GNSS-denied Ground Vehicle Localization for Off-road Environments with Bird’s-eye-view Synthesis
Abstract: Global localization is essential for the smooth navigation of autonomous vehicles. To obtain accurate vehicle states, on-board localization systems typically rely on Global Navigation Satellite System (GNSS) modules for consistent and reliable global positioning. However, GNSS signals can be obstructed by natural or artificial barriers, leading to temporary system failures and degraded state estimation. On the [...]
Scaling up Robot Skill Learning with Generative Simulation
Abstract: Generalist robots need to learn a wide variety of skills to perform diverse tasks across multiple environments. Current robot training pipelines rely on humans to either provide kinesthetic demonstrations or program simulation environments with manually-designed reward functions for reinforcement learning. Such human involvement is an important bottleneck towards scaling up robot learning across diverse [...]
Simulation as a Tool for Conspicuity Measurement
Abstract: The use of unmanned aerial vehicles (UAVs) for time critical tasks is becoming increasingly popular. Operators are expected to use information from these swarms to make real-time and informed decisions. Consequently, detecting and recognizing targets from video is extremely pivotal to the success of these systems. At greater altitudes or with more vehicles, this [...]
VP4D: View Planning for 3D and 4D Scene Understanding
Abstract: View planning plays a critical role by gathering views that optimize scene reconstruction. Such reconstruction has played an important part in virtual production and computer animation, where a 3D map of the film set and motion capture of actors lead to an immersive experience. Current methods use uncertainty estimation in neural rendering of view [...]
Unlocking Generalization for Robotics via Modularity and Scale
Abstract: How can we build generalist robot systems? Looking at fields such as vision and language, the common theme has been large scale end-to-end learning with massive, curated datasets. In robotics, on the other hand, scale alone may not be enough due to the significant multimodality of robotics tasks, lack of easily accessible data and [...]
Automating Annotation Pipelines by leveraging Multi-Modal Data
Abstract: The era of vision-language models (VLMs) trained on large web-scale datasets challenges conventional formulations of “open-world" perception. In this work, we revisit the task of few-shot object detection (FSOD) in the context of recent foundational VLMs. First, we point out that zero-shot VLMs such as GroundingDINO significantly outperform state-of-the-art few-shot detectors (48 vs. 33 AP) [...]
Leveraging Affordances for Accelerating Online RL
Abstract: The inability to explore environments efficiently makes online RL sample-inefficient. Most existing works tackle this problem in a setting devoid of prior information. However, additional affordances may often be cheaply available at the time of training. These affordances include small quantities of demo data, simulators that can reset to arbitrary states and domain specific [...]
Dynamic Route Guidance in Vehicle Networks by Simulating Future Traffic Patterns
Abstract: Roadway congestion leads to wasted time and money and environmental damage. One possible solution is adding more roadway capacity, but this can be impractical especially in urban environments and still may not make up for a poorly-calibrated traffic signal schedule. As such, it is becoming increasingly important to use existing road networks more efficiently. [...]
Safe, Robust and Adaptive Model Learning for Agile Robots: Autonomous Racing
Abstract: In recent years there has been a rapid development in agile robots capable of operating at their limits in dynamic environments. Autonomous racing and recent developments in it also spurred by competitions such as the Indy Autonomous Challenge, A2RL, and F1Tenth have shown how modern autonomous control algorithms are capable of operating racecars at [...]
Improving Lego Assembly with Vibro-Tactile Feedback
Abstract: Robotic manipulation is an important area of research to improve the level of efficiency and autonomy in manufacturing processes. Due to the high precision and repeatability of industrial robot arms, robotic manufacturing tasks are dominated by simple pick, place, and peg insertion actions performed in a highly structured environment. Lego blocks are an excellent [...]
Robots Crossing Boundaries
Abstract: Over the last 50 years, autonomous robots have made the leap from being novel research contributions in university labs to becoming the fundamental technology upon which companies are built. While they traditionally have belonged to the engineering and computer science disciplines, robots have now crossed into other areas of study and research - making impacts in oceanography, geology, archaeology, biomechanics and biology. [...]
DeltaWalker: A Soft, Linearly Actuated Delta Quadruped Robot
Abstract: Quadruped robots offer a versatile solution for navigating complex terrain, making them valuable for applications such as industrial automation or search and rescue. Although quadrupeds are more complex than bipeds, they are easier to balance and control and require fewer joints to actuate compared to hexapods. Traditional quadruped designs, however, often feature complex leg [...]
Propagative Distance Optimization for Constrained Inverse Kinematics
Abstract: This work investigates a constrained inverse kinematic (IK) problem that seeks a feasible configuration of an articulated robot under various constraints such as joint limits and obstacle collision avoidance. Due to the high-dimensionality and complex constraints, this problem is often solved numerically via iterative local optimization. Classic local optimization methods take joint angles as [...]
Advancing Legged Robot Agility: from Video Imitation to GPU Acceleration
Abstract: Achieving human and animal-level agility has been a long-standing goal in robotics research. Recent advancements in numerical optimization and machine learning have pushed legged systems to greater capabilities than ever before, enabling black flips, parkour, and manipulation of heavy objects. Despite these exciting developments, this thesis identifies two key limitations of current legged robot [...]
Model Predictive Control on Resource-Constrained Robots
Abstract: Model predictive control (MPC) is a powerful tool for controlling highly dynamic robotic systems subject to complex constraints. However, it is computationally expensive and often requires a large memory footprint. Larger robotic systems are capable of carrying and powering sophisticated computational hardware onboard. On the other hand, smaller robots typically have faster dynamics that [...]
Enhancing Bipedal Locomotion With Reaction Wheels
Abstract: Legged robot hardware has become more accessible in the last ten years. However, there is still a dearth of low-cost hardware platforms that are open-source and easy to build. With recent developments in accessible manufacturing methods, such as 3D printing, it has become possible to design and manufacture parts without relying on precision machining. [...]
Building Micron: The Next Handheld Manipulator for Microsurgery
Abstract: Robotic assistance is used today in a variety of surgeries as a means of precise, dexterous, and minimally-invasive manipulation. However, practical use in microsurgical environments such as vitreoretinal surgery remains a challenge for the most common mechanically-grounded robotic platforms. Microsurgery requires micron-level accuracy and the ability to manipulate with interaction forces in millinewtons. Vitreoretinal [...]
Towards Estimation, Modeling, and Control of Mixed Material Flows on Variable-Speed Conveyor Belt Systems with Applications in Recycling
Abstract: Whether it is in sorting defects from grain in an agricultural setting, ore from tailings in a mine, or letters in a postal system, the sorting of bulk material has long been a crucial aspect of human industry. Today, in the face of dwindling natural resource deposits and accelerating climate change, a particularly important [...]
Expressive Attentional Communication Learning using Graph Neural Networks
Abstract: Multi-agent reinforcement learning presents unique hurdles such as the non-stationary problem beyond single-agent reinforcement learning that makes learning effective decentralized cooperative policies using an agent's local state extremely challenging. Effective communication to share information and coordinate is vital for agents to work together and solve cooperative tasks, as the ubiquitous evidence of communication in [...]
Estimating Object Importance and Modeling Driver’s Situational Awareness for Intelligent Driving
Abstract: The ability to identify important objects in a complex and dynamic driving environment can help assistive driving systems alert drivers. These assistance systems also require a model of the drivers' situational awareness (SA) (what aspects of the scene they are already aware of) to avoid unnecessary alerts. This thesis builds towards such intelligent driving [...]
Carnegie Mellon University
AI for Human Mobility
Abstract This talk will describe a series of AI and robotics projects aimed at helping people independently move through cities and buildings. Projects include a deployed personalized transit information app, guide robots for people who are blind, and an integrated AI system that assists blind users with guidance and exploration. Specific findings will be presented [...]
Learning for Perception and Strategy: Adaptive Omnidirectional Stereo Vision and Tactical Reinforcement Learning
Abstract: Multi-view stereo omnidirectional distance estimation usually needs to build a cost volume with many hypothetical distance candidates. The cost volume building process is often computationally heavy considering the limited resources a mobile robot has. We propose a new geometry-informed way of distance candidates selection method which enables the use of a very small number [...]
Online-Adaptive Self-Supervised Learning with Visual Foundation Models for Autonomous Off-Road Driving
Abstract: Autonomous robot navigation in off-road environments currently presents a number of challenges. The lack of structure makes it difficult to handcraft geometry-based heuristics that are robust to the diverse set of scenarios the robot might encounter. Many of the learned methods that work well in urban scenarios require massive amounts of hand-labeled data, but [...]
Multimodal Representations for Adaptable Robot Policies in Human-Inhabited Spaces
Abstract: Human beings sense and express themselves through multiple modalities. To capture multimodal ways of human communication, I want to build adaptable robot policies that infer task pragmatics from video and language prompts, reason about sounds and other sensors, take actions, and learn mannerisms of interacting with people and objects. Existing solutions for robot policies [...]
Interleaving Discrete Search and Continuous Optimization for Kinodynamic Motion Planning
Abstract: Motion planning for dynamically complex robotic tasks requires explicit reasoning within constraints on velocity, acceleration, force/torque, and kinematics such as avoiding obstacles. To meet these constraints, planning algorithms must simultaneously make high-level discrete decisions and low-level continuous decisions. For example, pushing a heavy object involves making discrete decisions about contact locations and continuous decisions [...]
Goal-Expressive Movement for Social Navigation: Where and When to Behave Legibly
Abstract: Robots often need to communicate their navigation goals to assist observers in anticipating the robot's future actions. Enabling observers to infer where a robot is going from its movements is particularly important as robots begin to share workplaces, sidewalks, and social spaces with humans. We can use legible motion, or movements that use intentional [...]
Eye Gaze for Intelligent Driving
Abstract: Intelligent vehicles have been proposed as one path to increasing traffic safety and reducing on-road crashes. Driving “intelligence” today takes many forms, ranging from simple blind spot occupancy or forward collision warnings to distance-aware cruise and all the way to full driving autonomy in certain situations. Primarily, these methods are outward-facing and operate on [...]
AI-CARING
AI-CARING is an NSF-sponsored institute, led by Georgia Tech, whose mission is to investigate, develop and evaluate AI technologies to help older adults live independently. The Institute focuses on providing reminders to the older adults and alerting caregivers when necessary, assisting older adults with tasks such as meal preparation, motivating them to exercise, providing conversational [...]
Learning to Perceive and Predict Everyday Interactions
Abstract: This thesis aims to build computer systems to understand everyday hand-object interactions in the physical world – both perceiving ongoing interactions in 3D space and predicting possible interactions. This ability is crucial for applications such as virtual reality, robotic manipulations, and augmented reality. The problem is inherently ill-posed due to the challenges of one-to-many [...]
Sensorized Soft Material Systems with Integrated Electronics and Computing
Abstract: The integration of soft and multifunctional materials in emerging technologies is becoming more widespread due to their ability to enhance or improve functionality in ways not possible using typical rigid alternatives. This trend is evident in various fields. For example, wearable technologies are increasingly designed using soft materials to improve modulus compatibility with biological [...]
Deep Learning for Tactile Sensing: Development to Deployment
Abstract: The role of sensing is widely acknowledged for robots interacting with the physical environment. However, few contemporary sensors have gained widespread use among roboticists. This thesis proposes a framework for incorporating sensors into a robot learning paradigm, from development to deployment, through the lens of ReSkin -- a versatile and scalable magnetic tactile sensor. [...]
Learning and Translating Temporal Abstractions of Behaviour across Humans and Robots
Abstract: Humans are remarkably adept at learning to perform tasks by imitating other people demonstrating these tasks. Key to this is our ability to reason abstractly about the high-level strategy of the task at hand (such as the recipe of cooking a dish) and the behaviours needed to solve this task (such as the behaviour [...]