A video of my talk at the University of Toronto with the Q-and-A at the end.
Last week, I was very fortunate to give a talk “at” the University of Toronto in their AI in Robotics Reading Group. It gives a representative overview of my recent research in robotic manipulation. It’s a technical research talk, but still somewhat high-level, so hopefully it should be accessible to a broad range of robotics researchers. I normally feel embarrassed when watching recordings of my talks, since I realize I should have done X instead of Y in so many places. Fortunately I think this one turned out reasonably well. Furthermore, and to my delight, the YouTube / Google automatic captions captured my audio with a high degree of accuracy.
My talk covers these three papers in order:
- Deep Imitation Learning of Sequential Fabric Smoothing From an Algorithmic Supervisor, IROS 2020.
- VisuoSpatial Foresight for Multi-Step, Multi-Task Fabric Manipulation, RSS 2020.
- Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networks, ICRA 2021.
We covered the first two papers in a BAIR Blog post last year. I briefly mentioned the last one in a personal blog post a few months ago, with the accompanying backstory behind how we developed it. A joint Google AI and BAIR Blog post is in progress … I promise!
Regarding that third paper (for ICRA 2021), when making this talk in Keynote, I was finally able to create the kind of animation that shows the intuition for how a Goal-Conditioned Transporter Network works. Using Google Slides is great for drafting talks quickly, but I think Keynote is better for formal presentations.
I thank the organizers (Homanga Bharadhwaj, Arthur Allshire, Nishkrit Desai, and Professor Animesh Garg) for the opportunity, and I also thank them for helping to arrange the two sign language interpreters for my talk. Finally, if you found this talk interesting, I encourage you to view the talks from the other presenters in the series.