VASC Seminar
Nicholas Carlini
Research Scientist
Google

Deep Learning: (still) Not Robust

Abstract: One of the key limitations of deep learning is its inability to generalize to new domains. This talk studies recent attempts at increasing neural network robustness to both natural and adversarial distribution shifts. Robustness to adversarial examples, inputs crafted specifically to fool machine learning models, are arguably the most difficult type of domain shift. [...]

RI Seminar
Brittany A. Duncan
Assistant Professor
Computer Science & Engineering, University of Nebraska-Lincoln

Drones in Public: distancing and communication with all users

Abstract:  This talk will focus on the role of human-robot interaction with drones in public spaces and be focused on two individual research areas: proximal interactions in shared spaces and improved communication with both end-users and bystanders. Prior work on human-interaction with aerial robots has focused on communication from the users or about the intended direction [...]

VASC Seminar
Zoltán Ádám Milacski
PhD Candidate
ELTE Eötvös Loránd University

End-to-End ‘One Networks’: Learning Regularizers for Least Squares via Deep Neural Networks

Abstract: Linear Restoration Problems (or Linear Inverse Problems) involve reconstructing images or videos from noisy measurement vectors. Notable examples include denoising, inpainting, super-resolution, compressive sensing, deblurring and frame prediction. Often, multiple such tasks should be solved simultaneously, e.g., through Regularized Least Squares, where each individual problem is underdetermined (overcomplete) with infinitely many solutions from which [...]

RI Seminar
Chelsea Finn
Assistant Professor
Computer Science & Electrical Engineering, Stanford University

Data Scalability for Robot Learning

Abstract: Recent progress in robot learning has demonstrated how robots can acquire complex manipulation skills from perceptual inputs through trial and error, particularly with the use of deep neural networks. Despite these successes, the generalization and versatility of robots across environment conditions, tasks, and objects remains a major challenge. And, unfortunately, our existing algorithms and [...]

RI Seminar
Raj Reddy Assistant Professor in Robotics
Robotics Institute,
Carnegie Mellon University

Learning to Generalize beyond Training

Abstract: Generalization, i.e., the ability to adapt to novel scenarios, is the hallmark of human intelligence. While we have systems that excel at cleaning floors, playing complex games, and occasionally beating humans, they are incredibly specific in that they only perform the tasks they are trained for and are miserable at generalization. One of the [...]

PhD Thesis Proposal
Robotics Institute,
Carnegie Mellon University

Planning and Execution using Inaccurate Models with Provable Guarantees on Task Completeness

Abstract: Modern planning methods are effective in computing feasible and optimal plans for robotic tasks when given access to accurate dynamical models. However, robots operating in the real world often face situations that cannot be modeled perfectly before execution. Thus, we only have access to simplified but potentially inaccurate models. This imperfect modeling can lead [...]

VASC Seminar
Sheng-Yu Wang
PhD Student
CMU

Detecting Image Synthesis — Shallow and Deep

Abstract: The proliferation of synthetic media are subject to malicious usages such as disinformation campaigns, posing potential threats to media integrity and democracy. A way to combat this is developing forensics algorithms to identify manipulated media. In the beginning of the talk, I will discuss how one can train a model to detect photos manipulated [...]

RI Event
Robotics Institute,
Carnegie Mellon University

Shreyas Srivatchan – MSR Thesis Talk

Zoom

Zoom link: https://cmu.zoom.us/j/92767964421?pwd=N0NqRXZ5M04zQUhObklyZ3ZTL29jZz09 Meeting ID: 927 6796 4421 Password: password   Title: Development of a balancing robot as an indoor service agent   Abstract: This work presents a robotic system that can navigate human environments, respond to speech commands, and perform simple tasks. To achieve this, a ballbot-type robot that balances and navigates on a single spherical [...]

PhD Thesis Defense
Robotics Institute,
Carnegie Mellon University

Constraint-Based Coverage Path Planning: A Novel Approach to Achieving Energy-Efficient Coverage

Abstract: Despite substantial technological progress that has driven the proliferation of robots across various industries and aspects of our lives, the lack of a decisive breakthrough in energy storage capabilities has restrained this trend, particularly with respect to mobile robots designed for use in unstructured and unknown field environments. The fact that these domains are [...]