Talk, MIT, Cambridge, MA, Cambridge, MA
NEMS 2025 was an amazing event featuring the best and brightest minds of New England.
We had over 140 attendees, with 18 different talks, two keynote speakers, and 44 posters.
Thank you to my co-organizers Lael Odhner and Kait Becker.
Talk, Columbia Alumni Association of Boston, Boston, MA
I had so much fun being on the Lions in AI panel last week!
Barnet Sherman moderated a fantastic discussion with a series of expert panelists in AI and myself.
We received thought-provoking questions about AI and society.
Talk, Dr. Waku YouTube Channel, Online
I was featured on Dr. Waku’s channel, discussing the future of robotics and the importance of data-driven methods.
Thank you, Dr. Waku, for having me on. I am really excited about how this turned out!
Talk, Duke University - Mechanical Engineering Department, Durham, NC
In February 2024, I was invited to give a talk at Duke University on Foundation Models for Robotics.
Thank you to Boyuan Chen for hosting my visit.
It was incredibly cool to see the impressive work being done by the professors in the Mechanical Engineering department.
Talk, Podcast hosted by Ding Li, Online
In this episode, we discuss strategies for wrapping up your PhD dissertation and preparing for life after grad school.
Hosted by Ding Li, with guests Henrique Maia and David Watkins.
Talk, Brown University, Providence, RI
I had the great fortune of giving a talk at Brown University about the exciting work we at RAI Institute are doing with Foundation Models.
Thanks to George Konidaris and Stefanie Tellex for inviting me and organizing the event.
Talk, Yannic Kilcher Youtube, Boston, MA
Talk, ROAM Lab Weekly Meeting, New York, New York
Talk, CAIR Lab Weekly Meeting, New York, New York
Talk, IROS 2020, New York, New York
Talk, Columbia University Zoom, New York, New York
Talk, Columbia University, New York, New York
Providing mobile robots with the ability to manipulate objects has, despite decades of research, remained a challenging problem. The problem is approachable in constrained environments where there is ample prior knowledge of the environment and objects that will be manipulated. The challenge is in building systems that scale beyond specific situational instances and gracefully operate in novel conditions. In the past, heuristic and simple rule based strategies were used to accomplish tasks such as scene segmentation or reasoning about occlusion. These heuristic strategies work in constrained environments where a roboticist can make simplifying assumptions about everything from the geometries of the objects to be interacted with, level of clutter, camera position, lighting, and a myriad of other relevant variables. In this thesis we will demonstrate how a system for mobile manipulation can be built that is robust to changes in these variables. This robustness will be enabled by recent simultaneous advances in the fields of Big Data, Deep Learning, and Simulation. The ability of simulators to create realistic sensory data enables the generation of massive corpora of labeled training data for various grasping and navigation based tasks. We will show that it is now possible to build systems that works in the real world trained using deep learning almost entirely on synthetic data. The ability to train and test on synthetic data allows for quick iterative development of new perception, planning and grasp execution algorithms that work in a large number of environments.
Talk, Columbia University, New York, New York
Talk, NERC, Philadelphia, Pennsylvania
Talk, NYU Reading Group, Brooklyn, New York
Talk, Emptor Lightning Talks, New York, New York
Talk, Samsung Research NYC, New York, New York
Presented my work on visual tactile grasping. slide
Talk, Columbia University, New York, New York
Talk, Candidacy Exam, New York, New York
Real world robotics is a multifarious process spanning several fields including simulation, semantic/scene understanding, reinforcement learning, domain randomization, just to name a few. Ideally simulators would accurately capture the real world perfectly in a much faster capacity allowing for a predictive power of how a robot will interact with its environment. Unfortunately, simulators neither have the speed nor accuracy to support this. Simulators, such as Gazebo, Webots, and OpenRave, are supplemented with machine learned models of their environment to solve specific tasks such as scene understanding and path planning. This can be compared to a physical only solution which can be costly in terms of price and time. Advances in virtual reality allow for new ways for humans to provide training data for robotic systems in simulation. Using modern datasets such as SUNCG and Matterport3D we now have more ability than ever to train robots in virtual environments. Through understanding modern applications of simulations, better robotic platforms can be designed to solve some of the most pressing challenges of modern robotics.
Talk, Blackbox Lightning Talks, New York, New York
Talk, Columbia Data Science Day, New York, New York
Talk, Columbia University, Humanoid Robotics, New York, New York
Talk, Columbia University, Cultural Psychology, New York, New York