Scaling Robot Learning for Long-Horizon Manipulation Tasks with Language, Logic and Youtube

Scaling Robot Learning for Long-Horizon Manipulation Tasks with Language, Logic and Youtube (via Zoom)

Abstract:  My long-term research goal is enable real robots to manipulate any kind of object such that they can perform many different tasks in a wide variety of application scenarios such as in our homes, in hospitals, warehouses, or factories. Many of these tasks will require long-horizon reasoning and sequencing of skills to achieve a goal state. While learning approaches promise generalization beyond what the robot has seen during training, they require large data collection - a challenge when operating on real robots and specifically for long-horizon tasks. In this talk, I will present our work on enabling long-horizon reasoning on real robots for many different long-horizon tasks that can be solved by sequencing a large variety of composable skill primitives. We approach this problem from many different angles such as (i) using large-scale, language-annotated video datasets as a cheap data source for skill learning; (ii) using the same dataset for predict grounding thereby enabling closed-loop, symbolic task planning and (iii) sequencing learning skill primitives. 

Bio:  Jeannette Bohg is an Assistant Professor of Computer Science at Stanford University. She was a group leader at the Autonomous Motion Department (AMD) of the MPI for Intelligent Systems until September 2017. Before joining AMD in January 2012, Jeannette Bohg was a PhD student at the Division of Robotics, Perception and Learning (RPL) at KTH in Stockholm. In her thesis, she proposed novel methods towards multi-modal scene understanding for robotic grasping. She also 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. Her research focuses on perception and learning for autonomous robotic manipulation and grasping. She is specifically interested in developing methods that are goal-directed, real-time and multi-modal such that they can provide meaningful feedback for execution and learning. Jeannette Bohg has received several Early Career and Best Paper awards, most notably the 2019 IEEE Robotics and Automation Society Early Career Award and the 2020 Robotics: Science and Systems Early Career Award.