PhD Thesis Proposal
Carnegie Mellon University
Learning multi-robot behaviors for online control
Abstract: Finding dynamically feasible and safe global plans for multi-agent teams in real world applications is enormously difficult because the decision branching factor, when considering all possible interactions across agents and an environment, is usually intractable. Humans, however, have great success in the multi-agent planning domain by using behaviors: practiced, coordinated responses for groups of [...]
Carnegie Mellon University
Routing for Persistent Exploration in Dynamic Environments with Teams of Energy-Constrained Robots
Abstract: In domains requiring effective situational awareness with limited resources, prioritizing focus is critical. Search and rescue tasks require fast identification of safe avenues for rescuers to traverse the area. Inspection tasks must realize trends over long durations to identify issues caused by the confluence of high-stress modes that compound into catastrophic failure. Deploying robots [...]
Carnegie Mellon University
Enabling Role-Reversible Human-Robot Interaction by Leveraging Standardized Tools for Provider-Receiver Interactions
Abstract: Developing 'social intelligence' for assistive robots to seamlessly interact with humans remains an open research challenge. However, socially assistive robots typically engage in types of interactions that already exist between humans, which makes models of human-human interactions useful to inform the design of robot social behaviors. In particular, in applications such as healthcare, therapy [...]
Carnegie Mellon University
Towards Understanding and Mitigating Biases
Abstract: There are many problems in real life that involve collecting and aggregating evaluation from people, such as conference peer review and peer grading. In this thesis, we consider multiple sources of biases that may arise in this process: (1) human bias -- the data collected from people are noisy and reflect people's calibration criteria [...]
Sensor Planning for Large Numbers of Robots
Abstract: Teams of aerial sensing robots can provide valuable situational awareness to first responders during disasters and emergencies by locating victims, mapping environments, identifying hazards, and locating gas sources. While individual sensing tasks may feature a variety of goals, many tasks will be time-sensitive such as after a widespread disaster or due to imminent danger [...]
Carnegie Mellon University
Resource-Constrained State Estimation with Multi-Modal Sensing
Abstract: Accurate and reliable state estimation is essential for safe mobile robot operation in real-world environments because ego-motion estimates are required by many critical autonomy functions such as control, planning, and mapping. Computing accurate state estimates depends on the physical characteristics of the environment, the selection of suitable sensors to capture that information, and the [...]
Carnegie Mellon University
Open-world Object Detection and Tracking
Abstract: Computer vision today excels at recognition in narrow slices of the real world. Our systems seem to accurately detect cats, cars, or chairs, but largely ignore the vast diversity of objects in the world that are absent from our training datasets. Perception in the open world, however, requires detecting and tracking any object, regardless [...]
Carnegie Mellon University
‘Unboxing’ anomaly detection and panoptic segmentation
Abstract: Panoptic segmentation is a recent problem in computer vision that attempts to classify each pixel in an image according to its semantic and instance label (accomplishing both semantic segmentation and instance segmentation respectively). Most existing panoptic and instance segmentation methods run a detection-first pipeline, where a bounding box is placed around an object and [...]
Carnegie Mellon University
Transfers Between Multiple Service Robots
Abstract: With the deployment of more robots, human-robot interaction will no longer be limited to a one-to-one interaction between a user and a robot. Instead, users will likely have to interact with multiple robots, simultaneously or sequentially, throughout their day to receive services and complete different tasks. In this thesis proposal, I am proposing joint [...]
Carnegie Mellon University
Machine Learning Parallelism Could Be Adaptive, Composable and Automated
Abstract: In recent years, the pace of innovations in the fields of machine learning has accelerated. To cope with the sheer computational complexity of training large ML models on large datasets, researchers in SysML have created algorithms and systems that parallelize ML training and inference over multiple CPUs or GPUs, or even multiple computing nodes [...]
Carnegie Mellon University
Expressive Real-time Intersection Scheduling: New Methods for Adaptive Traffic Signal Control
Abstract: Traffic congestion is a widespread problem throughout global metropolitan areas. In this thesis, we consider methods to optimize the performance of traffic signals to reduce congestion. We begin by presenting Expressive Real-time Intersection Scheduling (ERIS), a schedule-driven intersection control strategy that runs independently on each intersection in a traffic network. For each intersection, ERIS [...]
Adaptive Planning and Control of Wheeled Mobile Robots in Challenging Environments
Abstract: Over the last two decades, we have seen driverless cars conquer the Mojave desert, drive on mars and operate on our streets and warehouses. One of the most fundamental requirements of such robots is their ability to navigate their environment with minimal human oversight. As more robots graduate from the confines of laboratories to [...]
Carnegie Mellon University
Learning Dense 3D Object Reconstruction without Geometric Supervision
Abstract: Geometric alignment across visual data has been the fundamental issue for effective and efficient computer vision algorithms. The established pixel correspondences between images indirectly infer the underlying 3D geometry, physically or semantically. While this builds the foundation of classical multi-view 3D reconstruction algorithms such as Structure from Motion (SfM) and Simultaneous Localization and Mapping [...]
Carnegie Mellon University
Eye Gaze for Assistive Manipulation
Abstract: Full robot autonomy is the traditional goal of robotics research. To work in a human-inhabited world, however, robots will often need to collaborate with humans. Many scenarios require human users to teleoperate robots to perform tasks, a paradigm that appears everywhere from space exploration, to disaster recovery, to assistive robotics. This collaboration enables tasks [...]
Carnegie Mellon University
Stability-Centric Mechanics for Rigid Body Manipulation
Abstract: The repertoire of human manipulation is filled with creative use of contacts to move the object about the hand and the environment. It’s the combination of these skills that makes human manipulation dexterous. However, in most robotic applications the robot just fix all contact points on the object and do grasping. Reliable robot manipulation [...]
Carnegie Mellon University
Robotic Grasping in the Wild
Zoom Link Abstract Robotics and artificial intelligence have witnessed tremendous progress in the past decade. Yet, we are still far from building the general purpose robot butler that can autonomously operate in homes and help with manipulation tasks like household chores. Grasping is an important action primitive for manipulation and needs to generalize to unstructured [...]
Carnegie Mellon University
Active Vision: Autonomous Aerial Cinematography with Learned Artistic Decision-Making
Zoom Link Abstract: Aerial cinematography is revolutionizing industries that require live and dynamic camera viewpoints such as entertainment, sports, and security. Fundamentally, it is a tool with immense potential to improve human creativity, expressiveness, and sharing of experiences. However, safely piloting a drone while filming a moving target in the presence of obstacles is immensely [...]
Carnegie Mellon University
Efficient Robot Decision-Making for Achieving Multiple Independent Tasks
Zoom Link Abstract: We focus on robotics applications where a robot is required to accomplish a set of tasks that are partially observable and evolve independently of each other according to their dynamics. One such domain that we target in this work is decision-making for a robot waiter waiting tables at a restaurant. The robot [...]
Carnegie Mellon University
Provably Constant-time Motion Planning
Zoom Link Abstract: In manufacturing and warehouse scenarios, robots often perform recurring manipulation tasks in structured environments. Fast and reliable motion planning is one of the key elements that ensure efficient operations in such environments. A very common example scenario is of manipulators working at conveyor belts, where they have limited time to pick moving [...]
Carnegie Mellon University
Safe and Resilient Multi-Robot Systems: Heterogeneity and Human Presence
Zoom Link Abstract: In the mission of a multi-robot team, the large number of robots behave like a system that relies on networking to enable smooth information propagation and inter-robot interaction as the mission evolves in a collective fashion. Key to the success of mission operation demands for safe and reliable robot interactions within the [...]
Carnegie Mellon University
Heuristics for routing and scheduling of Spatio-Temporal type problems in industrial environments
Zoom Link Abstract: Spatio-temporal problems are fairly common in industrial environments. In practice, these problems come with different characteristics and are often very hard to solve optimally. So practitioners prefer to develop heuristics that exploit mathematical structure specific to the problem for obtaining good performance. In this proposal, I will present work on heuristics for [...]
Carnegie Mellon University
Understanding, Exploiting and Improving Inter-view Relationships
Zoom Link Abstract: Multi-view machine learning has received substantial attention in various applications over recent years. These applications typically involve learning on data obtained from multiple sources of information, such as, for example, in multi-sensor systems such as self-driving cars and patient bed-side monitoring. Learning models for such applications can often benefit from leveraging not [...]
Carnegie Mellon University
Computational Contact Modes for Robotics
Zoom Link Abstract: A central theme in robotics is that of robots interacting with the world through physical contact. Whether it is a walking robot or robotic manipulator picking up an object, such as a spoon, we desire robots that physically interact with their environments. One significant challenge in physical robot interactions involves dealing with [...]
Carnegie Mellon University
Planning and Execution using Inaccurate Models with Provable Guarantees on Task Completeness
Abstract: Modern planning methods are effective in computing feasible and optimal plans for robotic tasks when given access to accurate dynamical models. However, robots operating in the real world often face situations that cannot be modeled perfectly before execution. Thus, we only have access to simplified but potentially inaccurate models. This imperfect modeling can lead [...]
Carnegie Mellon University
Physical Interaction and Manipulation of the Environment using Aerial Robots
Abstract: There has been an increasing demand for applications that include aerial robots' physical interactions with their environment, such as contact inspection, package pickup, and drilling. The demand has pushed the research groups towards new robot architectures and methods, but only limited research has been done to enable real-world applications. Fully-actuated multirotors were developed to [...]
Carnegie Mellon University
Visual Recognition Towards Autonomy
Abstract: Perception for autonomy presents a collection of compelling challenges for visual recognition. We focus on three key challenges in this thesis. The first key challenge is learning representations for 2D data such as RGB images. 2D sensing brings unique challenges in scale variance and occlusion. Intuitively, the cues for recognizing a 3px tall object [...]
Carnegie Mellon University
Rich Models and Maps in Factor Graphs with Applications to Tactile Sensing
Abstract: Factor graphs offer a flexible and powerful framework for solving large-scale, nonlinear inference problems as encountered in robot perception. Typically these methods rely on simple models that are efficient to optimize. However, robots often perceive the world through complex, high-dimensional observations. They must in turn infer states that are used downstream by planning and [...]
Carnegie Mellon University
Distributed Navigation of Quadrotor Teams in Uncertain 3D Workspaces
Abstract: A fundamental requirement for realizing scalable and responsive real-world multi-robot systems for time-sensitive critical applications such as search and rescue or building clearance is a motion-planning and coordination framework that exhibits two essential properties. The first property is safety which encompasses aspects relating to kinodynamic feasibility and collision-avoidance. The second property is reliability which [...]
Carnegie Mellon University
Bayesian Models for Science-Driven Robotic Exploration
Abstract: Planetary rovers have traversed many kilometers and made major scientific discoveries. However, they spend a considerable amount of time awaiting instructions from ground operators. The reason is that they are designed for automated science data collection, not for autonomous exploration. The exploration of more distant worlds with stronger communication constraints will require a new [...]
Verification and Accreditation of Artificial Intelligence
Abstract: This work involves formally verifying a trained model's adherence to important design specifications for the purpose of model accreditation. Accreditation of a trained model requires enumeration of the explicit operational conditions under which the model is certified to meet all necessary specifications. By verifying model adherence to specifications set by developers, we increase the [...]
Carnegie Mellon University
Direct-drive Hands: Making Robot Hands Transparent and Reactive to Contacts
Abstract: Industrial manipulators and end-effectors are a vital driver of the automation revolution. These robot hands, designed to reject disturbances with stiffness and strength, are inferior to their human counterparts. Human hands are dexterous and nimble effectors capable of a variety of interactions with the environment. Through this thesis we wish to answer a question: [...]
Carnegie Mellon University
Teleoperation via Intuition: Safe and Intent Oriented Navigation
Abstract: This thesis aims to enable seamless teleoperation of a mobile robot by a human operator, such that the robot navigates in unstructured environments following the operator’s intent intuitively, safely, and efficiently. The roles of the human and robot are disproportionate in traditional teleoperation: The human is responsible for most of the autonomy of the [...]
Carnegie Mellon University
Towards the Automated Design of Neural Networks
Abstract: Neural architecture search (NAS) is recently proposed to automate the process of designing network architectures. Instead of manually designing network architectures, NAS automatically finds the optimal architecture in a data-driven way. Despite its impressive progress, NAS is still far from being widely adopted as a common paradigm for architecture design in practice. This thesis [...]
Carnegie Mellon University
Dynamical Model Learning and Inversion for Aggressive Quadrotor Flight
Quadrotor applications have seen a surge recently and many tasks require precise and accurate controls. Flying fast is critical in many applications and the limited onboard power source makes completing tasks quickly even more important. Staying on a desired course while traveling at high speeds and high accelerations is difficult due to complex and stochastic [...]
Carnegie Mellon University
Development of an Agile and Dexterous Balancing Mobile Manipulator Robot
Abstract: The proposed thesis work focuses on the design and control of a new unique agile and dexterous mobile manipulator, the Carnegie Mellon University (CMU) ballbot. The CMU ballbot is a human-sized dynamically stable mobile robot that balances on a single ball. We present the development and integration of a new pair of seven-degree-of-freedom (7-DOF) [...]
Carnegie Mellon University
Accelerating Numerical Methods for Optimal Control
Abstract: Many modern control methods, such as model-predictive control, rely heavily on solving optimization problems in real time. In particular, the ability to efficiently solve optimal control problems has enabled many of the recent breakthroughs in achieving highly dynamic behaviors for complex robotic systems. The high computational requirements of these algorithms demand novel algorithms tailor-suited [...]
Carnegie Mellon University
Robust Object Representations for Robot Manipulation
Abstract: As robots become more common in our daily lives, they will need to interact with many different environments and countless types of objects. While we, as humans, can easily understand an object after seeing it only once, this task is not trivial for robots. Researchers have, for the most part, been left with two [...]
Carnegie Mellon University
Visual Representation and Recognition without Human Supervision
Abstract: Visual recognition models have seen great advancements by relying on large-scale, carefully curated datasets with human annotations. Most computer vision models leverage human supervision to either construct strong initial representations (e.g. using the ImageNet dataset) or for modeling the visual concepts relevant for downstream tasks (e.g. MS-COCO for object detection). In this thesis, we [...]
Carnegie Mellon University
Heuristic Search Based Planning by Minimizing Anticipated Search Efforts
Abstract: Robot planning problems in dynamic environments, such as navigation among pedestrians, driving at high-speed on densely populated roads, and manipulation for collaborative tasks alongside humans, necessitate efficient planning. Bounded-suboptimal heuristic search algorithms are a popular alternative to optimal heuristic search algorithms that compromise solution quality for computation speed. Specifically, these searches aim to find [...]
Carnegie Mellon University
Liquid Metal Actuators
Abstract: Bioinspired robotic actuators arise from the advances in soft materials and activation methods to achieve desired performance. Because of their intrinsic compliance, actuators built from soft materials and liquids can achieve elastic resilience and adaptability similar to their biological counterparts. Liquid metals provide great opportunities for creating an artificial muscle that generates forces at [...]
Carnegie Mellon University
Self-Learning of Structured Visual Representations
Abstract: Most computer vision models in deployment today are not learning. Instead, they are in a "test" mode, where they will behave the same way perpetually, until they are replaced by newer models. This is a problem, because it means the models may perform poorly as soon as their "test" environment becomes different from their [...]
Carnegie Mellon University
Resource-Constrained Learning and Inference for Visual Perception
Abstract: Real-world applications usually require computer vision algorithms to meet certain resource constraints. In this talk, I will present evaluation methods and principled solutions for both training and testing. First, I will talk about a formal setting for studying training under the non-asymptotic, resource-constrained regime, i.e., budgeted training. We analyze the following problem: "given a [...]
Social Navigation with Pedestrian Groups
Abstract: Autonomous navigation in human crowds (i.e., social navigation) presents several challenges: The robot often needs to rely on its noisy sensors to identify and localize the pedestrians in human crowds; The robot needs plan efficient paths to reach its goals; The robot needs to do so in a safe and socially appropriate manner. In [...]
Control of Robots with Nonstationary Dynamics
Abstract: Robots can be constructed with fewer resources and less strict constraints than is generally believed. Soft robots can be constructed with very few parts and from a wide variety of materials. This makes them a potentially appealing choice for applications where there are resource constraints on system fabrication. However, soft robot dynamics are difficult [...]
Meshlet Primitives for Dense RGB-D SLAM in Dynamic Environments
Abstract: Dense RGB-D SLAM has been well established as a method for achieving robust localization while providing high quality dense surface reconstruction. However, despite significant progress, dense RGB-D SLAM has remained difficult to achieve on computationally constrained platforms, such as those used on autonomous aerial vehicles. A significant limiting factor in the current state of [...]
Computational Light Transport with Interferometry
Abstract: Optical interferometry is the measurement of small, sub-wavelength distances by exploiting the wave nature of light. Due to its capability to resolve micron-scale displacements, it has found widespread applications in biomedical imaging, industrial fabrication, physics, and astrophysics. In this thesis, we introduce a set of techniques we call computational interferometry, that bring the benefits [...]
Carnegie Mellon University
3D Reconstruction using Differential Imaging
Abstract: 3D reconstruction has been at the core of many computer vision applications, including autonomous driving, visual inspection in manufacturing, and augmented and virtual reality (AR/VR). Despite the tremendous progress made over the years, there remain challenging open-research problems. This thesis addresses three such problems in 3D reconstruction. First, we address the problem of defocus [...]
Carnegie Mellon University
Beyond rigid objects: Data-driven Methods for Manipulation of Deformable Objects
Abstract: Manipulation of deformable objects challenges common assumptions made for rigid objects. Deformable objects have high intrinsic state representation and complex dynamics with high degrees of freedom, making it difficult for state estimation and planning. The completed work can be divided into two parts. In the first part, we explore reinforcement learning (RL) as a [...]
Carnegie Mellon University
Simulation, Perception, and Generation of Human Behavior
Abstract: Understanding and modeling human behavior is fundamental to almost any computer vision and robotics applications that involve humans. In this thesis, we take a holistic approach to human behavior modeling and tackle its three essential aspects --- simulation, perception, and generation. Throughout this thesis, we show how the three aspects are deeply connected and [...]
Carnegie Mellon University
Structured Learning for Robust Robot Manipulation
Abstract: Robust and generalizable robots that can autonomously manipulate objects in semi-structured environments can bring material benefits to society. Data-driven learning approaches are crucial for enabling such systems by identifying and exploiting patterns in semi-structured environments, allowing robots to adapt to novel scenarios with minimal human supervision. However, despite significant prior work in learning for [...]
Simulation-based Planning for Pick-and-Place in Heavy Clutter using Non-prehensile Manipulation
Abstract: Robot manipulation in domestic households, industrial manufacturing and warehouses might require contact-rich interactions with objects in the environment. For pick-and-place style grasping tasks in cluttered scenes, it can be more economical for the robot to rely on non-prehensile actions vis-à-vis deliberate prehensile rearrangement. Non-prehensile actions also let the robot manipulate large and bulky objects [...]
Carnegie Mellon University
Learning with Diverse Forms of Imperfect and Indirect Supervision
Abstract: High capacity Machine Learning (ML) models trained on large, annotated datasets have driven impressive advances in several fields including natural language processing and computer vision, in turn leading to impactful applications of ML in areas such as healthcare, e-commerce, and predictive maintenance. However, obtaining annotated datasets at the scale required for training such models [...]
Carnegie Mellon University
3D Representation Learning for Perception and Prediction: A Modular Yet Highly Integrated Approach
Abstract: Modularized and cascaded autonomy stacks (object detection, then tracking and then trajectory prediction) have been widely adopted in many autonomous systems such as self-driving cars due to its interpretability. In this talk, I advocate the use of such a modular approach but improve its accuracy and robustness by developing different 3D representations for each [...]
Carnegie Mellon University
Understanding Unbalanced Datasets Through Simple Models and Dataset Exploration
Abstract: Computer vision models have proven to be tremendously capable of recognizing and detecting several classes and objects. They succeed in classes widely ranging in type and scale from humans to cans to pens. However, the best performing classes have abundant examples in large-scale datasets today. In unbalanced datasets, where some categories are seen in [...]
Carnegie Mellon University
Self-Supervising Occlusions for Vision
Abstract: Virtually every scene has occlusions. Even a scene with a single object exhibits self-occlusions - a camera can only view one side of an object (left or right, front or back), or part of the object is outside the field of view. More complex occlusions occur when one or more objects block part(s) of [...]
Carnegie Mellon University
Massively Parallelized Lazy Planning Algorithms
Abstract: Search-based planning algorithms enable autonomous agents like robots to come up with well-reasoned long horizon plans to achieve a given task objective. They do so by optimizing a task-specific cost function while respecting the constraints on either the agent (e.g. motion constraints) or the environment (e.g. obstacles). In robotics, such as in motion planning [...]
Run-Time Optimization in the Deep Learning Age
Abstract: In a recovery task one seeks to obtain an estimate of an unknown signal from a set of incomplete measurements. These problems arise in a number of computer vision applications, from image based tasks such as super-resolution and in-painting to 3D reconstruction tasks such as Non-Rigid Structure from Motion and scene flow estimation. Early [...]
Carnegie Mellon University
System Identification and Control of Multiagent Systems Through Interactions
Abstract: This thesis investigates the problem of identifying dynamics models of individual agents of a multiagent system (MAS) and exploiting these models to shape their behavior using robots extrinsic to the MAS. While task-based control of a MAS using onboard controllers of its agents is well studied, we investigate (a) how easy it is for [...]
Carnegie Mellon University
Driving Reconfigurable Unmanned Vehicle Design for Mobility Performance
Abstract: Unmanned ground vehicles are being deployed in increasingly diverse and complex environments. Advances in the field of robotics, including perception technology, computing power, and machine learning, have brought robots from the lab to the real world. Remote and autonomous vehicles are now used to explore volcanoes, caves, pipes, war zones, disaster sites, and even [...]
Towards Large-scale and Long-term Neural Map Representations
Abstract: We address the problem of large-scale and long-term neural map representations. Maps, as our prior understanding toward the environment, provide valuable information for modern robotics applications such as autonomous driving and AR/VR. The size of maps largely affects the end task performance: usually a more detailed map can support better performance, but would cost [...]
Manipulating Objects with Challenging Visual and Geometric Properties
Abstract: Object manipulation is a well-studied domain in robotics, yet manipulation remains difficult for objects with visually and geometrically challenging properties. Visually challenging properties, such as transparency and specularity, break assumptions of Lambertian reflectance that existing methods rely on for grasp estimation. On the other hand, deformable objects such as cloth pose both visual and [...]
Large Scale Dense 3D Reconstruction via Sparse Representations
Abstract: Scene reconstruction systems take in (3D) videos as input, and output 3D models with associated poses for inputs. With the demand of 3D content generation, the technique has been drastically evolving in recent years. For professionals equipped with depth sensors, efficient dense reconstruction systems have become available to efficiently recover scene geometry. For ordinary [...]
Teaching Agent Reward Functions via Demonstrations for Human Inverse Reinforcement Learning
Abstract: For intelligent agents (e.g. robots) to be seamlessly integrated into human society, humans must be able to understand their decision making. For example, the decision making of autonomous cars must be clear to the engineers certifying their safety, passengers riding them, and nearby drivers negotiating the road simultaneously. As an agent's decision making can [...]
Policy Decomposition: Approximate Optimal Control with Suboptimality Estimates
Abstract: Optimal Control is a formulation for designing controllers for dynamical systems by posing it as an optimization problem, whereby the desired long-term behavior of the system is expressed using a cost function. The objective is to compute a policy, i.e. a mapping from the state of the system to its control inputs, that minimizes [...]
Audience-Aware Legibility for Social Navigation
Abstract: Robots often need to communicate their goals to humans when navigating in a shared space to assist observers in anticipating the robot’s future actions. These human observers are often scattered throughout the environment, and each observer only has a partial view of the robot and its movements. A path that non-verbally communicates with multiple [...]
On Sample-Efficient Reinforcement Learning for Nuclear Fusion
Abstract: In many practical applications of reinforcement learning (RL), it is expensive to observe state transitions from the environment. For example, in the problem of plasma control for nuclear fusion, determining the next state for a given state-action pair requires querying an expensive transition function which can lead to many hours of computer simulation or [...]
Carnegie Mellon University
Towards reconstructing non-rigidity from single camera
Abstract: In this proposal, we study how to infer 3D from images captured by a single camera, without assuming the target scenes / objects being static. The non-static setting makes our problem ill-posed and challenging to solve, but is vital in practical applications where target-of-interest is non-static. To solve ill-posed problems, the current trend in [...]
Efficient 3D Representations: Algebraic Surfaces for Differentiable Rendering
Abstract: In this proposal, we show how some classic computer vision tasks can robustly be solved via optimization techniques by using an object representation that is compact and interpretable. Specifically, we explore the applications and benefits of representing 3D objects with an analytical, algebraic function by building an approximate, ray-based differentiable renderer. Our approximate formulation [...]
Continual Robot Learning: Benchmarks and Modular Methods
Zoom Meeting Passcode: 841755 Abstract: The earliest reinforcement learning models were designed to learn one task, specified up-front. However, an agent operating freely in the real world will not in general be granted this luxury, as the demands placed on the agent may change as environments or goals change. We refer to this ever-shifting scenario [...]
Improving Robotic Exploration with Self-Supervision and Diverse Data
Abstract: Reinforcement learning (RL) holds great promise for improving robotics, as it allows systems to move beyond passive learning and interact with the world while learning from these interactions. A key aspect of this interaction is exploration: which actions should an RL agent take to best learn about the world? Prior work on exploration is typically [...]
Combining Offline Reinforcement Learning with Stochastic Multi-Agent Planning for Autonomous Driving
Abstract: Fully autonomous vehicles have the potential to greatly reduce vehicular accidents and revolutionize how people travel and how we transport goods. Many of the major challenges for autonomous driving systems emerge from the numerous traffic situations that require complex interactions with other agents. For the foreseeable future, autonomous vehicles will have to share the [...]
Causal Robot Learning for Manipulation
Abstract: Two decades into the third age of AI, the rise of deep learning has yielded two seemingly disparate realities. In one, massive accomplishments have been achieved in deep reinforcement learning, protein folding, and large language models. Yet, in the other, the promises of deep learning to empower robots that operate robustly in real-world environments [...]
Dense Reconstruction of Dynamic Structures from Monocular RGB Videos
Abstract: We study the problem of 3D reconstruction of {\em generic} and {\em deformable} objects and scenes from {\em casually-taken} RGB videos, to create a system for capturing the dynamic 3D world. Being able to reconstruct dynamic structures from casual videos allows one to create avatars and motion references for arbitrary objects without specialized devices, [...]
Learning via Visual-Tactile Interaction
Abstract: Humans learn by interacting with their surroundings using all of their senses. The first of these senses to develop is touch, and it is the first way that young humans explore their environment, learn about objects, and tune their cost functions (via pain or treats). Yet, robots are often denied this highly informative and [...]
Tactile SLAM: perception for dexterity via vision-based touch
Abstract: Touch provides a direct window into robot-object interaction, free from occlusion and aliasing faced by visual sensing. Collated tactile perception can facilitate contact-rich tasks---like in-hand manipulation, sliding, and grasping. Here, online estimates of object geometry and pose are crucial for downstream planning and control. With significant advances in tactile sensing, like vision-based touch, a [...]
Resource Allocation for Learning in Robotics
Abstract: Robots operating in the real world need fast and intelligent decision making systems. While these systems have traditionally consisted of human-engineered behaviors and world models, there has been a lot of interest in integrating them with data-driven components to achieve faster execution and reduce hand-engineering. Unfortunately, these learning-based methods require large amounts of training [...]
Planning with Dynamics by Interleaving Search and Trajectory Optimization
Abstract: Search-based planning algorithms enable autonomous agents like robots to come up with well-reasoned long-horizon plans to achieve a given task objective. They do so by searching over the graph that results from discretizing the state and action space. However, in robotics, several dynamically rich tasks require high-dimensional planning in the continuous space. For such [...]
Utilizing Panoptic Segmentation and a Locally-Conditioned Neural Representation to Build Richer 3D Maps
Abstract: Advances in deep-learning based perception and maturation of volumetric RGB-D mapping algorithms have allowed autonomous robots to be deployed in increasingly complex environments. For robust operation in open-world conditions however, perceptual capabilities are still lacking. Limitations of commodity depth sensors mean that complex geometries and textures cannot be reconstructed accurately. Semantic understanding is still [...]
Multi-Human 3D Reconstruction from Monocular RGB Videos
Abstract: We study the problem of multi-human 3D reconstruction from RGB videos captured in the wild. Humans have dynamic motion, and reconstructing them in arbitrary settings is key to building immersive social telepresence, assistive humanoid robots, and augmented reality systems. However, creating such a system requires addressing fundamental issues with previous works regarding the data [...]
Learning and Translating Temporal Abstractions across Humans and Robots
Abstract: Humans possess a remarkable ability to learn to perform tasks from a variety of different sources-from language, instructions, demonstration, etc. In each case, they are able to easily extract the high-level strategy to solve the task, such as the recipe of cooking a dish, whilst ignoring irrelevant details, such as the precise shape of [...]
Predicting The Future and Linking the Past: Learning and Constructing Structured Models for Robotic Manipulation
Abstract: Intelligent robotic agents need to reason about the dynamics of their surrounding world, and use such dynamics reasoning to make future predictions for efficient task planning. In addition, it is also desirable for robots to associate past experience in their memories to their current observation, and conduct analogical reasoning to complete tasks at their [...]
Perception for High-Speed Off-Road Driving
Abstract: On-road autonomous driving has seen rapid progress in recent years with driverless vehicles being tested in various cities worldwide. However, this progress is limited to cities with well-established infrastructure and has yet to transfer to off-road regimes with unstructured environments and few paved roads. Advances in high-speed and reliable autonomous off-road driving can unlock [...]
Continual Learning of Compositional Skills for Robust Robot Manipulation
Abstract: Real world robots need to continuously learn new manipulation tasks in a lifelong learning manner. These new tasks often share sub-structures (in the form of sub-tasks, controllers) with previously learned tasks. To utilize these shared sub-structures, we explore a compositional and object-centric approach to learn manipulation tasks. While compositionality in robot manipulation can manifest [...]
Equivalent Policy Sets for Learning Aligned Models and Abstractions
Abstract: Recent successes in model-based reinforcement learning (MBRL) have demonstrated the enormous value that learned representations of environmental dynamics (i.e., models) can impart to autonomous decision making. While a learned model can never perfectly represent the dynamics of complex environments, models that are accurate in the "right” ways may still be highly useful for decision [...]
Adaptive Robotic Assistance through Observations of Human Behavior
Abstract: Assistive robots should take actions that support people's goals. This is especially true as robots enter into environments where personal agency is paramount, such as a person's home. Home environments have a wide variety of "optimal' solutions that depend on personal preference, making it difficult for a robot to know the goal it should [...]
Beyond Pick-and-Place: Towards Dynamic and Contact-rich Motor Skills with Reinforcement Learning
Abstract: Interactions with the physical world are at the core of robotics. However, robotics research, especially in manipulation, has been mainly focused on tasks with limited interactions with the physical world such as pick-and-place or pushing objects on the table top. These interactions are often quasi-static, have predefined or limited sequence of contact events and [...]
Adaptive-Anytime Planning and Mapping for Multi-Robot Exploration in Large Environments
Abstract: Robotic systems are being leveraged to explore environments too hazardous for humans to enter. Robot sensing, compute, and kinodynamic (SCK) capabilities are inextricably tied to the size, weight, and power (SWaP) constraints of the vehicle. When designing a robot team for exploration, the diversity and types of robots used must be carefully considered because [...]
Enabling Data-Efficient Real-World Model-Based Manipulation by Estimating Preconditions for Inaccurate Models
Abstract: This thesis explores estimating and reasoning about model deviation in robot learning for manipulation to improve data efficiency and reliability to enable real-robot manipulation in a world where models are inaccurate but still useful. Existing strategies are presented for improving planning robustness with low amounts of real-world data by an empirically estimated model precondition to guide [...]
Robust Adaptive Reinforcement Learning for Safety Critical Applications via Curricular Learning
Abstract: Reinforcement Learning (RL) presents great promises for autonomous agents. However, when using robots in a safety critical domain, a system has to be robust enough to be deployed in real life. For example, the robot should be able to perform across different scenarios it will encounter. The robot should avoid entering undesirable and irreversible [...]
Towards Photorealistic Dynamic Capture and Animation of Human Hair and Head
Abstract: Realistic human avatars play a key role in immersive virtual telepresence. To reach a high level of realism, a human avatar needs to faithfully reflect human appearance. A human avatar should also be drivable and express natural motions. Existing works have made significant progress on building drivable realistic face avatars, but they rarely include [...]
Eye Gaze for Intelligent Driving
Abstract: Intelligent vehicles have been proposed as one path to increasing vehicular safety and reduce on-road crashes. Driving intelligence has taken many forms, ranging from simple blind spot occupancy or forward collision warnings to lane keeping and all the way to full driving autonomy in certain situations. Primarily, these methods are outward-facing and operate on [...]
Passive Coupling in Robot Swarms
Abstract: In unstructured environments, ant colonies demonstrate remarkable abilities to adaptively form functional structures in response to various obstacles, such as stairs, gaps, and holes. Drawing inspiration from these creatures, robot swarms can collectively exhibit complex behaviors and achieve tasks that individual robots cannot accomplish. Existing modular robot platforms that employ dynamic coupling and decoupling [...]
Learning to Perceive and Predict Everyday Interactions
Abstract: This thesis aims to develop a computer vision system that can understand everyday human interactions with rich spatial information. Such systems can benefit VR/AR to perceive the reality and modify its virtual twin, and robotics to learn manipulation by watching human. Previous methods have been limited to constrained lab environment or pre-selected objects with [...]
Active Vision for Manipulation
Abstract: Decades of research on computer vision has highlighted the importance of active sensing -- where the agent actively controls parameters of the sensor to improve perception. Research on active perception the context of robotic manipulation has demonstrated many novel and robust sensing strategies involving a multitude of sensors like RGB and RGBD cameras, a [...]
Design Iteration of Dexterous Compliant Robotic Manipulators
Abstract: One goal of personal robotics is to have robots in homes performing everyday tasks efficiently to improve our quality of life. Towards this end, manipulators are needed which are low cost, safe around humans, and approach human-level dexterity. However, existing off-the-shelf manipulators are expensive both in cost and manufacturing time, difficult to repair, and [...]
Whisker Sensors for Unstructured Environments
Abstract: As robot applications expand from controllable factory settings to unknown environments, the robots will need a larger breadth of sensors to perceive these complex environments. In this thesis, I focus on developing whisker sensors for robot perception. The inspiration for whisker sensors comes from the biological world, where whiskers serve as tactile and flow [...]
Sparse-view 3D in the Wild
Abstract: Reconstructing 3D scenes and objects from images alone has been a long-standing goal in computer vision. We have seen tremendous progress in recent years, capable of producing near photo-realistic renderings from any viewpoint. However, existing approaches generally rely on a large number of input images (typically 50-100) in order to compute camera poses and [...]
Carnegie Mellon University
Spectral Mapping using Simple Sensors for Micro-Explorers
Abstract: Spectral mapping is an essential task in exploration as it expands our understanding of material composition in an explored region. Although imaging spectrometers are ideal for obtaining spectra to construct spectral maps, their large size, high power consumption, and operational complexity make them impractical for small rovers and limited missions. In contrast, RGB cameras [...]
Simulation-driven vision-based tactile sensor design using Physics Based Rendering
Abstract: Touch is an essential sensing modality for making autonomous robots more dexterous and works collaboratively with humans. With the advent of vision-based tactile sensors, roboticists have tried to incorporate tactile sensors in various robot structures for various robotic manipulation tasks to increase robustness, precision, and reliability. However, the design of vision-based tactile sensors is [...]
Efficient Interactive Learning with Unobserved Confounders
Abstract: Interactive learning systems like self-driving cars, recommender systems, and large language model chatbots are becoming increasingly ubiquitous in everyday life. From a machine learning perspective, the key technical challenge underlying such systems is that rather than simple prediction on i.i.d. data, an interactive learner influences the distribution of inputs it sees via the choices [...]
Learning to Manipulate Using Diverse Datasets
Abstract: Manipulation is a key challenge in the robotic fields that impedes the deployment of robots in real-world scenarios. While notable advancements have been made in solving high/mid level planning problems, such as decomposing tasks (e.g. "bring me a bottle") into primitives (e.g. "pick up bottle"), the acquisition of fundamental manipulation primitives remains a difficult [...]
Unified Control for Over and Fully-Actuated Aerial Vehicles
Abstract: The growing domain of aerial robotics necessitates advancements in the control strategies and robustness of over-actuated and fully-actuated aerial vehicles. This thesis proposal makes contributions to this endeavor by providing in-depth analysis and methodologies concerning these vehicles, control allocation strategies during actuator failures, high-fidelity simulations, and a unified control framework. Our completed work has [...]
Personalized Context-aware Affective Nonverbal Robot Feedback
Abstract: We first consider the problem of estimating context, specifically key features of the human state. We predict engagement-related events in an educational activity before the end of that activity, which could allow the robot to provide feedback early enough to improve the human's experience. We then explore generating nonverbal affective robot behavior by correlating [...]