PhD Thesis Defense
Postdoctoral Fellow
Robotics Institute,
Carnegie Mellon University

Computational Interferometric Imaging

NSH 4305

Abstract: Imaging systems typically accumulate photons that, as they travel from a light source to a camera, follow multiple different paths and interact with several scene objects. This multi-path accumulation process confounds the information that is available in captured images about the scene and makes using these images to infer properties of scene objects, such [...]

Special Talk
Senior Systems Scientist
Robotics Institute,
Carnegie Mellon University

Making AI trustworthy and understandable by clinicians

Newell-Simon Hall 4305

Abstract:  Understandable-AI techniques facilitate to use of AI as a tool by human experts, giving humans insight into how AI decisions are made thereby helping experts discern which AI predictions should or shouldn’t be trusted.  Understandable techniques may be especially useful for applications with insufficient validation data for regulatory approval, for which human experts must remain the final decision [...]

VASC Seminar
Christoph Lassner
Senior Research Scientist
Epic Games

Towards Interactive Radiance Fields

Newell-Simon Hall 3305

Abstract:  Over the last years, the fields of computer vision and computer graphics have increasingly converged. Using the exact same processes to model appearance during 3D reconstruction and rendering has shown tremendous benefits, especially when combined with machine learning techniques to model otherwise hard-to-capture or -simulate optical effects. In this talk, I will give an [...]

PhD Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

Robust and Context-Aware Real-Time Collaborative Robot Handling with Dynamic Gesture Commands

GHC 6501

Abstract: Real-time collaborative robot (cobot) handling is a task where the cobot maneuvers an object under human dynamic gesture commands. Enabling dynamic gesture commands is useful when the human needs to avoid direct contact with the robot or the object handled by the robot. However, the key challenge lies in the heterogeneity in human behaviors [...]

RI Seminar
Dorsa Sadigh
Assistant Professor
Computer Science Electrical Engineering, Stanford University

Learning Representations for Interactive Robotics

Newell-Simon Hall 1305

In this talk, I will be discussing the role of learning representations for robots that interact with humans and robots that interactively learn from humans through a few different vignettes. I will first discuss how bounded rationality of humans guided us towards developing learned latent action spaces for shared autonomy. It turns out this “bounded rationality” is not a [...]

RI Seminar
Russ Tedrake
Professor
Electrical Engineering & Computer Science, MIT

Motion Planning Around Obstacles with Graphs of Convex Sets

1305 Newell Simon Hall

Abstract: In this talk, I'll describe a new approach to planning that strongly leverages both continuous and discrete/combinatorial optimization. The framework is fairly general, but I will focus on a particular application of the framework to planning continuous curves around obstacles. Traditionally, these sort of motion planning problems have either been solved by trajectory optimization [...]

RI Seminar
Jorgen Pedersen
Chief Operating Officer
Sarcos Technology and Robotics Corporation

RE2 Robotics: from RI spinout to Acquisition

1305 Newell Simon Hall

Abstract: It was July 2001.  Jorgen Pedersen founded RE2 Robotics.  It was supposed to be a temporary venture while he figured out his next career move.  But the journey took an unexpected course.  RE2 became a leading developer of mobile manipulation systems.  Fast forward to 2022, RE2 Robotics exited via an acquisition to Sarcos Technology and [...]

PhD Thesis Proposal
PhD Student
Robotics Institute,
Carnegie Mellon University

Equivalent Policy Sets for Learning Aligned Models and Abstractions

GHC 4405

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 [...]