Theses and dissertations

This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students. 

Displaying 1 - 3 of 3
Filtering by

Clear all filters

157016-Thumbnail Image.png

Foundations of Human-Aware Planning -- A Tale of Three Models

Description
A critical challenge in the design of AI systems that operate with humans in the loop is to be able to model the intentions and capabilities of the humans, as well as their beliefs and expectations of the AI system itself. This allows the AI system to be "human- aware"…

A critical challenge in the design of AI systems that operate with humans in the loop is to be able to model the intentions and capabilities of the humans, as well as their beliefs and expectations of the AI system itself. This allows the AI system to be "human- aware" -- i.e. the human task model enables it to envisage desired roles of the human in joint action, while the human mental model allows it to anticipate how its own actions are perceived from the point of view of the human. In my research, I explore how these concepts of human-awareness manifest themselves in the scope of planning or sequential decision making with humans in the loop. To this end, I will show (1) how the AI agent can leverage the human task model to generate symbiotic behavior; and (2) how the introduction of the human mental model in the deliberative process of the AI agent allows it to generate explanations for a plan or resort to explicable plans when explanations are not desired. The latter is in addition to traditional notions of human-aware planning which typically use the human task model alone and thus enables a new suite of capabilities of a human-aware AI agent. Finally, I will explore how the AI agent can leverage emerging mixed-reality interfaces to realize effective channels of communication with the human in the loop.
ContributorsChakraborti, Tathagata (Author) / Kambhampati, Subbarao (Thesis advisor) / Talamadupula, Kartik (Committee member) / Scheutz, Matthias (Committee member) / Ben Amor, Hani (Committee member) / Zhang, Yu (Committee member) / Arizona State University (Publisher)
Created2018
172013-Thumbnail Image.png

Dynamic Potential Fields for Flexible Behavior-based Swarm Control via Reinforcement Learning

Description
In this thesis work, a novel learning approach to solving the problem of controllinga quadcopter (drone) swarm is explored. To deal with large sizes, swarm control is often achieved in a distributed fashion by combining different behaviors such that each behavior implements some desired swarm characteristics, such as avoiding ob- stacles and staying…

In this thesis work, a novel learning approach to solving the problem of controllinga quadcopter (drone) swarm is explored. To deal with large sizes, swarm control is often achieved in a distributed fashion by combining different behaviors such that each behavior implements some desired swarm characteristics, such as avoiding ob- stacles and staying close to neighbors. One common approach in distributed swarm control uses potential fields. A limitation of this approach is that the potential fields often depend statically on a set of control parameters that are manually specified a priori. This paper introduces Dynamic Potential Fields for flexible swarm control. These potential fields are modulated by a set of dynamic control parameters (DCPs) that can change under different environment situations. Since the focus is only on these DCPs, it simplifies the learning problem and makes it feasible for practical use. This approach uses soft actor critic (SAC) where the actor only determines how to modify DCPs in the current situation, resulting in more flexible swarm control. In the results, this work will show that the DCP approach allows for the drones to bet- ter traverse environments with obstacles compared to several state-of-the-art swarm control methods with a fixed set of control parameters. This approach also obtained a higher safety score commonly used to assess swarm behavior. A basic reinforce- ment learning approach is compared to demonstrate faster convergence. Finally, an ablation study is conducted to validate the design of this approach.
ContributorsFerraro, Calvin Shores (Author) / Zhang, Yu (Thesis advisor) / Ben Amor, Hani (Committee member) / Berman, Spring (Committee member) / Arizona State University (Publisher)
Created2022
161731-Thumbnail Image.png

Robotic Swarm Control using Deep Reinforcement Learning Strategies based on Mean-Field Models

Description
As technological advancements in silicon, sensors, and actuation continue, the development of robotic swarms is shifting from the domain of science fiction to reality. Many swarm applications, such as environmental monitoring, precision agriculture, disaster response, and lunar prospecting, will require controlling numerous robots with limited capabilities and information to redistribute…

As technological advancements in silicon, sensors, and actuation continue, the development of robotic swarms is shifting from the domain of science fiction to reality. Many swarm applications, such as environmental monitoring, precision agriculture, disaster response, and lunar prospecting, will require controlling numerous robots with limited capabilities and information to redistribute among multiple states, such as spatial locations or tasks. A scalable control approach is to program the robots with stochastic control policies such that the robot population in each state evolves according to a mean-field model, which is independent of the number and identities of the robots. Using this model, the control policies can be designed to stabilize the swarm to the target distribution. To avoid the need to reprogram the robots for different target distributions, the robot control policies can be defined to depend only on the presence of a “leader” agent, whose control policy is designed to guide the swarm to a particular distribution. This dissertation presents a novel deep reinforcement learning (deep RL) approach to designing control policies that redistribute a swarm as quickly as possible over a strongly connected graph, according to a mean-field model in the form of the discrete-time Kolmogorov forward equation. In the leader-based strategies, the leader determines its next action based on its observations of robot populations and shepherds the swarm over the graph by probabilistically repelling nearby robots. The scalability of this approach with the swarm size is demonstrated with leader control policies that are designed using two tabular Temporal-Difference learning algorithms, trained on a discretization of the swarm distribution. To improve the scalability of the approach with robot population and graph size, control policies for both leader-based and leaderless strategies are designed using an actor-critic deep RL method that is trained on the swarm distribution predicted by the mean-field model. In the leaderless strategy, the robots’ control policies depend only on their local measurements of nearby robot populations. The control approaches are validated for different graph and swarm sizes in numerical simulations, 3D robot simulations, and experiments on a multi-robot testbed.
ContributorsKakish, Zahi Mousa (Author) / Berman, Spring (Thesis advisor) / Yong, Sze Zheng (Committee member) / Marvi, Hamid (Committee member) / Pavlic, Theodore (Committee member) / Pratt, Stephen (Committee member) / Ben Amor, Hani (Committee member) / Arizona State University (Publisher)
Created2021