PhD Speaking Qualifier
Carnegie Mellon University
Online, Interactive User Guidance for High-dimensional, Constrained Motion Planning
Abstract: We consider the problem of planning a collision-free path for a high-dimensional robot. Specifically, we suggest a planning framework where a motion-planning algorithm can obtain guidance from a user. In contrast to existing approaches that try to speed up planning by incorporating experiences or demonstrations ahead of planning, we suggest to seek user guidance [...]
Carnegie Mellon University
Robot Task Execution by Policy Adaptation and Switching Among Multiple Tasks
Abstract: While mobile robots reliably perform service tasks by accurately localizing and safely navigating while avoiding obstacles, they do not respond in any other way to their surroundings. In this work, we introduce two methods that enable the robots to be more responsive to their environment, including humans and other robots. The first algorithm enables [...]
Carnegie Mellon University
Persistent Multi-Robot Mapping in an Uncertain Environment
Abstract: We present a system that addresses the challenge of concurrently mapping, scheduling, and deploying a team of energy-constrained robots to persistently cover an unknown and potentially dynamic environment. This system can passively maintain an accurate representation of occupied space, allowing robots reliable access for monitoring, study, or search and rescue. Current state-of-the-art algorithms only [...]
Carnegie Mellon University
Direct Drive Hands: Force-Motion Transparency in Gripper Design
Abstract: The Direct Drive Hand (DDHand) project is exploring a new design philosophy for grippers. The conventional approach is to prioritize clamping force, leading to high gear ratios, slow motion, and poor transmission of force/motion signals. Instead, the DDHand prioritizes transparency: we view the gripper as a signal transmission channel, and seek high-bandwidth, high-fidelity transmission [...]
Carnegie Mellon University
Learning to Align without Geometric Supervision
Abstract: Extracting geometric information from image data is a highly nonlinear problem that exhibits in a number of visual recognition tasks such as object localization, facial landmark tracking and human pose estimation. Successful alignment across image data often serves as a crucial component in making them possible. In this talk, I will present how one [...]
Carnegie Mellon University
Towards Safe and Robust Behavior Mixing for Multi-Robot Systems
Abstract: Multi-robot systems have been widely studied for extending its capability of accomplishing complex tasks through cooperative behaviors. In large-scale multi-robot behavior mixing, the heterogeneous robotic team executes simultaneously multiple behaviors or sequences of behaviors with various task-prescribed controllers in real time to increase efficiency in parallel tasks. Key to the success of behavior mixing [...]
Carnegie Mellon University
Speeding Up Search-based Motion Planning Via Conservative Heuristics
Abstract: Weighted A* search (wA*) is a popular tool for robot motion-planning. Its efficiency however depends on the quality of heuristic function used. In fact, it has been shown that the correlation between the heuristic function and the true cost-to-goal significantly affects the efficiency of the search, when used with a large weight on the [...]
Carnegie Mellon University
Toward a New Type of Agile and Dexterous Mobile Manipulator
Abstract: Mobile robot bases have been developed over many decades, but only recently have researchers added arms to these bases, opening up the rich field of mobile manipulation. Most of these robots either need wide, heavy, statically-stable bases that may or may not be omnidirectional to support the arms and provide stability. Such robot bases, [...]
Carnegie Mellon University
Dexterous Manipulation via Simple Robot Hands
Abstract: Most of the industrial robotic applications nowadays can only deal with pick-and-place manipulation, in which fixed graspings are the only interactions between the object and the robot hand. Simple hands, such as pinch grippers and suction cups, suffice to accomplish such tasks. However, there exist many unsolved automation problems where more dexterous manipulations are [...]
Carnegie Mellon University
Contrastive View Predictive Learning with 3D-Bottlenecked RNNs
Abstract: In this talk, I will describe our recent work on neural architectures for visual recognition, which use 3D not as input nor as the desired output space, but rather as the bottleneck of the learned representations. We consider embodied agents moving in otherwise static worlds equipped with these architectures; they learn 3D visual feature [...]