15,863 research outputs found
Learning While Doing in the Human Services Sector: Becoming a Learning Organization Through Organizational Change
Outlines Casey Family Services' model for organizational learning and managing change; the process and outcomes of shifting focus to securing permanent, loving families for children and youth and their timely exit from foster care; and recommendations
Two-echelon freight transport optimisation: unifying concepts via a systematic review
Multi-echelon distribution schemes are one of the most common strategies adopted by the transport companies in an aim of cost reduction, but their identification in scientific literature is not always easy due to a lack of unification. This paper presents the main concepts of two-echelon distribution via a systematic review, in the specific a meta-narrative analysis, in order to identify and unify the main concepts, issues and methods that can be helpful for scientists and transport practitioners. The problem of system cost optimisation in two-echelon freight transport systems is defined. Moreover, the main variants are synthetically presented and discussed. Finally, future research directions are proposed.location-routing problems, multi-echelon distribution, cross-docking, combinatorial optimisation, systematic review.
Post-Apartheid National Spatial Development Planning in South Africa - A Brief History
Since coming to power in 1994 successive ANC-governments have engaged in a series of attempts at national spatial development planning in South Africa. These engagements have received scant treatment in the planning literature. In this paper a broad overview of these initiatives is provided, with an emphasis on the different instruments; the context in which they were developed; the institutions that were proposed and/or created in support of the instruments; and the extent to which the instruments were implemented and what their levels of success were. The paper concludes with a call for comparative research, including South Africa, in this arena
Optimal task and motion planning and execution for human-robot multi-agent systems in dynamic environments
Combining symbolic and geometric reasoning in multi-agent systems is a
challenging task that involves planning, scheduling, and synchronization
problems. Existing works overlooked the variability of task duration and
geometric feasibility that is intrinsic to these systems because of the
interaction between agents and the environment. We propose a combined task and
motion planning approach to optimize sequencing, assignment, and execution of
tasks under temporal and spatial variability. The framework relies on
decoupling tasks and actions, where an action is one possible geometric
realization of a symbolic task. At the task level, timeline-based planning
deals with temporal constraints, duration variability, and synergic assignment
of tasks. At the action level, online motion planning plans for the actual
movements dealing with environmental changes. We demonstrate the approach
effectiveness in a collaborative manufacturing scenario, in which a robotic arm
and a human worker shall assemble a mosaic in the shortest time possible.
Compared with existing works, our approach applies to a broader range of
applications and reduces the execution time of the process.Comment: 12 pages, 6 figures, accepted for publication on IEEE Transactions on
Cybernetics in March 202
Refining 6-DoF Grasps with Context-Specific Classifiers
In this work, we present GraspFlow, a refinement approach for generating
context-specific grasps. We formulate the problem of grasp synthesis as a
sampling problem: we seek to sample from a context-conditioned probability
distribution of successful grasps. However, this target distribution is
unknown. As a solution, we devise a discriminator gradient-flow method to
evolve grasps obtained from a simpler distribution in a manner that mimics
sampling from the desired target distribution. Unlike existing approaches,
GraspFlow is modular, allowing grasps that satisfy multiple criteria to be
obtained simply by incorporating the relevant discriminators. It is also simple
to implement, requiring minimal code given existing auto-differentiation
libraries and suitable discriminators. Experiments show that GraspFlow
generates stable and executable grasps on a real-world Panda robot for a
diverse range of objects. In particular, in 60 trials on 20 different household
objects, the first attempted grasp was successful 94% of the time, and 100%
grasp success was achieved by the second grasp. Moreover, incorporating a
functional discriminator for robot-human handover improved the functional
aspect of the grasp by up to 33%.Comment: IROS 2023, Code and Datasets are available at
https://github.com/tasbolat1/graspflo
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