10,443 research outputs found
Task planning using physics-based heuristics on manipulation actions
© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In order to solve mobile manipulation problems, the efficient combination of task and motion planning is usually required. Moreover, the incorporation of physics-based information has recently been taken into account in order to plan the tasks in a more realistic way. In the present paper, a task and motion planning framework is proposed based on a modified version of the Fast-Forward task planner that is guided by physics-based knowledge.
The proposal uses manipulation knowledge for reasoning on symbolic literals (both in offline and online modes) taking into account geometric information in order to evaluate the applicability as well as feasibility of actions while evaluating the heuristic cost. It results in an efficient search of the state space and in the obtention of low-cost physically-feasible plans. The proposal has been implemented and is illustrated with a manipulation problem consisting of a mobile robot and some fixed and manipulatable objects.Peer ReviewedPostprint (author's final draft
A Maintenance Planning Framework using Online and Offline Deep Reinforcement Learning
Cost-effective asset management is an area of interest across several
industries. Specifically, this paper develops a deep reinforcement learning
(DRL) solution to automatically determine an optimal rehabilitation policy for
continuously deteriorating water pipes. We approach the problem of
rehabilitation planning in an online and offline DRL setting. In online DRL,
the agent interacts with a simulated environment of multiple pipes with
distinct lengths, materials, and failure rate characteristics. We train the
agent using deep Q-learning (DQN) to learn an optimal policy with minimal
average costs and reduced failure probability. In offline learning, the agent
uses static data, e.g., DQN replay data, to learn an optimal policy via a
conservative Q-learning algorithm without further interactions with the
environment. We demonstrate that DRL-based policies improve over standard
preventive, corrective, and greedy planning alternatives. Additionally,
learning from the fixed DQN replay dataset in an offline setting further
improves the performance. The results warrant that the existing deterioration
profiles of water pipes consisting of large and diverse states and action
trajectories provide a valuable avenue to learn rehabilitation policies in the
offline setting, which can be further fine-tuned using the simulator.Comment: Published Neural Comput & Applic (2023), 12 pages, 8 Figur
Regional Data Archiving and Management for Northeast Illinois
This project studies the feasibility and implementation options for establishing a regional data archiving system to help monitor
and manage traffic operations and planning for the northeastern Illinois region. It aims to provide a clear guidance to the
regional transportation agencies, from both technical and business perspectives, about building such a comprehensive
transportation information system. Several implementation alternatives are identified and analyzed. This research is carried
out in three phases.
In the first phase, existing documents related to ITS deployments in the broader Chicago area are summarized, and a
thorough review is conducted of similar systems across the country. Various stakeholders are interviewed to collect
information on all data elements that they store, including the format, system, and granularity. Their perception of a data
archive system, such as potential benefits and costs, is also surveyed. In the second phase, a conceptual design of the
database is developed. This conceptual design includes system architecture, functional modules, user interfaces, and
examples of usage. In the last phase, the possible business models for the archive system to sustain itself are reviewed. We
estimate initial capital and recurring operational/maintenance costs for the system based on realistic information on the
hardware, software, labor, and resource requirements. We also identify possible revenue opportunities.
A few implementation options for the archive system are summarized in this report; namely:
1. System hosted by a partnering agency
2. System contracted to a university
3. System contracted to a national laboratory
4. System outsourced to a service provider
The costs, advantages and disadvantages for each of these recommended options are also provided.ICT-R27-22published or submitted for publicationis peer reviewe
Bridging the Innovation Divide: An Agenda for Disseminating Technology Innovations within the Nonprofit Sector
Examines technology practices -- such as neighborhood information systems, electronic advocacy, Internet-based micro enterprise support, and digital inclusion initiatives -- that strengthen the capacity of nonprofits and community organizations
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