PhD Thesis Defense
Robotics Institute,
Carnegie Mellon University

Lidar Simulation for Robotic Application Development: Modeling and Evaluation

NSH 1305

Abstract: Given the increase in scale and complexity of robotics, robot application development is challenging in the real world. It may be expensive, unsafe, or impractical to collect data, or test systems, in reality. Simulation provides an answer to these challenges. In simulation, data collection is relatively inexpensive, scenes can be procedurally generated, and state [...]

PhD Thesis Defense
Robotics Institute,
Carnegie Mellon University

Adapting to Context in Robot Perception

NSH 3305

Abstract: The promised future filled with robots sensing and acting intelligently in the world is near fruition, thanks in part to continuous progress in robotic perception. However, a number of challenges remain before robots and their perception systems can be truly reliable. In particular, we must consider what happens when highly complex perception systems designed [...]

PhD Thesis Proposal
Robotics Institute,
Carnegie Mellon University

Depth Imaging for Navigation in Challenging Environments

NSH 1507

Abstract: Depth sensors for robust navigation must measure scenes in darkness, bright light, and in scattering media. Scanning LIDAR devices are the most robust to these conditions, but capture sparse measurements, are slow, and expensive. Consumer depth cameras, on the other hand, are inexpensive and produce dense, high rate depth measurements, but fail in bright [...]

MSR Thesis Defense
Robotics Institute,
Carnegie Mellon University

Learning with Auxiliary Supervision

NSH 1507

Abstract: Supervised learning for high-level vision tasks has advanced significantly over the last decade. One of the primary driving forces for these improvements has been the availability of vast amounts of labeled data. However, annotating data is an expensive and time-consuming process. For example, densely segmenting a natural scene image takes approximately 30 minutes. This mode [...]

MSR Thesis Defense
PhD Student
Robotics Institute,
Carnegie Mellon University

Inverse Reinforcement Learning with Conditional Choice Probabilities

NSH 4513

Abstract: We make an important connection to existing results in econometrics to describe an alternative formulation of inverse reinforcement learning (IRL). In particular, we describe an algorithm to solve the IRL problem, using easy-to-compute estimates of the Conditional Choice Probability (CCP) vector, which is the policy function of an expert integrated over factors econometricians cannot [...]

Staff Events

RI Staff Appreciation Lunch

The Cafe Carnegie 4400 Forbes Avenue, Pittsburgh, PA, United States

Private Event: By invitation only The annual RI administrative staff appreciation lunch.  This will be a nice time to relax with colleagues and enjoy a good meal.  There is no formal program per se.  Hope to see you there!