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