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- All Subjects: artificial intelligence
- Genre: Masters Thesis
- Creators: Srivastava, Siddharth
Description
Visual navigation is a useful and important task for a variety of applications. As the prevalence of robots increase, there is an increasing need for energy-efficient navigation methods as well. Many aspects of efficient visual navigation algorithms have been implemented in the literature, but there is a lack of work on evaluation of the efficiency of the image sensors. In this thesis, two methods are evaluated: adaptive image sensor quantization for traditional camera pipelines as well as new event-based sensors for low-power computer vision.The first contribution in this thesis is an evaluation of performing varying levels of sensor linear and logarithmic quantization with the task of visual simultaneous localization and mapping (SLAM). This unconventional method can provide efficiency benefits with a trade off between accuracy of the task and energy-efficiency. A new sensor quantization method, gradient-based quantization, is introduced to improve the accuracy of the task. This method only lowers the bit level of parts of the image that are less likely to be important in the SLAM algorithm since lower bit levels signify better energy-efficiency, but worse task accuracy. The third contribution is an evaluation of the efficiency and accuracy of event-based camera intensity representations for the task of optical flow. The results of performing a learning based optical flow are provided for each of five different reconstruction methods along with ablation studies. Lastly, the challenges of an event feature-based SLAM system are presented with results demonstrating the necessity for high quality and high resolution event data. The work in this thesis provides studies useful for examining tradeoffs for an efficient visual navigation system with traditional and event vision sensors. The results of this thesis also provide multiple directions for future work.
ContributorsChristie, Olivia Catherine (Author) / Jayasuriya, Suren (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2022
Description
While the growing prevalence of robots in industry and daily life necessitatesknowing how to operate them safely and effectively, the steep learning curve of
programming languages and formal AI education is a barrier for most beginner users.
This thesis presents an interactive platform which leverages a block based programming
interface with natural language instructions to teach robotics programming to novice
users. An integrated robot simulator allows users to view the execution of their
high-level plan, with the hierarchical low level planning abstracted away from them.
Users are provided human-understandable explanations of their planning failures and
hints using LLMs to enhance the learning process. The results obtained from a user
study conducted with students having minimal programming experience show that
JEDAI-Ed is successful in teaching robotic planning to users, as well as increasing
their curiosity about AI in general.
ContributorsDobhal, Daksh (Author) / Srivastava, Siddharth (Thesis advisor) / Gopalan, Nakul (Committee member) / Seifi, Hasti (Committee member) / Arizona State University (Publisher)
Created2024
Description
Detection of anomalies before they are included in the downstream diagnosis/prognosis models is an important criterion for maintaining the medical AI imaging model performance across internal and external datasets. Furthermore, the need to curate huge amounts of data to train supervised models that produce precise results also requires an automated model that can accurately identify in-distribution (ID) and out-of-distribution (OOD) data for ensuring the training dataset quality. However, the core challenges for designing such as system are – (i) given the infinite variations of the anomaly, curation of training data is in-feasible; (ii) making assumptions about the types of anomalies are often hypothetical. The proposed work designed an unsupervised anomaly detection model using a cascade variational autoencoder coupled with a zero-shot learning network that maps the latent vectors to semantic attributes. The performance of the proposed model is shown on two different use cases – skin images and chest radiographs and also compare against the same class of state-of-the-art generative OOD detection models.
ContributorsRamasamy, Gokul (Author) / Banerjee, Imon (Thesis advisor) / Sanyal, Arindam (Thesis advisor) / Jayasuriya, Suren (Committee member) / Arizona State University (Publisher)
Created2023
Description
Robot motion and control remains a complex problem both in general and inthe field of machine learning (ML). Without ML approaches, robot controllers are
typically designed manually, which can take considerable time, generally requiring
accounting for a range of edge cases and often producing models highly constrained
to specific tasks. ML can decrease the time it takes to create a model while simultaneously allowing it to operate on a broader range of tasks. The utilization of neural
networks to learn from demonstration is, in particular, an approach with growing
popularity due to its potential to quickly fit the parameters of a model to mimic
training data.
Many such neural networks, especially in the realm of transformer-based architectures, act more as planners, taking in an initial context and then generating a
sequence from that context one step at a time. Others hybridize the approach, predicting a latent plan and conditioning immediate actions on that plan. Such approaches may limit a model’s ability to interact with a dynamic environment, needing to replan to fully update its understanding of the environmental context. In this
thesis, Language-commanded Scene-aware Action Response (LanSAR) is proposed as
a reactive transformer-based neural network that makes immediate decisions based
on previous actions and environmental changes. Its actions are further conditioned
on a language command, serving as a control mechanism while also narrowing the
distribution of possible actions around this command. It is shown that LanSAR successfully learns a strong representation of multimodal visual and spatial input, and
learns reasonable motions in relation to most language commands. It is also shown
that LanSAR can struggle with both the accuracy of motions and understanding the
specific semantics of language commands
ContributorsHardy, Adam (Author) / Ben Amor, Heni (Thesis advisor) / Srivastava, Siddharth (Committee member) / Pavlic, Theodore (Committee member) / Arizona State University (Publisher)
Created2024
Description
Reinforcement Learning (RL) presents a diverse and expansive collection of approaches that enable systems to learn and adapt through interaction with their environments. However, the widespread deployment of RL in real-world applications is hindered by challenges related to sample efficiency and the interpretability of decision-making processes. This thesis addresses the critical challenges of sample efficiency and interpretability in reinforcement learning (RL), which are pivotal for advancing RL applications in complex, real-world scenarios.This work first presents a novel approach for learning dynamic abstract representations for continuous or parameterized state and action spaces. Empirical evaluations show that the proposed approach achieves a higher sample efficiency and beat state- of-the-art Deep-RL methods. Second, it presents a new approach HOPL for Transfer Reinforcement Learning (RL) for Stochastic Shortest Path (SSP) problems in factored domains with unknown transition functions. This approach continually learns transferable, generalizable knowledge in the form of symbolically represented options and integrates search techniques with RL to solve new problems by efficiently composing the learned options. The empirical results show that the approach achieves superior sample efficiency as compared to SOTA methods for transfering learned knowledge.
ContributorsVerma, Shivanshu (Author) / Srivastava, Siddharth (Thesis advisor) / Gopalan, Nakul (Committee member) / Choi, YooJung (Committee member) / Arizona State University (Publisher)
Created2024
Description
Reinforcement learning (RL) is a powerful methodology for teaching autonomous agents complex behaviors and skills. A critical component in most RL algorithms is the reward function -- a mathematical function that provides numerical estimates for desirable and undesirable states. Typically, the reward function must be hand-designed by a human expert and, as a result, the scope of a robot's autonomy and ability to safely explore and learn in new and unforeseen environments is constrained by the specifics of the designed reward function. In this thesis, I design and implement a stateful collision anticipation model with powerful predictive capability based upon my research of sequential data modeling and modern recurrent neural networks. I also develop deep reinforcement learning methods whose rewards are generated by self-supervised training and intrinsic signals. The main objective is to work towards the development of resilient robots that can learn to anticipate and avoid damaging interactions by combining visual and proprioceptive cues from internal sensors. The introduced solutions are inspired by pain pathways in humans and animals, because such pathways are known to guide decision-making processes and promote self-preservation. A new "robot dodge ball' benchmark is introduced in order to test the validity of the developed algorithms in dynamic environments.
ContributorsRichardson, Trevor W (Author) / Ben Amor, Heni (Thesis advisor) / Yang, Yezhou (Committee member) / Srivastava, Siddharth (Committee member) / Arizona State University (Publisher)
Created2018
Description
In this thesis, a new approach to learning-based planning is presented where critical regions of an environment with low probability measure are learned from a given set of motion plans. Critical regions are learned using convolutional neural networks (CNN) to improve sampling processes for motion planning (MP).
In addition to an identification network, a new sampling-based motion planner, Learn and Link, is introduced. This planner leverages critical regions to overcome the limitations of uniform sampling while still maintaining guarantees of correctness inherent to sampling-based algorithms. Learn and Link is evaluated against planners from the Open Motion Planning Library (OMPL) on an extensive suite of challenging navigation planning problems. This work shows that critical areas of an environment are learnable, and can be used by Learn and Link to solve MP problems with far less planning time than existing sampling-based planners.
In addition to an identification network, a new sampling-based motion planner, Learn and Link, is introduced. This planner leverages critical regions to overcome the limitations of uniform sampling while still maintaining guarantees of correctness inherent to sampling-based algorithms. Learn and Link is evaluated against planners from the Open Motion Planning Library (OMPL) on an extensive suite of challenging navigation planning problems. This work shows that critical areas of an environment are learnable, and can be used by Learn and Link to solve MP problems with far less planning time than existing sampling-based planners.
ContributorsMolina, Daniel, M.S (Author) / Srivastava, Siddharth (Thesis advisor) / Li, Baoxin (Committee member) / Zhang, Yu (Committee member) / Arizona State University (Publisher)
Created2019
Description
Hyperspectral unmixing is an important remote sensing task with applications including material identification and analysis. Characteristic spectral features make many pure materials identifiable from their visible-to-infrared spectra, but quantifying their presence within a mixture is a challenging task due to nonlinearities and factors of variation. In this thesis, physics-based approaches are incorporated into an end-to-end spectral unmixing algorithm via differentiable programming. First, sparse regularization and constraints are implemented by adding differentiable penalty terms to a cost function to avoid unrealistic predictions. Secondly, a physics-based dispersion model is introduced to simulate realistic spectral variation, and an efficient method to fit the parameters is presented. Then, this dispersion model is utilized as a generative model within an analysis-by-synthesis spectral unmixing algorithm. Further, a technique for inverse rendering using a convolutional neural network to predict parameters of the generative model is introduced to enhance performance and speed when training data are available. Results achieve state-of-the-art on both infrared and visible-to-near-infrared (VNIR) datasets as compared to baselines, and show promise for the synergy between physics-based models and deep learning in hyperspectral unmixing in the future.
ContributorsJaniczek, John (Author) / Jayasuriya, Suren (Thesis advisor) / Dasarathy, Gautam (Thesis advisor) / Christensen, Phil (Committee member) / Arizona State University (Publisher)
Created2020
Description
In order for a robot to solve complex tasks in real world, it needs to compute discrete, high-level strategies that can be translated into continuous movement trajectories. These problems become increasingly difficult with increasing numbers of objects and domain constraints, as well as with the increasing degrees of freedom of robotic manipulator arms.
The first part of this thesis develops and investigates new methods for addressing these problems through hierarchical task and motion planning for manipulation with a focus on autonomous construction of free-standing structures using precision-cut planks. These planks can be arranged in various orientations to design complex structures; reliably and autonomously building such structures from scratch is computationally intractable due to the long planning horizon and the infinite branching factor of possible grasps and placements that the robot could make.
An abstract representation is developed for this class of problems and show how pose generators can be used to autonomously compute feasible robot motion plans for constructing a given structure. The approach was evaluated through simulation and on a real ABB YuMi robot. Results show that hierarchical algorithms for planning can effectively overcome the computational barriers to solving such problems.
The second part of this thesis proposes a deep learning-based algorithm to identify critical regions for motion planning. Further investigation is done whether these learned critical regions can be translated to learn high-level landmark actions for automated planning.
The first part of this thesis develops and investigates new methods for addressing these problems through hierarchical task and motion planning for manipulation with a focus on autonomous construction of free-standing structures using precision-cut planks. These planks can be arranged in various orientations to design complex structures; reliably and autonomously building such structures from scratch is computationally intractable due to the long planning horizon and the infinite branching factor of possible grasps and placements that the robot could make.
An abstract representation is developed for this class of problems and show how pose generators can be used to autonomously compute feasible robot motion plans for constructing a given structure. The approach was evaluated through simulation and on a real ABB YuMi robot. Results show that hierarchical algorithms for planning can effectively overcome the computational barriers to solving such problems.
The second part of this thesis proposes a deep learning-based algorithm to identify critical regions for motion planning. Further investigation is done whether these learned critical regions can be translated to learn high-level landmark actions for automated planning.
ContributorsKumar, Kislay (Author) / Srivastava, Siddharth (Thesis advisor) / Zhang, Yu (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2019
Description
This research introduces Roblocks, a user-friendly system for learning Artificial Intelligence (AI) planning concepts using mobile manipulator robots. It uses a visual programming interface based on block-structured programming to make AI planning concepts easier to grasp for those who are new to robotics and AI planning. Users get to accomplish any desired tasks by dynamically populating puzzle shaped blocks encoding the robot’s possible actions, allowing them to carry out tasks like navigation, planning, and manipulation by connecting blocks instead of writing code. Roblocks has two levels, where in the first level users are made to re-arrange a jumbled set of actions of a plan in the correct order so that a given goal could be achieved. In the second level, they select actions of their choice but at each step only those actions pertaining to the current state are made available to them, thereby pruning down the vast number of possible actions and suggesting only the truly feasible and relevant actions. Both of these levels have a simulation where the user plan is executed. Moreover, if the user plan is invalid or fails to achieve the given goal condition then an explanation for the failure is provided in simple English language. This makes it easier for everyone (especially for non-roboticists) to understand the cause of the failure.
ContributorsDave, Chirav (Author) / Srivastava, Siddharth (Thesis advisor) / Hsiao, Ihan (Committee member) / Zhang, Yu (Committee member) / Arizona State University (Publisher)
Created2019