PhD Thesis Defense
Robotics Institute,
Carnegie Mellon University

Heuristic Search Based Planning by Minimizing Anticipated Search Efforts

Abstract: We focus on relatively low dimensional robot motion planning problems, such as planning for navigation of a self-driving vehicle, unmanned aerial vehicles (UAVs), and footstep planning for humanoids. In these problems, there is a need for fast planning, potentially compromising the solution quality. Often, we want to plan fast but are also interested in [...]

RI Seminar
Systems Scientist
Robotics Institute,
Carnegie Mellon University

Robotic Cave Exploration for Search, Science, and Survey

1305 Newell Simon Hall

Abstract: Robotic cave exploration has the potential to create significant societal impact through facilitating search and rescue, in the fight against antibiotic resistance (science), and via mapping (survey). But many state-of-the-art approaches for active perception and autonomy in subterranean environments rely on disparate perceptual pipelines (e.g., pose estimation, occupancy modeling, hazard detection) that process the same underlying sensor data in different [...]

VASC Seminar
Alexander Richard
Research Scientist
Reality Labs Research

Audio-Visual Learning for Social Telepresence

Newell-Simon Hall 3305

Abstract Relationships between people are strongly influenced by distance. Even with today’s technology, remote communication is limited to a two-dimensional audio-visual experience and lacks the availability of a shared, three-dimensional space in which people can interact with each other over the distance. Our mission at Reality Labs Research (RLR) in Pittsburgh is to develop such [...]

Faculty Events
Systems Scientist
Robotics Institute,
Carnegie Mellon University

An autonomous navigation system that could hopefully support RI research

Newell-Simon Hall 4305

I will show a few videos as the key results of our research in the last several years. These results span the scope of state estimation, mapping, autonomous navigation, and exploration. While these results illustrate separate pieces of work, the underlying modules contribute to a final, integrated autonomy system in the end. I will show a simulation [...]

PhD Thesis Proposal
PhD Student
Robotics Institute,
Carnegie Mellon University

Combining Offline Reinforcement Learning with Stochastic Multi-Agent Planning for Autonomous Driving

GHC 4405

Abstract: Fully autonomous vehicles have the potential to greatly reduce vehicular accidents and revolutionize how people travel and how we transport goods. Many of the major challenges for autonomous driving systems emerge from the numerous traffic situations that require complex interactions with other agents. For the foreseeable future, autonomous vehicles will have to share the [...]

Special Events

Argo Poster Session

Newell Simon Hall Atrium

Join us for an opportunity to see what Center students have been working on.  Check out an Argo AI self-driving car in person, and grab some free appetizers, soft drinks, and Argo AI swag! All are welcome to attend.

VASC Seminar
Postdoctoral Fellow
Robotics Institute,
Carnegie Mellon University

Representations in Robot Manipulation: Learning to Manipulate Ropes, Fabrics, Bags, and Liquids

3305 Newell-Simon Hall

Abstract: The robotics community has seen significant progress in applying machine learning for robot manipulation. However, much manipulation research focuses on rigid objects instead of highly deformable objects such as ropes, fabrics, bags, and liquids, which pose challenges due to their complex configuration spaces, dynamics, and self-occlusions. To achieve greater progress in robot manipulation of [...]

PhD Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

Human-to-Robot Imitation in the Wild

NSH 4305

Abstract: In this talk, I approach the problem of learning by watching humans in the wild. While traditional approaches in Imitation and Reinforcement Learning are promising for learning in the real world, they are either sample inefficient or are constrained to lab settings. Meanwhile, there has been a lot of success in processing passive, unstructured human [...]

RI Seminar
Soon-Jo Chung
Bren Professor of Aerospace and Control and Dynamical Systems
Department of Aerospace , Caltech

Safe and Stable Learning for Agile Robots without Reinforcement Learning

1305 Newell Simon Hall

Abstract: My research group (https://aerospacerobotics.caltech.edu/) is working to systematically leverage AI and Machine Learning techniques towards achieving safe and stable autonomy of safety-critical robotic systems, such as robot swarms and autonomous flying cars. Another example is LEONARDO, the world's first bipedal robot that can walk, fly, slackline, and skateboard. Stability and safety are often research problems [...]