PhD Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

Incorporating Robustness into Learning-Based Aircraft Detection and Tracking Systems

NSH 4305

Abstract: In the field of aviation, the Detect and Avoid (DAA) problem deals with incorporating collision avoidance capabilities into current autopilot navigation systems. In order to standardize DAA capabilities, ASTM has published performance requirements to define safe DAA operations of unmanned aircraft systems (UAS). However, the performance of DAA models are entirely dependent on the [...]

Faculty Events

RI Faculty Business Meeting

Newell-Simon Hall 4305

Meeting for RI Faculty. Discussions include various department topics, policies, and procedures. Generally meets weekly.

Faculty Events

RI Faculty Business Meeting

Newell-Simon Hall 4305

Meeting for RI Faculty. Discussions include various department topics, policies, and procedures. Generally meets weekly.

MSR Thesis Proposal
Robotics Institute,
Carnegie Mellon University

MSR Thesis Talk: Siddarth Venkatraman

GHC 4405

Title: Latent Skill Models for Offline Reinforcement Learning Abstract: Offline reinforcement learning (RL) holds promise as a means to learn high-value policies from a static dataset, without the need for further environment interactions. However, a key challenge in offline RL lies in effectively stitching portions of suboptimal trajectories from the static dataset while avoiding extrapolation [...]

VASC Seminar
Santhosh Kumar Ramakrishnan
Ph.D. Candidate
University of Texas at Austin

Predictive Scene Representations for Embodied Visual Search

GHC 6501

Abstract:  My research advances embodied AI by developing large-scale datasets and state-of-the-art algorithms. In my talk, I will specifically focus on the embodied visual search problem, which aims to enable intelligent search for robots and augmented reality (AR) assistants. Embodied visual search manifests as the visual navigation problem in robotics, where a mobile agent must efficiently navigate [...]

MSR Thesis Defense
PhD Student
Robotics Institute,
Carnegie Mellon University

Long-Tailed 3D Detection via Multi-Modal Fusion

NSH 3305

Abstract: Contemporary autonomous vehicle (AV) benchmarks have advanced techniques for training 3D detectors, particularly on large-scale LiDAR data. Surprisingly, although semantic class labels naturally follow a long-tailed distribution, these benchmarks focus on only a few common classes (e.g., pedestrian and car) and neglect many rare classes in-the-tail (e.g., debris and stroller). However, in the real [...]

Faculty Events
Associate Professor
Robotics Institute,
Carnegie Mellon University

TBA

Newell-Simon Hall 4305

MSR Thesis Defense
MSR Student
Robotics Institute,
Carnegie Mellon University

MSR Thesis Talk: Eric Schneider

GHC 4405

Title: Phenotyping and Skeletonization for Agricultural Robotics Abstract: Scientific phenotyping of plants is a crucial aspect of experimental plant breeding. By accurately measuring plant characteristics, phenotyping plays a vital role in the development of new plant varieties that are better adapted to specific environments and have improved yield, quality, and resistance to stress and disease. In [...]

MSR Thesis Defense
MSR Student
Robotics Institute,
Carnegie Mellon University

MSR Thesis Talk: Shivesh Khaitan

Newell-Simon Hall 4305

Zoom Link: https://cmu.zoom.us/j/95273358670?pwd=Z09Jc3g1aDV1dTdTMEVUWUwxcUZPQT09 Meeting ID: 952 7335 8670 Passcode: 050721 Title: Exploring Reinforcement Learning approaches for Safety Critical EnvironmentsAbstract: Reinforcement Learning (RL) has emerged as a powerful paradigm for addressing challenging decision-making and robotic control tasks. By leveraging the principles of trial-and-error learning, RL algorithms enable agents to learn optimal strategies through interactions with an environment. However, [...]

MSR Thesis Defense
MSR Student
Robotics Institute,
Carnegie Mellon University

MSR Thesis Talk: Ravi Tej Akella

NSH 4305

Title: Distributional Distance Classifiers for Goal-Conditioned Reinforcement Learning Abstract: Autonomous systems are increasingly being deployed in stochastic real-world environments. Often, these agents are trying to find the shortest path to a commanded goal. But what does it mean to find the shortest path in stochastic environments, where every strategy has a non-zero probability of failing? At [...]