MSR Speaking Qualifier
Robotics Institute,
Carnegie Mellon University

MSR Thesis Talk: Steven Lee

Title: Learning to Represent and Accurately Arrange Food Items   Abstract: Arrangements of objects are commonplace in a myriad of everyday scenarios, such as decorations at one’s home, displays at museums, and plates of food at restaurants. An efficient personal robot should be able to learn how to robustly recreate an arrangement using only a few [...]

MSR Speaking Qualifier
Robotics Institute,
Carnegie Mellon University

MSR Thesis Talk: Amrita Sawhney

Title: Learning to Perceive and Manipulate Diverse Food Materials Through Interaction Abstract: The home kitchen environment presents many challenges for an autonomous cooking robot, such as the deformability of food items, the wide range of material properties of food, and the complex interaction dynamics involved in food manipulation tasks. Material properties are important when interacting [...]

MSR Speaking Qualifier
Robotics Institute,
Carnegie Mellon University

MSR Thesis Talk: Haidar Jamal

Zoom

Title: Localization for Lunar Micro-Rovers   Abstract: This talk presents an avionics and localization system that enables a lunar micro-rover to navigate autonomously. This system is important for the latest class of small, low-powered, and fast robots going to the Moon in search of polar ice. The first component of the system is an Extended [...]

PhD Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

PoseIt: A Visual-Tactile Dataset of Holding Poses for Grasp Stability Analysis

Abstract: When humans grasp objects in the real world, we often move our arm to hold the object in a different pose where we can use it. In contrast, typical lab settings only study the stability of the grasp immediately after lifting, without any subsequent re-positioning of the arm. However, an object’s stability could vary [...]

PhD Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

Planning to Minimize Human and Robot Efforts Over Tasks

Abstract: It is not feasible to pre-program robots a priori for every possible task they may encounter in unstructured domains. Upon encountering a task that a robot can't solve, one common strategy is to teach it new skills via demonstrations. However, demonstrating a task can often be more cumbersome than performing the task directly. This [...]

MSR Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

MSR Thesis Talk: Akash Sharma

Title: Incorporating Semantic Structure in SLAM Abstract: For robots to understand the environment they interact with, a combination of geometric information and semantic information is imperative. In this talk, I propose a fast and scalable Simultaneous Localization and Mapping (SLAM) system that represents indoor scenes as a graph of semantic objects. Leveraging the observation that [...]

VASC Seminar
Arsalan Mousavian
Senior Robotics Research Scientist
NVIDIA

Propelling Robot Manipulation of Unknown Objects using Learned Object Centric Models

Abstract: There is a growing interest in using data-driven methods to scale up manipulation capabilities of robots for handling a large variety of objects. Many of these methods are oblivious to the notion of objects and they learn monolithic policies from the whole scene in image space. As a result, they don’t generalize well to [...]

MSR Speaking Qualifier
Robotics Institute,
Carnegie Mellon University

Yaadhav Raaj MSR Thesis Talk

Title: Exploiting Uncertainty in Triangulation Light Curtains for Object Tracking and Depth Estimation   Abstract: Active sensing through the use of Adaptive Depth Sensors is a nascent field, with potential in areas such as Advanced driver-assistance systems (ADAS). One such class of sensor is the Triangulation Light Curtain, which was developed in the Illumination and Imaging [...]

PhD Thesis Defense
Robotics Institute,
Carnegie Mellon University

Active Vision: Autonomous Aerial Cinematography with Learned Artistic Decision-Making

Abstract: Aerial cinematography is revolutionizing industries that require live and dynamic camera viewpoints such as entertainment, sports, and security. Fundamentally, it is a tool with immense potential to improve human creativity, expressiveness, and sharing of experiences. However, safely piloting a drone while filming a moving target in the presence of obstacles is immensely taxing, often [...]

PhD Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

Fine-Tuning Offline Reinforcement Learning with Model-Based Policy Optimization

Abstract: In offline reinforcement learning (RL), we attempt to learn a control policy from a fixed dataset of environment interactions. This setting has the potential benefit of allowing us to learn effective policies without needing to collect additional interactive data, which can be expensive or dangerous in real-world systems. However, traditional off-policy RL methods tend [...]