learning

Shaping in humans and animals has been shown to be a powerful tool for learning complex tasks as compared to learning in a randomized fashion. This makes the problem less complex and enables one to solve the easier sub task at hand first. Generating a curriculum for such guided learning involves subjecting the agent to easier goals first, and then gradually increasing their difficulty. This paper takes a similar direction and proposes a dual curriculum scheme for solving robotic manipulation tasks with sparse rewards, called MaMiC. It includes a macro curriculum scheme which divides the task into multiple sub-tasks followed by a micro curriculum scheme which enables the agent to learn between such discovered sub-tasks. We show how combining macro and micro curriculum strategies help in overcoming major exploratory constraints considered in robot manipulation tasks without having to engineer any complex rewards. We also illustrate the meaning of the individual curricula and how they can be used independently based on the task. The performance of such a dual curriculum scheme is analyzed on the Fetch environments.Comment: To appear in the Proceedings of the 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019). (Extended Abstract

Managing Service-Heterogeneity using Osmotic Computing

Computational resource provisioning that is closer to a user is becoming increasingly important, with a rise in the number of devices making continuous service requests and with the significant recent take up of latency-sensitive applications, such as streaming and real-time data processing. Fog computing provides a solution to such types of applications by bridging the gap between the user and public/private cloud infrastructure via the inclusion of a "fog" layer. Such approach is capable of reducing the overall processing latency, but the issues of redundancy, cost-effectiveness in utilizing such computing infrastructure and handling services on the basis of a difference in their characteristics remain. This difference in characteristics of services because of variations in the requirement of computational resources and processes is termed as service heterogeneity. A potential solution to these issues is the use of Osmotic Computing -- a recently introduced paradigm that allows division of services on the basis of their resource usage, based on parameters such as energy, load, processing time on a data center vs. a network edge resource. Service provisioning can then be divided across different layers of a computational infrastructure, from edge devices, in-transit nodes, and a data center, and supported through an Osmotic software layer. In this paper, a fitness-based Osmosis algorithm is proposed to provide support for osmotic computing by making more effective use of existing Fog server resources. The proposed approach is capable of efficiently distributing and allocating services by following the principle of osmosis. The results are presented using numerical simulations demonstrating gains in terms of lower allocation time and a higher probability of services being handled with high resource utilization.Comment: 7 pages, 4 Figures, International Conference on Communication, Management and Information Technology (ICCMIT 2017), At Warsaw, Poland, 3-5 April 2017, http://www.iccmit.net/ (Best Paper Award