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- All Subjects: artificial intelligence
Description
As intelligent agents become pervasive in our lives, they are expected to not only achieve tasks alone but also engage in tasks with humans in the loop. In such cases, the human naturally forms an understanding of the agent, which affects his perception of the agent’s behavior. However, such an understanding inevitably deviates from the ground truth due to reasons such as the human’s lack of understanding of the domain or misunderstanding of the agent’s capabilities. Such differences would result in an unmatched expectation of the agent’s behavior with the agent’s optimal behavior, thereby biasing the human’s assessment of the agent’s performance. In this dissertation, I focus on when these differences are due to a biased belief about domain dynamics. I especially investigate the impact of such a biased belief on the agent’s decision-making process in two different problem settings from a learning perspective. In the first setting, the agent is tasked to accomplish a task alone but must infer the human’s objectives from the human’s feedback on the agent’s behavior in the environment. In such a case, the human biased feedback could mislead the agent to learn a reward function that results in a sub-optimal and, potentially, undesired policy. In the second setting, the agent must accomplish a task with a human observer. Given that the agent’s optimal behavior may not match the human’s expectation due to the biased belief, the agent’s optimal behavior may be viewed as inexplicable, leading to degraded performance and loss of trust. Consequently, this dissertation proposes approaches that (1) endow the agent with the ability to be aware of the human’s biased belief while inferring the human’s objectives, thereby (2) neutralize the impact of the model differences in a reinforcement learning framework, and (3) behave explicably by reconciling the human’s expectation and optimality during decision-making.
ContributorsGong, Ze (Author) / Zhang, Yu (Thesis advisor) / Amor, Hani Ben (Committee member) / Kambhampati, Subbarao (Committee member) / Zhang, Wenlong (Committee member) / Arizona State University (Publisher)
Created2022
Description
The ubiquity of single camera systems in society has made improving monocular depth estimation a topic of increasing interest in the broader computer vision community. Inspired by recent work in sparse-to-dense depth estimation, this thesis focuses on sparse patterns generated from feature detection based algorithms as opposed to regular grid sparse patterns used by previous work. This work focuses on using these feature-based sparse patterns to generate additional depth information by interpolating regions between clusters of samples that are in close proximity to each other. These interpolated sparse depths are used to enforce additional constraints on the network’s predictions. In addition to the improved depth prediction performance observed from incorporating the sparse sample information in the network compared to pure RGB-based methods, the experiments show that actively retraining a network on a small number of samples that deviate most from the interpolated sparse depths leads to better depth prediction overall.
This thesis also introduces a new metric, titled Edge, to quantify model performance in regions of an image that show the highest change in ground truth depth values along either the x-axis or the y-axis. Existing metrics in depth estimation like Root Mean Square Error(RMSE) and Mean Absolute Error(MAE) quantify model performance across the entire image and don’t focus on specific regions of an image that are hard to predict. To this end, the proposed Edge metric focuses specifically on these hard to classify regions. The experiments also show that using the Edge metric as a small addition to existing loss functions like L1 loss in current state-of-the-art methods leads to vastly improved performance in these hard to classify regions, while also improving performance across the board in every other metric.
This thesis also introduces a new metric, titled Edge, to quantify model performance in regions of an image that show the highest change in ground truth depth values along either the x-axis or the y-axis. Existing metrics in depth estimation like Root Mean Square Error(RMSE) and Mean Absolute Error(MAE) quantify model performance across the entire image and don’t focus on specific regions of an image that are hard to predict. To this end, the proposed Edge metric focuses specifically on these hard to classify regions. The experiments also show that using the Edge metric as a small addition to existing loss functions like L1 loss in current state-of-the-art methods leads to vastly improved performance in these hard to classify regions, while also improving performance across the board in every other metric.
ContributorsRai, Anshul (Author) / Yang, Yezhou (Thesis advisor) / Zhang, Wenlong (Committee member) / Liang, Jianming (Committee member) / Arizona State University (Publisher)
Created2019
Description
Self-Driving cars are a long-lasting ambition for many AI scientists and engineers. In the last decade alone, many self-driving cars like Google Waymo, Tesla Autopilot, Uber, etc. have been roaming the streets of many cities. As a rapidly expanding field, researchers all over the world are attempting to develop more safe and efficient AI agents that can navigate through our cities. However, driving is a very complex task to master even for a human, let alone the challenges in developing robots to do the same. It requires attention and inputs from the surroundings of the car, and it is nearly impossible for us to program all the possible factors affecting this complex task. As a solution, imitation learning was introduced, wherein the agents learn a policy, mapping the observations to the actions through demonstrations given by humans. Through imitation learning, one could easily teach self-driving cars the expected behavior in many scenarios. Despite their autonomous nature, it is undeniable that humans play a vital role in the development and execution of safe and trustworthy self-driving cars and hence form the strongest link in this application of Human-Robot Interaction. Several approaches were taken to incorporate this link between humans and self-driving cars, one of which involves the communication of human's navigational instruction to self-driving cars. The communicative channel provides humans with control over the agent’s decisions as well as the ability to guide them in real-time. In this work, the abilities of imitation learning in creating a self-driving agent that can follow natural language instructions given by humans based on environmental objects’ descriptions were explored. The proposed model architecture is capable of handling latent temporal context in these instructions thus making the agent capable of taking multiple decisions along its course. The work shows promising results that push the boundaries of natural language instructions and their complexities in navigating self-driving cars through towns.
ContributorsMoudhgalya, Nithish B (Author) / Amor, Hani Ben (Thesis advisor) / Baral, Chitta (Committee member) / Yang, Yezhou (Committee member) / Zhang, Wenlong (Committee member) / Arizona State University (Publisher)
Created2021