692 research outputs found
Fourth Amendment - Search and Seizure - Law Enforcement Officer May, in the Exercise of Routine Procedure, Command Passenger of Lawfully Detained Automobile to Exit Vehicle - Maryland v. Wilson, 117 S. Ct. 882 (1997).
Sculptural Motion
http://deepblue.lib.umich.edu/bitstream/2027.42/83779/1/cadop_1303270866.pd
Universal Design Manikin: Integrative Simulation and Visualization Techniques
This research demonstrates methods for integrating simulation and visualization techniques with the current tools used in design work-flows. The techniques are applied to human factors with a concentration on disabilities. A tool named Universal Design Manikin is developed. The tool integrates a virtual manikin and wheelchair with a coresponding graphical user interface. The research covers factors from a human scale of reach abilitiy to a large scale of building navigation. The research presents an opportunity for seamless collaboration between scientists and designers by integrating joint analysis tools with design tools. Methods for simulation and visualization of reach, vision, navigation, and spatial zones are presented.Master of ScienceArchitecture and Urban PlanningUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/95705/1/Schwartz_Masters.pd
Torque-based Deep Reinforcement Learning for Task-and-Robot Agnostic Learning on Bipedal Robots Using Sim-to-Real Transfer
In this paper, we review the question of which action space is best suited
for controlling a real biped robot in combination with Sim2Real training.
Position control has been popular as it has been shown to be more sample
efficient and intuitive to combine with other planning algorithms. However, for
position control gain tuning is required to achieve the best possible policy
performance. We show that instead, using a torque-based action space enables
task-and-robot agnostic learning with less parameter tuning and mitigates the
sim-to-reality gap by taking advantage of torque control's inherent compliance.
Also, we accelerate the torque-based-policy training process by pre-training
the policy to remain upright by compensating for gravity. The paper showcases
the first successful sim-to-real transfer of a torque-based deep reinforcement
learning policy on a real human-sized biped robot. The video is available at
https://youtu.be/CR6pTS39VRE
Optimizing Indoor Navigation Policies For Spatial Distancing
In this paper, we focus on the modification of policies that can lead to
movement patterns and directional guidance of occupants, which are represented
as agents in a 3D simulation engine. We demonstrate an optimization method that
improves a spatial distancing metric by modifying the navigation graph by
introducing a measure of spatial distancing of agents as a function of agent
density (i.e., occupancy). Our optimization framework utilizes such metrics as
the target function, using a hybrid approach of combining genetic algorithm and
simulated annealing. We show that within our framework, the
simulation-optimization process can help to improve spatial distancing between
agents by optimizing the navigation policies for a given indoor environment.Comment: 9 pages, 8 figures, conference-- simulation in architecture and urban
design, in-cooperation with ACM SIGSI
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