PhD Speaking Qualifier
Carnegie Mellon University
Terrain Perception using Structured Light for Micro-Rovers
Abstract: With continuing advancement in technology, the future of planetary exploration is likely to be dominated by robotic missions. Yet rovers capable of science investigations are slow and bulky with very limited computing which prohibits demonstrating full autonomy. These rovers are also risk averse due to their huge mission cost. However there is a new [...]
Carnegie Mellon University
Analysis of Deadlock in Multirobot Systems
Abstract: Collision avoidance for multirobot systems is a well-studied problem. Recently, control barrier functions (CBFs) have been proposed for synthesizing controllers that guarantee safety while simultaneously encouraging goal stabilization for multiple robots. However, it has been noted that reactive control synthesis methods (such as CBFs) are prone to deadlock, an equilibrium of system dynamics that [...]
Interleaving Graph Search and Trajectory Optimization for Aggressive Quadrotor Flight
Abstract: Quadrotors can achieve aggressive flight by tracking complex maneuvers and rapidly changing directions. Planning for aggressive flight with trajectory optimization could be incredibly fast, even in higher dimensions, and can account for dynamics of the quadrotor, however, only provides a locally optimal solution. On the other hand, planning with discrete graph search can handle [...]
See, Hear, Explore: Curiosity via Audio-Visual Association
Abstract: Exploration is one of the core challenges in reinforcement learning. A common formulation of curiosity-driven exploration uses the difference between the real future and the future predicted by a learned model. However, predicting the future is an inherently difficult task which can be ill-posed in the face of stochasticity. In this work, we introduce [...]
MonoClothCap: Towards Temporally Coherent Clothing Capture from Monocular RGB Video
Abstract: We present a method to capture temporally coherent dynamic clothing deformation from a monocular RGB video input. In contrast to the existing literature, our method does not require a pre-scanned personalized mesh template, and thus can be applied to in-the-wild videos. To constrain the output to a valid deformation space, we build statistical deformation [...]
Policy Decomposition : Approximate Optimal Control with Suboptimality Estimates
Abstract: Owing to the curse of dimensionality, numerically computing global policies to optimal control problems for complex dynamical systems quickly becomes intractable. In consequence, a number of approximation methods have been developed. However, none of the current methods can quantify by how much the resulting control underperforms the elusive globally optimal solution. We propose Policy [...]
Inverse Reinforcement Learning with Explicit Policy Estimates
Abstract: Various methods for solving the inverse reinforcement learning (IRL) problem have been developed independently in machine learning and economics. In particular, the method of Maximum Causal Entropy IRL is based on the perspective of entropy maximization, while related advances in the field of economics instead assume the existence of unobserved action shocks to explain [...]
Learning to Compose Hierarchical Object-Centric Controllers for Robotic Manipulation
Abstract: To perform manipulation tasks in the real world, robots need to operate on objects with various shapes, sizes and without access to geometric models. It is often infeasible to train monolithic neural network policies across such large variance in object properties. Towards this generalization challenge, we propose task-axis controllers, which are defined relative to [...]
Causal Reasoning in Simulation for Structure and Transfer Learning of Robot Manipulation Policies
Abstract: Real-world environments, such as homes, hospitals, and restaurants, often contain many objects that a robot could possibly manipulate. However, for a given manipulation task, only a small number of objects and object properties may actually be relevant. This talk presents CREST (Causal Reasoning for Efficient Structure Transfer), our approach to learn the relevant state [...]
Grasping Transparent, Specular, and Deformable Objects
Abstract: A large body of research exists on grasping for objects with ideal properties like Lambertian reflectance and rigidity. On the other hand, real-world environments contain many objects for which such properties do not hold, such as transparent, specular, and deformable objects. For such objects, new approaches are required to achieve the same level of [...]
PoseIt: A Visual-Tactile Dataset of Holding Poses for Grasp Stability Analysis
Abstract: When humans grasp objects in the real world, we often move our arm to hold the object in a different pose where we can use it. In contrast, typical lab settings only study the stability of the grasp immediately after lifting, without any subsequent re-positioning of the arm. However, an object’s stability could vary [...]
Planning to Minimize Human and Robot Efforts Over Tasks
Abstract: It is not feasible to pre-program robots a priori for every possible task they may encounter in unstructured domains. Upon encountering a task that a robot can't solve, one common strategy is to teach it new skills via demonstrations. However, demonstrating a task can often be more cumbersome than performing the task directly. This [...]
Fine-Tuning Offline Reinforcement Learning with Model-Based Policy Optimization
Abstract: In offline reinforcement learning (RL), we attempt to learn a control policy from a fixed dataset of environment interactions. This setting has the potential benefit of allowing us to learn effective policies without needing to collect additional interactive data, which can be expensive or dangerous in real-world systems. However, traditional off-policy RL methods tend [...]
Modeling Coupled Human-Robot Motion for Provable Safety
Abstract: Guide robots that help users who are blind or low vision navigate through crowds and complex environments show promise for improving accessibility in public spaces. These robots must provide real-time safety guarantees for the users, which requires accurate modeling of their behavior in the context of closely coupled human-robot motion. This model must also [...]
Diminished Reality for Close Quarters Robotic Telemanipulation
Abstract: In robot telemanipulation tasks, the robot itself can sometimes occlude a target object from the user's view. We investigate the potential of diminished reality to address this problem. Our method uses an optical see-through head-mounted display to create a diminished reality illusion that the robot is transparent, allowing users to see occluded areas behind [...]
Learning Compositional Radiance Fields of Dynamic Human Heads
Meeting ID: 942 4671 0665 Passcode: jkhzoom Abstract: Photorealistic rendering of dynamic humans is an important capability for telepresence systems. Recently, neural rendering methods have been developed to create high-fidelity models of humans and objects. Some of these methods do not produce results with high-enough fidelity for driveable human models (Neural Volumes) whereas others have [...]
An Experimental Design Perspective on Model-Based Reinforcement Learning
Abstract: In many practical applications of RL, it is expensive to observe state transitions from the environment. For example, in the problem of plasma control for nuclear fusion, computing 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 dollars of [...]
Learning Model Preconditions for Planning with Multiple Models
Abstract: Different models can provide differing levels of fidelity when a robot is planning. Analytical models are often fast to evaluate but only work in limited ranges of conditions. Meanwhile, physics simulators are effective at modeling complex interactions between objects but are typically more computationally expensive. Learning when to switch between the various models can [...]
Reconstructing common objects to interact with
Abstract: We humans are able to understand 3D shapes of common daily objects and interact with them from a wide range of categories. We understand cups are usually cylinder-like and we can easily predict the shape of one particular cup, both in isolation or even when it is held by a human. We aim to [...]
A causal framework to diagnose and fix issues with doors
Abstract: Many animals, such as ravens, (and a fortiori humans) exhibit a great deal of physical intelligence that allows them to solve complex multi-step physical puzzles. This ability indicates an understanding or a faculty to represent causality and mechanisms, understand when something goes wrong, and figure out how to deal with it. As a step [...]
Designing Whisker Sensors to Detect Multiple Mechanical Stimuli for Robotic Applications
Abstract: Many mammals, such as rats and seals, use their whiskers as versatile mechanical sensors to gain precise information about their surroundings. Whisker-inspired sensors on robotic platforms have shown their potential benefit, improving applications ranging from drone navigation to texture mapping. Despite this, there is a gap between the engineered sensors and many of the [...]