MSR Thesis Defense
Learning Neural Parsers with Deterministic Differentiable Imitation Learning
Abstract: In this work, we explore the problem of learning to decompose spatial tasks into segments, as exemplified by the problem of a painting robot covering a large object. Inspired by the ability of classical decision tree algorithms to construct structured partitions of their input spaces, we formulate the problem of decomposing objects into segments [...]
Carnegie Mellon University
Integrating Structure with Deep Reinforcement and Imitation Learning
Most deep reinforcement and imitation learning methods are data-driven and do not utilize the underlying structure of the problem. While these methods have achieved great success on many challenging tasks, several key problems such as generalization, data efficiency and compositionality remain open. Utilizing problem structure in the form of architecture design, priors, domain knowledge etc. may [...]
Carnegie Mellon University
Learning Reactive Flight Control Policies: from LIDAR measurements to Actions
Abstract The end goal of a reactive flight control pipeline is to output control commands based on local sensor inputs. Classical state estimation and control algorithms break down this problem by first estimating the robot’s velocity and then computing a roll and pitch command based on that velocity. However, this approach is not robust in [...]
Carnegie Mellon University
Transparency in Deep Reinforcement Learning Networks
In the recent years there has been a growing interest in the field of Explainability for machine learning models in general and deep learning in particular. This is because, deep learning based approaches have made tremendous progress in the field of computer vision, reinforcement learning, language related domains and are being increasingly used in application areas [...]
Carnegie Mellon University
Geometric approaches to motion planning for two classes of low-Reynolds number swimmers
Microrobots have the potential to impact many areas of medicine such as microsurgery, targeted drug delivery and minimally invasive sensing. Just like microorganisms themselves, microrobots developed for these applications need to swim in a low-Reynolds number regime which warrants locomotive strategies that differ from their macroscopic counterparts. To this end, Purcell’s three-link planar swimmer has [...]
Carnegie Mellon University
Autonomous 3D Reconstruction in Underwater Unstructured Scenes
Abstract Reconstruction of marine structures such as pilings underneath piers presents a plethora of interesting challenges. It is one of those tasks better suited to a robot due to harsh underwater environments. Underwater reconstruction typically involves human operators remotely controlling the robot to predetermined way-points based on some prior knowledge of the location and model [...]
Carnegie Mellon University
Wire Detection, Reconstruction, and Avoidance for Unmanned Aerial Vehicles
Abstract Thin objects, such as wires and power lines are one of the most challenging obstacles to detect and avoid for UAVs, and are a cause of numerous accidents each year. This thesis makes contributions in three areas of this domain: wire segmentation, reconstruction, and avoidance. Pixelwise wire detection can be framed as a binary [...]
The Art of Robotics: Toward a Holistic Approach
I arrived at the Robotics Institute two years ago looking for a good project, something tangible and preferably related to legged locomotion. Instead, I met Matt Mason and started to think about the big picture, ask the big questions. What is manipulation? What is robotics? What makes robotics particularly hard? To answer these questions, I [...]
Carnegie Mellon University
Mapping gamma sources and their flux fields using non-directional flux measurements
There is a compelling need to determine the location and activity of radiation sources from the flux that they generate. There is also a need to create dense flux maps from sparse measurements. This research solves these dual problems. An example of a situation where these capabilities would be vital is at the location of [...]
Carnegie Mellon University
Automated Design of Special Purpose Dexterous Manipulators
Grasp planning and motion synthesis for dexterous manipulation tasks are traditionally done given a pre-existing kinematic model for the robotic hand. In this thesis, we introduce a framework for automatically designing hand topologies best suited for manipulation tasks given high level objectives as input. Our goal is to ultimately design a program that is able [...]
Carnegie Mellon University
Toward Invariant Visual Inertial State Estimation using Information Sparsification
Abstract In this work, we address two current challenges in real-time visual-inertial odometry (VIO) systems - efficiency and accuracy. To this end, we present a novel approach to tightly couple visual and inertial measurements in a fixed-lag VIO framework using information sparsification. To bound computational complexity, fixed-lag smoothers perform marginalization of variables but consequently deteriorate accuracy and [...]
Generative Point Cloud Modeling with Gaussian Mixture Models for Multi-Robot Exploration
Autonomous exploration in rich 3D environments requires the construction and maintenance of a representation derived from accumulated 3D observations. Volumetric models, which are commonly employed to enable joint reasoning about occupied and free space, scale poorly with the size of the environment. Techniques employed to mitigate this scaling include hierarchical discretization, learning local data summarizations [...]
Carnegie Mellon University
Integrating Model-based Planning with Skill learning for Mobile Manipulation
With an ever-growing demand to automate different day-to-day activities, the task of autonomous manipulation using articulated robots has gained serious traction lately. In this regard, motion planning for manipulation is one of the highly researched topics. The Motion planning for manipulation is often cast as either a model-based planning problem or a machine learning problem. However, both of these [...]
Carnegie Mellon University
In-Field Robotic Leaf Grasping and Automated Crop Spectroscopy
Agricultural robotics is a growing field of intelligent automation that is proving to drastically increase the speed and reliability of in-field tasks such as precision seed planting, harvesting, field mapping, and crop monitoring. More specifically, plant breeders are beginning to use robotic systems to record the physical traits of crops throughout the growing season at [...]
Carnegie Mellon University
Soft-matter Artificial Muscle by Electrochemical Surface Oxidation of Liquid Metal
Natural muscles, a result of more than 500 millions years of evolution, are elegant machines that generate force and motion electrochemically. The brief history of robotics does not have the luxury of millions of years to reverse-engineer many aspects of life. The development of artificial muscles therefore seeks to build more muscle-like actuators for robots. [...]
Radiation Source Localization using a Gamma-ray Camera
Radiation source localization is a common and critical task across applications such as nuclear facility decommissioning, radioactive disaster response, and security. Traditional count-based sensors (e.g. Geiger counters) infer range to the source based on the observed number of gamma photons, expected source strength, and assumed intermediate attenuation from the environment. In cluttered 3D settings, such [...]
Carnegie Mellon University
Improving Imitation Learning through Efficient Expert Querying
Learning from demonstration is an intuitive approach to encoding complex behaviors in autonomous agents. Learners have shown success in challenging tasks like autonomous driving, aerial obstacle avoidance, and information gathering, through observation and mimicry alone. State of the art algorithms like Dataset Aggregation (DAgger) have made significant advances over traditional behavior cloning, demonstrating strong theoretical [...]
Failure Is an Option: How the Severity of Robot Errors Affects Human-Robot Interactions
Abstract: Just as humans are imperfect, even the best of robots will eventually fail at performing a task. The likelihood of failure increases as robots expand their roles in our lives. Although task failure is a common problem in robotics and human-robot interaction (HRI), there has been little research investigating human tolerance to said failures, [...]
Carnegie Mellon University
MSR Thesis Talk: Avi Rudich
Title: Kinematic Analysis of 3D Printed Flexible Delta Robots Abstract: Flexible Delta robots show significant promise for use in a wide array of manipulation tasks. They are simple to design and manufacture, and they maintain a high level of repeatability and precision in open loop control. This thesis analyzes the kinematic properties of flexible [...]
Learning Parameter-Efficient Quadrotor Dynamics Models
Abstract: Operation of quadrotors through high-speed, high-acceleration maneuvers remains a challenging problem due to the complex aerodynamics in this regime. While standard physical models suffice for control in near-hover conditions, the primary challenge in executing aggressive trajectories is obtaining a model for the quadrotor dynamics that adequately models the aerodynamic effects present, including lift, drag, [...]
Human-in-the-loop Model Creation
Abstract: Deep generative models make visual content creation more accessible to novice users by automating the synthesis of diverse, realistic content based on a collected dataset. However, the current machine learning approaches miss several elements of the creative process -- the ability to synthesize things that go far beyond the data distribution and everyday experience, [...]
Learning Models and Cost Functions from Unlabeled Data for Off-Road Driving
Abstract: Off-road driving is an important instance of navigation in unstructured environments, which is a key robotics problem with many applications, such as exploration, agriculture, disaster response and defense. The key challenge in off-road driving is to be able to take in high dimensional, multi-modal sensing data and use it to make intelligent decisions on [...]
MSR Thesis Talk: Chonghyuk Song
Title: Total-Recon: Deformable Scene Reconstruction for Embodied View Synthesis Abstract: We explore the task of embodied view synthesis from monocular videos of deformable scenes. Given a minute-long RGBD video of people interacting with their pets, we render the scene from novel camera trajectories derived from in-scene motion of actors: (1) egocentric cameras that simulate the point [...]
MSR Thesis Talk: Shivam Duggal
Title: Learning Single Image 3D Reconstruction from Single-View Image Collections Abstract We present a framework for learning 3D object shapes and dense cross-object 3D correspondences from just an unaligned category-specific image collection. The 3D shapes are generated implicitly as deformations to a category-specific signed distance field and are learned in an unsupervised manner solely from unaligned [...]
MSR Thesis Talk: Himangi Mittal
Title: Audio-Visual State-Aware Representation Learning from Interaction-Rich Data Abstract In robotics and augmented reality, the input to the agent is a long stream of video from the first-person or egocentric point of view. Recently, there have been significant efforts to capture humans from their first-person/egocentric view interacting with their own environment as they go about [...]
MSR Thesis Talk: Ken Liu
Title: On Privacy and Personalization in Federated Learning: Analyses and Applications Abstract: Recent advances in machine learning often rely on large and centralized datasets. However, curating such data can be challenging when they hold private information, and policies/regulations may mandate that they remain distributed across data silos (e.g. mobile devices or hospitals). Federated learning (FL) [...]
Carnegie Mellon University
MSR Thesis Talk: Haolun Zhang
Title: Seeing in 3D: Towards Generalizable 3D Visual Representations for Robotic Manipulation Abstract: Despite the recent progress in computer vision and deep learning, robot perception remains a tremendous challenge due to the variations of the objects and the scenes in manipulation tasks. Ideally, a robot trying to manipulate a new object should be able to [...]
MSR Thesis Talk: Muyang Li
Title: Efficient Spatially Sparse Inference for Conditional GANs and Diffusion Models Abstract: During image editing, existing deep generative models tend to re-synthesize the entire output from scratch, including the unedited regions. This leads to a significant waste of computation, especially for minor editing operations. In this work, we present Spatially Sparse Inference (SSI), a general-purpose technique [...]
MSR Thesis Talk: Rohan Zeng
Title: Spectral Unmixing and Mapping of Coral Reef Benthic Cover Abstract: Coral reefs are important to the global ecosystem and the local communities and wildlife that rely on the habitat they create. However, coral reefs are also in critical and rapid decline: reefs have degraded over recent decades and what remains is at increasing risk [...]