Student Talks
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 [...]
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 [...]
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
Relationships in instance segmentation and anomaly detection
Abstract: This thesis primarily covers work on two different tasks in computer vision: (1) anomaly detection and (2) instance segmentation. Anomaly detection is an underexplored unsupervised problem that has existed in many fields. On the other hand, instance (and panoptic) segmentation is a supervised problem that can leverage the powerful data and key developments from [...]
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 [...]
MRSD Annual Poster Presentation
Four student teams from the MRSD program will use posters, videos, and hardware to show their project work on robots for room disinfection, search & rescue, increasing human capability via a third arm, and increased-efficiency factory-floor obstacle avoidance.
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 [...]