PhD Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

Learning to perform dynamic and interactive tasks using structural and algorithmic priors

NSH 3002

Abstract: Everyday human tasks such as picking up an object in one smooth motion, pushing a heavy door using the momentum of our bodies or pushing off a wall to quickly turn a corner involve complex dynamic interactions between the human and the environment, as well as switching dynamics when the robot makes and breaks [...]

Special Events

The Robotics Institute Semi-formal

All Robotics Institute faculty, students, visitors and staff are invited with to attend. One guest per person. RSVP required. Please check your emails for the e-vite and RSVP link. Please contact Debbie Tobin, dmz@cs.cmu.edu, with any questions.

PhD Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

Simple Shape Descriptors for Retinal Surface Estimation using a Laser-Aiming Beam

Abstract: Retinal surgery procedures like epiretinal membrane peeling and retinal vein cannulation require surgeons to manipulate very delicate structures in the eye with little room for error. Many robotic surgery systems have been developed to help surgeons and enforce safeguards during these demanding procedures. One essential piece of information that is required to create and [...]

PhD Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

Affective Robot Behavior Improves Learning in a Sorting Game

GHC 4405

Abstract: Nonverbal communication in the field of education can allow teachers to emotionally support their students and improve educational experience and performance. Robot nonverbal movements have been shown to improve both subjective experiences and task performance, and this work investigates whether affective robot behavior can improve human learning. This is tested using an online sorting [...]

PhD Thesis Proposal
PhD Student
Robotics Institute,
Carnegie Mellon University

Policy Decomposition: Approximate Optimal Control with Suboptimality Estimates

NSH 3305

Abstract: Optimal Control is a formulation for designing controllers for dynamical systems by posing it as an optimization problem, whereby the desired long-term behavior of the system is expressed using a cost function. The objective is to compute a policy, i.e. a mapping from the state of the system to its control inputs, that minimizes [...]

PhD Thesis Proposal
PhD Student
Robotics Institute,
Carnegie Mellon University

Audience-Aware Legibility for Social Navigation

Abstract: Robots often need to communicate their goals to humans when navigating in a shared space to assist observers in anticipating the robot’s future actions. These human observers are often scattered throughout the environment, and each observer only has a partial view of the robot and its movements. A path that non-verbally communicates with multiple [...]

Special Events

Commencement Celebration

The Pittsburgh Golf Club 5280 Northumberland Street, Pittsburgh, PA, United States

Special Events

CMU Community Picnic

As shared during President Jahanian’s recent town hall discussions, the CMU Community Picnic is returning on May 18 (11:30 am to 1:30 pm). The Office of Human Resources, in partnership with Staff Council and the Office of the President, sponsors and organizes this yearly celebration as a thank you for the hard work and contributions [...]

Faculty Events
Raj Reddy Assistant Professor in Robotics
Robotics Institute,
Carnegie Mellon University

Generalization for Robot Learning In The Wild

Newell-Simon Hall 4305

Abstract: How can we train a robot that can generalize to perform thousands of tasks in thousands of environments? This question underscores the holy grail of robot learning, more generally machine learning, research. Current AI systems are incredibly specific in that they only perform the tasks they are trained for and are miserable at generalization. [...]

PhD Thesis Proposal
PhD Student
Robotics Institute,
Carnegie Mellon University

On Sample-Efficient Reinforcement Learning for Nuclear Fusion

NSH 4305

Abstract: In many practical applications of reinforcement learning (RL), it is expensive to observe state transitions from the environment. For example, in the problem of plasma control for nuclear fusion, determining the next state for a given state-action pair requires querying an expensive transition function which can lead to many hours of computer simulation or [...]