Vision, Touch and Motion: On the Value of Multimodal Data in Robot Manipulation and How to Leverage it
, Dept. of Computer Science, Stanford University
Date: Tuesday, December 11, 2018
Time: 3:00 PM to 4:00 PM
Location: Seminar Room G882 (Hewlett Room)
Event Type: Seminar
Host: Josh Tenenbaum, BCS/CSAIL, MIT
Contact: Sholei Croom, firstname.lastname@example.org
Speaker URL: None
TALK: Jeannette Bohg - Vision, Touch and Motion: On the Value of Multimodal Data in Robot Manipulation and How to Leverage it
Recent approaches in robotics follow the insight that perception is facilitated by physical interaction with the environment. First, interaction creates a rich sensory signal that would otherwise not be present. And second, knowledge of the regularity in the combined space of sensory data and action parameters facilitate the prediction and interpretation of the signal.
In this talk, I will focus on what this rich sensory signal may consist of and how it can be leveraged for better perception and manipulation. I will start with our recent work that exploits RGB, Depth and Motion to perform instance segmentation of an unknown number of simultaneously moving objects. The underlying model estimates dense, per-pixel scene flow that is then followed by clustering in motion trajectory space. We show how this outperforms state-of-the-art in scene flow estimation and multi-object segmentation.
Furthermore, I will present our recent work on fusing vision and touch for contact-rich manipulation tasks. It is non-trivial to manually design a robot controller that combines modalities with very different characteristics. While deep reinforcement learning has shown success in learning control policies for high-dimensional inputs, these algorithms are generally intractable to deploy on real robots due to sample complexity. We use self-supervision to learn a compact and multimodal representation of visual and haptic sensory inputs, which can then be used to improve the sample efficiency of policy learning. I present experiments on a peg insertion task where the learned policy generalises over different geometry, configurations, and clearances, while being robust to external perturbations.
Jeannette Bohg is an Assistant Professor of Computer Science at Stanford University. Her research focuses on perception and learning for autonomous robotic manipulation and grasping. She is specifically interesting in developing methods that are goal-directed, real-time and multi-modal such that they can provide meaningful feedback for execution and learning. Jeannette was a group leader at the MPI for Intelligent Systems until September 2017. Before joining the Autonomous Motion lab of MPI-IS in January 2012, Jeannette was a PhD student at the Computer Vision and Active Perception lab (CVAP) at KTH in Stockholm. Her thesis on Multi-modal scene understanding for Robotic Grasping was supervised by Prof. Danica Kragic. She studied at Chalmers in Gothenburg and at the Technical University in Dresden where she received her Master in Art and Technology and her Diploma in Computer Science, respectively.
AI & Machine Learning, Graphics & Vision, Robotics
Created by Jiajun Wu at Tuesday, November 27, 2018 at 2:17 PM.