Matching Items (24)
Filtering by
- Genre: Academic theses
Description
There has been exciting progress in the area of Unmanned Aerial Vehicles (UAV) in the last decade, especially for quadrotors due to their nature of easy manipulation and simple structure. A lot of research has been done on achieving autonomous and robust control for quadrotors. Recently researchers have been utilizing linear temporal logic as mission specification language for robot motion planning due to its expressiveness and scalability. Several algorithms have been proposed to achieve autonomous temporal logic planning. Also, several frameworks are designed to compose those discrete planners and continuous controllers to make sure the actual trajectory also satisfies the mission specification. However, most of these works use first-order kinematic models which are not accurate when quadrotors fly at high speed and cannot fully utilize the potential of quadrotors.
This thesis work describes a new design for a hierarchical hybrid controller that is based on a dynamic model and seeks to achieve better performance in terms of speed and accuracy compared with some previous works. Furthermore, the proposed hierarchical controller is making progress towards guaranteed satisfaction of mission specification expressed in Linear Temporal Logic for dynamic systems. An event-driven receding horizon planner is also utilized that aims at distributed and decentralized planning for large-scale navigation scenarios. The benefits of this approach will be demonstrated using simulations results.
This thesis work describes a new design for a hierarchical hybrid controller that is based on a dynamic model and seeks to achieve better performance in terms of speed and accuracy compared with some previous works. Furthermore, the proposed hierarchical controller is making progress towards guaranteed satisfaction of mission specification expressed in Linear Temporal Logic for dynamic systems. An event-driven receding horizon planner is also utilized that aims at distributed and decentralized planning for large-scale navigation scenarios. The benefits of this approach will be demonstrated using simulations results.
ContributorsZhang, Xiaotong (Author) / Fainekos, Georgios (Thesis advisor) / Ben Amor, Heni (Committee member) / Shrivastava, Aviral (Committee member) / Arizona State University (Publisher)
Created2016
Description
Imitation learning is a promising methodology for teaching robots how to physically interact and collaborate with human partners. However, successful interaction requires complex coordination in time and space, i.e., knowing what to do as well as when to do it. This dissertation introduces Bayesian Interaction Primitives, a probabilistic imitation learning framework which establishes a conceptual and theoretical relationship between human-robot interaction (HRI) and simultaneous localization and mapping. In particular, it is established that HRI can be viewed through the lens of recursive filtering in time and space. In turn, this relationship allows one to leverage techniques from an existing, mature field and develop a powerful new formulation which enables multimodal spatiotemporal inference in collaborative settings involving two or more agents. Through the development of exact and approximate variations of this method, it is shown in this work that it is possible to learn complex real-world interactions in a wide variety of settings, including tasks such as handshaking, cooperative manipulation, catching, hugging, and more.
ContributorsCampbell, Joseph (Author) / Ben Amor, Heni (Thesis advisor) / Fainekos, Georgios (Thesis advisor) / Yamane, Katsu (Committee member) / Kambhampati, Subbarao (Committee member) / Arizona State University (Publisher)
Created2021
Description
Autonomous systems powered by Artificial Neural Networks (NNs) have shown remarkable capabilities in performing complex tasks that are difficult to formally specify. However, ensuring the safety, reliability, and trustworthiness of these NN-based systems remains a significant challenge, especially when they encounter inputs that fall outside the distribution of their training data. In robot learning applications, such as lower-leg prostheses, even well-trained policies can exhibit unsafe behaviors when faced with unforeseen or adversarial inputs, potentially leading to harmful outcomes. Addressing these safety concerns is crucial for the adoption and deployment of autonomous systems in real-world, safety-critical environments. To address these challenges, this dissertation presents a neural network repair framework aimed at enhancing safety in robot learning applications. First, a novel layer-wise repair method utilizing Mixed-Integer Quadratic Programming (MIQP) is introduced that enables targeted adjustments to specific layers of a neural network to satisfy predefined safety constraints without altering the network’s structure. Second, the practical effectiveness of the proposed methods is demonstrated through extensive experiments on safety-critical assistive devices, particularly lower-leg prostheses, to ensure the generation of safe and reliable neural policies. Third, the integration of predictive models is explored to enforce implicit safety constraints, allowing for anticipation and mitigation of unsafe behaviors through a two-step supervised learning approach that combines behavioral cloning with neural network repair. By addressing these areas, this dissertation advances the state-of-the-art in neural network repair for robot learning. The outcome of this work promotes the development of robust and secure autonomous systems capable of operating safely in unpredictable and dynamic real-world environments.
ContributorsMajd, Keyvan (Author) / Ben Amor, Heni (Thesis advisor) / Fainekos, Georgios (Thesis advisor) / Srivastava, Siddharth (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2024
Description
Autonomous Vehicles (AVs) have the potential to significantly evolve transportation. AVs are expected to make transportation safer by avoiding accidents that happen due to human errors. When AVs become connected, they can exchange information with the infrastructure or other Connected Autonomous Vehicles (CAVs) to efficiently plan their future motion and therefore, increase the road throughput and reduce energy consumption. Cooperative algorithms for CAVs will not be deployed in real life unless they are proved to be safe, robust, and resilient to different failure models. Since intersections are crucial areas where most accidents happen, this dissertation first focuses on making existing intersection management algorithms safe and resilient against network and computation time, bounded model mismatches and external disturbances, and the existence of a rogue vehicle. Then, a generic algorithm for conflict resolution and cooperation of CAVs is proposed that ensures the safety of vehicles even when other vehicles suddenly change their plan. The proposed approach can also detect deadlock situations among CAVs and resolve them through a negotiation process. A testbed consisting of 1/10th scale model CAVs is built to evaluate the proposed algorithms. In addition, a simulator is developed to perform tests at a large scale. Results from the conducted experiments indicate the robustness and resilience of proposed approaches.
ContributorsKhayatian, Mohammad (Author) / Shrivastava, Aviral (Thesis advisor) / Fainekos, Georgios (Committee member) / Ben Amor, Heni (Committee member) / Yang, Yezhou (Committee member) / Lou, Yingyan (Committee member) / Iannucci, Bob (Committee member) / Arizona State University (Publisher)
Created2021
Description
A swarm describes a group of interacting agents exhibiting complex collective behaviors. Higher-level behavioral patterns of the group are believed to emerge from simple low-level rules of decision making at the agent-level. With the potential application of swarms of aerial drones, underwater robots, and other multi-robot systems, there has been increasing interest in approaches for specifying complex, collective behavior for artificial swarms. Traditional methods for creating artificial multi-agent behaviors inspired by known swarms analyze the underlying dynamics and hand craft low-level control logics that constitute the emerging behaviors. Deep learning methods offered an approach to approximate the behaviors through optimization without much human intervention.
This thesis proposes a graph based neural network architecture, SwarmNet, for learning the swarming behaviors of multi-agent systems. Given observation of only the trajectories of an expert multi-agent system, the SwarmNet is able to learn sensible representations of the internal low-level interactions on top of being able to approximate the high-level behaviors and make long-term prediction of the motion of the system. Challenges in scaling the SwarmNet and graph neural networks in general are discussed in detail, along with measures to alleviate the scaling issue in generalization is proposed. Using the trained network as a control policy, it is shown that the combination of imitation learning and reinforcement learning improves the policy more efficiently. To some extent, it is shown that the low-level interactions are successfully identified and separated and that the separated functionality enables fine controlled custom training.
This thesis proposes a graph based neural network architecture, SwarmNet, for learning the swarming behaviors of multi-agent systems. Given observation of only the trajectories of an expert multi-agent system, the SwarmNet is able to learn sensible representations of the internal low-level interactions on top of being able to approximate the high-level behaviors and make long-term prediction of the motion of the system. Challenges in scaling the SwarmNet and graph neural networks in general are discussed in detail, along with measures to alleviate the scaling issue in generalization is proposed. Using the trained network as a control policy, it is shown that the combination of imitation learning and reinforcement learning improves the policy more efficiently. To some extent, it is shown that the low-level interactions are successfully identified and separated and that the separated functionality enables fine controlled custom training.
ContributorsZhou, Siyu (Author) / Ben Amor, Heni (Thesis advisor) / Walker, Sara I (Thesis advisor) / Davies, Paul (Committee member) / Pavlic, Ted (Committee member) / Presse, Steve (Committee member) / Arizona State University (Publisher)
Created2020
Description
A complex social system, whether artificial or natural, can possess its macroscopic properties as a collective, which may change in real time as a result of local behavioral interactions among a number of agents in it. If a reliable indicator is available to abstract the macrolevel states, decision makers could use it to take a proactive action, whenever needed, in order for the entire system to avoid unacceptable states or con-verge to desired ones. In realistic scenarios, however, there can be many challenges in learning a model of dynamic global states from interactions of agents, such as 1) high complexity of the system itself, 2) absence of holistic perception, 3) variability of group size, 4) biased observations on state space, and 5) identification of salient behavioral cues. In this dissertation, I introduce useful applications of macrostate estimation in complex multi-agent systems and explore effective deep learning frameworks to ad-dress the inherited challenges. First of all, Remote Teammate Localization (ReTLo)is developed in multi-robot teams, in which an individual robot can use its local interactions with a nearby robot as an information channel to estimate the holistic view of the group. Within the problem, I will show (a) learning a model of a modular team can generalize to all others to gain the global awareness of the team of variable sizes, and (b) active interactions are necessary to diversify training data and speed up the overall learning process. The complexity of the next focal system escalates to a colony of over 50 individual ants undergoing 18-day social stabilization since a chaotic event. I will utilize this natural platform to demonstrate, in contrast to (b), (c)monotonic samples only from “before chaos” can be sufficient to model the panicked society, and (d) the model can also be used to discover salient behaviors to precisely predict macrostates.
ContributorsChoi, Taeyeong (Author) / Pavlic, Theodore (Thesis advisor) / Richa, Andrea (Committee member) / Ben Amor, Heni (Committee member) / Yang, Yezhou (Committee member) / Liebig, Juergen (Committee member) / Arizona State University (Publisher)
Created2020
Description
Autonomous systems should satisfy a set of requirements that guarantee their safety, efficiency, and reliability when working under uncertain circumstances. These requirements can have financial, or legal implications or they can describe what is assigned to autonomous systems.As a result, the system controller needs to be designed in order to comply with these - potentially complicated - requirements, and the closed-loop system needs to be tested and verified against these requirements.
However, when the complexity of the system and its requirements increases, designing a requirement-based controller for the system and analyzing the closed-loop system against the requirement becomes very challenging. In this case, existing design and test methodologies based on trial-and-error would fail, and hence disciplined scientific approaches should be considered.
To address some of these challenges, in this dissertation, I present different methods that facilitate efficient testing, and control design based on requirements:
1. Gradient-based methods for improved optimization-based testing,
2. Requirement-based learning for the design of neural-network controllers,
3. Methods based on barrier functions for designing control inputs that ensure the satisfaction of safety constraints.
ContributorsYaghoubi, Shakiba (Author) / Fainekos, Georgios (Thesis advisor) / Ben Amor, Heni (Committee member) / Bertsekas, Dimitri (Committee member) / Pedrielli, Giulia (Committee member) / Sankaranarayanan, Sriram (Committee member) / Arizona State University (Publisher)
Created2021
Description
This work explores combining state-of-the-art \gls{mbrl} algorithms focused on learning complex policies with large state-spaces and augmenting them with distributional reward perspective on \gls{rl} algorithms. Distributional \gls{rl} provides a probabilistic reward formulation as opposed to the classic \gls{rl} formulation which models the estimation of this distributional return. These probabilistic reward formulations help the agent choose highly risk-averse actions, which in turn makes the learning more stable. To evaluate this idea, I experiment in simulation on complex high-dimensional environments when subject under different noisy conditions.
ContributorsAgarwal, Nikhil (Author) / Ben Amor, Heni (Thesis advisor) / Phielipp, Mariano (Committee member) / DV, Hemanth (Committee member) / Arizona State University (Publisher)
Created2021
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
This dissertation is focused on developing an algorithm to provide current state estimation and future state predictions for biomechanical human walking features. The goal is to develop a system which is capable of evaluating the current action a subject is taking while walking and then use this to predict the future states of biomechanical features.
This work focuses on the exploration and analysis of Interaction Primitives (Amor er al, 2014) and their relevance to biomechanical prediction for human walking. Built on the framework of Probabilistic Movement Primitives, Interaction Primitives utilize an EKF SLAM algorithm to localize and map a distribution over the weights of a set of basis functions. The prediction properties of Bayesian Interaction Primitives were utilized to predict real-time foot forces from a 9 degrees of freedom IMUs mounted to a subjects tibias. This method shows that real-time human biomechanical features can be predicted and have a promising link to real-time controls applications.
This work focuses on the exploration and analysis of Interaction Primitives (Amor er al, 2014) and their relevance to biomechanical prediction for human walking. Built on the framework of Probabilistic Movement Primitives, Interaction Primitives utilize an EKF SLAM algorithm to localize and map a distribution over the weights of a set of basis functions. The prediction properties of Bayesian Interaction Primitives were utilized to predict real-time foot forces from a 9 degrees of freedom IMUs mounted to a subjects tibias. This method shows that real-time human biomechanical features can be predicted and have a promising link to real-time controls applications.
ContributorsClark, Geoffrey Mitchell (Author) / Ben Amor, Heni (Thesis advisor) / Si, Jennie (Committee member) / Berisha, Visar (Committee member) / Arizona State University (Publisher)
Created2018