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 - 7 of 7
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- All Subjects: robotics
- Creators: Ben Amor, Hani
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" -- 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
Description
For those interested in the field of robotics, there are not many options to get your hands on a physical robot without paying a steep price. This is why the folks at BCN3D Technologies decided to design a fully open-source 3D-printable robotic arm. Their goal was to reduce the barrier to entry for the field of robotics and make it exponentially more accessible for people around the world. For our honors thesis, we chose to take the design from BCN3D and attempt to build their robot, to see how accessible the design truly is. Although their designs were not perfect and we were forced to make some adjustments to the 3D files, overall the work put forth by the people at BCN3D was extremely useful in successfully building a robotic arm that is programmed with ease.
ContributorsCohn, Riley (Co-author) / Petty, Charles (Co-author) / Ben Amor, Hani (Thesis director) / Yong, Sze Zheng (Committee member) / Computer Science and Engineering Program (Contributor) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
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 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
Description
Automated driving systems (ADS) have come a long way since their inception. It is clear that these systems rely heavily on stochastic deep learning techniques for perception, planning, and prediction, as it is impossible to construct every possible driving scenario to generate driving policies. Moreover, these systems need to be trained and validated extensively on typical and abnormal driving situations before they can be trusted with human life. However, most publicly available driving datasets only consist of typical driving behaviors. On the other hand, there is a plethora of videos available on the internet that capture abnormal driving scenarios, but they are unusable for ADS training or testing as they lack important information such as camera calibration parameters, and annotated vehicle trajectories. This thesis proposes a new toolbox, DeepCrashTest-V2, that is capable of reconstructing high-quality simulations from monocular dashcam videos found on the internet. The toolbox not only estimates the crucial parameters such as camera calibration, ego-motion, and surrounding road user trajectories but also creates a virtual world in Car Learning to Act (CARLA) using data from OpenStreetMaps to simulate the estimated trajectories. The toolbox is open-source and is made available in the form of a python package on GitHub at https://github.com/C-Aniruddh/deepcrashtest_v2.
ContributorsChandratre, Aniruddh Vinay (Author) / Fainekos, Georgios (Thesis advisor) / Ben Amor, Hani (Thesis advisor) / Pedrielli, Giulia (Committee member) / Arizona State University (Publisher)
Created2022
Description
Multiple robotic arms collaboration is to control multiple robotic arms to collaborate with each other to work on the same task. During the collaboration, theagent is required to avoid all possible collisions between each part of the robotic arms.
Thus, incentivizing collaboration and preventing collisions are the two principles which
are followed by the agent during the training process. Nowadays, more and more
applications, both in industry and daily lives, require at least two arms, instead of
requiring only a single arm. A dual-arm robot satisfies much more needs of different
types of tasks, such as folding clothes at home, making a hamburger in a grill or
picking and placing a product in a warehouse.
The applications done in this paper are all about object pushing. This thesis
focuses on how to train the agent to learn pushing an object away as far as possible.
Reinforcement Learning (RL), which is a type of Machine Learning (ML), is then
utilized in this paper to train the agent to generate optimal actions. Deep Deterministic
Policy Gradient (DDPG) and Hindsight Experience Replay (HER) are the two RL
methods used in this thesis.
ContributorsLin, Steve (Author) / Ben Amor, Hani (Thesis advisor) / Redkar, Sangram (Committee member) / Zhang, Yu (Committee member) / Arizona State University (Publisher)
Created2023
Description
The goal of reinforcement learning is to enable systems to autonomously solve tasks in the real world, even in the absence of prior data. To succeed in such situations, reinforcement learning algorithms collect new experience through interactions with the environment to further the learning process. The behaviour is optimized by maximizing a reward function, which assigns high numerical values to desired behaviours. Especially in robotics, such interactions with the environment are expensive in terms of the required execution time, human involvement, and mechanical degradation of the system itself. Therefore, this thesis aims to introduce sample-efficient reinforcement learning methods which are applicable to real-world settings and control tasks such as bimanual manipulation and locomotion. Sample efficiency is achieved through directed exploration, either by using dimensionality reduction or trajectory optimization methods. Finally, it is demonstrated how data-efficient reinforcement learning methods can be used to optimize the behaviour and morphology of robots at the same time.
ContributorsLuck, Kevin Sebastian (Author) / Ben Amor, Hani (Thesis advisor) / Aukes, Daniel (Committee member) / Fainekos, Georgios (Committee member) / Scholz, Jonathan (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2019
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 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