RI Event
Omry Yadan
Research Engineer
Facebook AI Research

Vision Tool Seminar: Hydra

3305 Newell-Simon Hall

Abstract: Hydra is an open-source Python framework developed at FAIR that aims to reduce the amount of boilerplate code in research and other complex applications. The key feature is the ability to dynamically create a hierarchical configuration by composition and override it through config files and the command line. The name Hydra comes from its [...]

RI Seminar
Raquel Urtasun
Chief Scientist & Head
Uber Advanced Technologies Group Toronto

CANCELLED

CIC Building Room 1201

VASC Seminar
Benjamin Schmidt
President and Co-Founder
RoadBotics

Building Trust in Real World Applications of Vision Based Machine Learning

GHC 6501

Abstract:  In all machine learning problems, there is an explicit trade off between cost and benefit. In real world vision problems, this optimization becomes increasingly difficult since those trade offs directly impact technology and product development as well as business strategy. For any successful business case, it is critical that the cost/benefit trade offs in [...]

PhD Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

Tendon Driven Foam Hands

GHC 6501

Abstract: There has been great progress in soft robot design, manufacture, and control in recent years, and soft robots are a tool of choice for safe and robust handling of objects in conditions of uncertainty. Still, dexterous in-hand manipulation using soft robots remains a challenge. This talk introduces a novel class of soft robots in [...]

VASC Seminar
Partha Pratim Talukdar
Associate Professor
IIScBangalore / Founder, KENOME

Knowledge Infused Deep Learning

Newell-Simon Hall 4305

Abstract:  This talk is motivated by the following thesis: Background knowledge is key to intelligent decision making. While deep learning methods have made significant strides over the last few years, they often lack the context in which they operate. Knowledge Graphs (and more generally multi-relational graphs) provide a flexible framework to capture and represent knowledge [...]

PhD Speaking Qualifier
Robotics Institute,
Carnegie Mellon University

Towards a Good Representation For Reinforcement Learning

WEH 5421

Abstract: Deep reinforcement learning has achieved many successes over the recent years. However, its high sample complexity and the difficulty in specifying a reward function have limited its application. In this talk, I will take a representation learning perspective towards these issues. Is it possible to map from the raw observation, potentially in high dimension, [...]

RI Seminar
Sarjoun Skaff
Co-Founder & CTO
Bossa Nova Robotics

Yes, That’s a Robot in Your Grocery Store. Now what?

CIC Building Room 1201

Abstract: Retail stores are becoming ground zero for indoor robotics. Fleet of different robots have to coexist with each others and humans every day, navigating safely, coordinating missions, and interacting appropriately with people, all at large scale. For us roboticists, stores are giant labs where we're learning what doesn't work and iterating. If we get [...]

VASC Seminar
Georgios Pavlakos
PhD Student
University of Pennsylvania

Learning to Reconstruct 3D Humans

GHC 6501

Abstract:  Recent advances in 2D perception have led to very successful systems, able to estimate the 2D pose of humans with impressive robustness. However, our interactions with the world are fundamentally 3D, so to be able to understand, explain and predict these interactions, it is crucial to reconstruct people in 3D. In this talk, I [...]

PhD Thesis Proposal
Robotics Institute,
Carnegie Mellon University

Eye Gaze for Assistive Manipulation

NSH 4305

Abstract: Full robot autonomy is the traditional goal of robotics research. To work in a human-inhabited world, however, robots will often need to collaborate with humans. Many scenarios require human users to teleoperate robots to perform tasks, a paradigm that appears everywhere from space exploration, to disaster recovery, to assistive robotics. This collaboration enables tasks [...]

RI Seminar
Scott Niekum
Assistant Professor
Department of Computer Science, University of Texas at Austin

CANCELLED

Abstract: Before learning robots can be deployed in the real world, it is critical that probabilistic guarantees can be made about the safety and performance of such systems.  In recent years, safe reinforcement learning algorithms have enjoyed success in application areas with high-quality models and plentiful data, but robotics remains a challenging domain for scaling [...]