PhD Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

Learning Contextual Actions for Heuristic Search-Based Motion Planning

Zoom Link Abstract: Heuristic search-based motion planning can be computationally costly in large state and action spaces. In this work we explore the use of generative models to learn contextual actions for successor generation in heuristic search. We focus on cases where the robot operates in similar environments, i.e. environments drawn from some underlying distribution. [...]

VASC Seminar
Cristian Sminchisescu
Research Scientist / Professor
Google / Lund University

End-to-end Generative 3D Human Shape and Pose Models and Active Human Sensing

Virtual VASC Seminar:  https://cmu.zoom.us/j/249106600 Title:  End-to-end Generative 3D Human Shape and Pose Models and Active Human Sensing Abstract:  I will review some of our recent work in 3d human modeling, synthesis, and active vision. I will present our new, end-to-end trainable nonlinear statistical 3d human shape and pose models of different resolutions (GHUM and GHUMLite) as [...]

PhD Thesis Proposal
Robotics Institute,
Carnegie Mellon University

Safe and Resilient Multi-Robot Systems: Heterogeneity and Human Presence

Zoom Link Abstract: In the mission of a multi-robot team, the large number of robots behave like a system that relies on networking to enable smooth information propagation and inter-robot interaction as the mission evolves in a collective fashion. Key to the success of mission operation demands for safe and reliable robot interactions within the [...]

MSR Speaking Qualifier
Robotics Institute,
Carnegie Mellon University

Michael Tatum – MSR Thesis Talk

Archived Zoom Video Password: 1u%i4YO%   Title: Communications Coverage in Unknown Underground Environments   Abstract:In field robotics, maintaining communications between the user at a stationary basestation and all deployed robots is essential.  This task's difficulty increases when the test environment is underground and the environment is unknown to the operator and robots.  A common approach [...]

MSR Speaking Qualifier
Robotics Institute,
Carnegie Mellon University

Brendan Miller – MSR Thesis Talk

NSH 4305

Zoom Link: https://cmu.zoom.us/j/96617143856 Title: IBB-Net: Fast Iterative Bounding Box Regression for Point Clouds Abstract: Currently, most point cloud based detection pipelines are focused on producing high accuracy results while requiring significant computational resources and a high-end GPU. Our research explores how to reduce the computational overhead by improving a key element of detection: bounding box regression. We [...]

PhD Speaking Qualifier
Robotics Institute,
Carnegie Mellon University

Interactive Weak Supervision – Learning Useful Heuristics for Data Labeling

Zoom Link Abstract: Obtaining large annotated datasets is critical for training successful machine learning models and it is frequently a bottleneck in practice. Weak supervision offers a promising alternative for producing labeled datasets without ground truth annotations by generating probabilistic labels using multiple noisy heuristics. This process can scale to large amounts of data and [...]

PhD Thesis Defense
Robotics Institute,
Carnegie Mellon University

Automated Action Selection and Embodied Simulation for Socially Assistive Robots using Standardized Interactions

Zoom Link Abstract: Robots have the tremendous potential of assisting people in their lives, allowing them to achieve goals that they would not be able to achieve by themselves. In particular, socially assistive robots provide assistance primarily through social interaction, in healthcare, therapy, and education contexts. Despite their potential, current socially assistive robots still lack [...]

VASC Seminar
Bryan Russell
Senior Research Scientist
Adobe Research

Telling Left from Right: Learning Spatial Correspondence Between Sight and Sound

Virtual VASC Seminar:  https://cmu.zoom.us/j/92741882813?pwd=R1R0eGRaeXFHTEF2VWNwY2VIZmU5Zz09 Abstract:  Self-supervised audio-visual learning aims to capture useful representations of video by leveraging correspondences between visual and audio inputs. Existing approaches have focused primarily on matching semantic information between the sensory streams. In my talk, I’ll describe a novel self-supervised task to leverage an orthogonal principle: matching spatial information in the [...]

PhD Thesis Proposal
Robotics Institute,
Carnegie Mellon University

Heuristics for routing and scheduling of Spatio-Temporal type problems in industrial environments

Zoom Link Abstract: Spatio-temporal problems are fairly common in industrial environments. In practice, these problems come with different characteristics and are often very hard to solve optimally. So practitioners prefer to develop heuristics that exploit mathematical structure specific to the problem for obtaining good performance. In this proposal, I will present work on heuristics for [...]