RSS 2020 VLRRM Workshop

Visual Learning and Reasoning for Robotic Manipulation

Full-day workshop at RSS 2020

Oregon State University at Corvallis, Oregon, USA

July 13, 2020, Pacific Time (PT)

Welcome! Please attend our virtual workshop via this link.

This workshop includes three live events:

Invited Talks (25 min talk + 5 min Q&A)
Spotlight Talks (4 × 5 min pre-recorded videos + 10 min Q&A)
Panel Discussion (60 min)

Schedule

Time (PT)	Invited Speaker	Title
9:15 - 9:30	-	Opening Remarks \| Video \|
9:30 - 10:00	Kostas Daniilidis University of Pennsylvania	The Curious Explorer \| Video \|
10:00 - 10:30	Thomas Funkhouser Google Research	Spatial Action Maps \| Video \|
10:30 - 11:00	Spotlight Talks + Q&A	Latent Space Roadmap for Visual Action Planning Martina Lippi (University of Salerno); Petra Poklukar (KTH); Michael Welle (KTH); Anastasiia Varava (KTH); Hang Yin (KTH); Alessandro Marino (University of Cassino and Southern Lazio); Danica Kragic (KTH) \| PDF \| Video \| Revisiting Grasp Map Representation with a Focus on Orientation in Grasp Synthesis Nikolaos Gkanatsios (DeepLab); Georgia Chalvatzaki (TU Darmstadt); Petros Maragos (National Technical University of Athens); Jan Peters (TU Darmstadt + Max Planck Institute for Intelligent Systems) \| PDF \| Video \| Simple Sensor Intentions for Exploration Tim Hertweck (DeepMind); Martin Riedmiller (DeepMind); Michael Bloesch (Google); Jost Tobias Springenberg (DeepMind); Noah Siegel (DeepMind); Markus Wulfmeier (DeepMind); Roland Hafner (Google DeepMind); Nicolas Heess (DeepMind) \| PDF \| Video \| Goal-Aware Prediction: Learning to Model What Matters Suraj Nair (Stanford University); Silvio Savarese (Stanford University); Chelsea Finn (Stanford University) \| PDF \| Supp \| Video \| Q&A \| Video \|
11:00 - 11:30	Sonia Chernova Georgia Tech / FAIR	Semantic Grasping through Wide and Deep Learning \| Video \|
11:30 - 12:00	Russ Tedrake MIT / Toyota Research Institute	Toward Category-Level Manipulation \| Video \|
12:00 - 1:30	-	Lunch Break
1:30 - 2:00	Pieter Abbeel UC Berkeley	Can Deep Reinforcement Learning from Pixels Be Made as Efficient as from States? \| Video \|
2:00 - 2:30	Fei-Fei Li Stanford University	Octopus, Kittens & Babies: From Seeing to Doing \| Video \|
2:30 - 3:00	Spotlight Talks + Q&A	Cloth Region Segmentation for Robust Grasp Selection Thomas Weng (Carnegie Mellon University); Jianing Qian (Carnegie Mellon University); Brian Okorn (Carnegie Mellon University); Luxin Zhang (Carnegie Mellon University); David Held (Carnegie Mellon University) \| PDF \| Supp \| Video \| Action for Better Prediction Bernadette K Bucher (University of Pennsylvania); Karl Schmeckpeper (University of Pennsylvania); Nikolai Matni (University of Pennsylvania); Kostas Daniilidis (University of Pennsylvania) \| PDF \| Video \| Learning Visual Servo Policies via Planner Cloning Ulrich Viereck (Northeastern University); Kate Saenko (Boston University); Robert Platt (Northeastern University) \| PDF \| Video \| Learning to Plan with Point Cloud Affordances for General-Purpose Dexterous Manipulation Anthony Simeonov (MIT); Yilun Du (MIT); Beomjoon Kim (MIT); Francois Hogan (MIT); Alberto Rodriguez (MIT); Pulkit Agrawal (MIT) \| PDF \| Video \| Q&A \| Video \|
3:00 - 3:30	Spotlight Talks + Q&A	Efficient Adaptation for End-to-End Vision-Based Robotic Manipulation Ryan C Julian (University of Southern California); Benjamin Swanson (Google); Gaurav Sukhatme (University of Southern California); Sergey Levine (Google); Chelsea Finn (Google Brain); Karol Hausman (Google Brain) \| PDF \| Video \| Self-Supervised Goal-Conditioned Pick and Place Coline M Devin (UC Berkeley); Payam Rowghanian (Osaro); Chris Vigorito (Osaro); Will Richards (Osaro); Khashayar Rohanimanesh (Osaro) \| PDF \| Video \| Seeing Through Your Skin: A Novel Visuo-Tactile Sensor for Robotic Manipulation Francois R Hogan (Samsung Electronics), Sahand Rezaei-Shoshtari (Samsung Electronics), Michael Jenkin (Samsung Electronics), Yogesh Girdhar (Samsung Electronics), David Meger (Samsung Electronics), Gregory Dudek (Samsung Electronics) \| PDF \| Video \| kPAM 2.0: Feedback Control for Category-Level Robotic Manipulation Wei Gao (MIT); Russ Tedrake (MIT) \| PDF \| Video \| Q&A \| Video \|
3:30 - 4:00	Dieter Fox University of Washington / NVIDIA	Manipulating Known and Unknown Objects \| Video \|
4:00 - 5:00	Greg Dudek (Moderator) McGill University / Samsung + Ken Goldberg UC Berkeley + Invited Speakers	Panel Discussion \| Video \|

Introduction

Visual perception is essential for achieving robot autonomy in the real world. To perform complex manipulation tasks in unknown environments, a robot needs to actively acquire knowledge through physical interactions and conduct sophisticated reasoning of the observed objects. This invites a series of research challenges in developing computational tools to close the perception-action loop. Given the recent advances in computer vision and deep learning, we look for new potential solutions for performing real-world robotic manipulation in an effective and computationally efficient manner.

We focus on the two parallel themes in this workshop:

How could a robot’s interaction with the physical world facilitate the development of its visual perception?
How a deep understanding of the physical world through visual learning and reasoning could give rise to effective and robust robotic control?

Call for Papers

We're inviting submissions! If you're interested in (remotely) presenting a spotlight talk, please submit a short paper (or extended abstract) to CMT. We suggest extended abstracts of 2 pages in the RSS format. A maximum of 4 pages will be considered. References will not count towards the page limit. The review process is double-blind.

Please note that we have a Best Paper Award. The winner will receive a prize sponsored by Samsung.

Important Dates:

Paper Submission: ~~June 8, 2020~~
Author Notification: ~~June 29, 2020~~
Conference Date: July 13, 2020

Award

Best Paper Award:
kPAM 2.0: Feedback Control for Category-Level Robotic Manipulation.
Wei Gao (MIT); Russ Tedrake (MIT)

Organizers

Kuan Fang Stanford University	David Held CMU	Yuke Zhu UT Austin / NVIDIA	Dinesh Jayaraman Univ. of Pennsylvania
Animesh Garg Univ. of Toronto / NVIDIA	Lin Sun Samsung	Yu Xiang NVIDIA	Greg Dudek McGill / Samsung

Sponsor

Contact

For further information, please contact us at rss20vlrrm [AT] gmail [DOT] com