Stanford AA289/ENGR319 - Robotics and Autonomous Systems Seminar - Stanford Seminar - Toward robust manipulation in complex environments

Dieter Fox UW/NVIDIA February 28, 2020 Over the last years, advances in deep learning and GPU-based computing have enabled significant progress in several areas of robotics, including visual recognition, real-time tracking, object manipulation, and learning-based control. This progress has turned applications such as autonomous driving and delivery tasks in warehouses, hospitals, or hotels into realistic application scenarios. However, robust manipulation in complex settings is still an open research problem. Various research efforts show promising results on individual pieces of the manipulation puzzle, including manipulator control, touch sensing, object pose detection, task and motion planning, and object pickup. In this talk, I will present our recent work in integrating such components into a complete manipulation system. Specifically, I will describe a mobile robot manipulator that moves through a kitchen, can open and close cabinet doors and drawers, detect and pickup objects, and move these objects to desired locations. Our baseline system is designed to be applicable in a wide variety of environments, only relying on 3D articulated models of the kitchen and the relevant objects. I will discuss the design choices behind our approach, the lessons we learned so far, and various research directions toward enabling more robust and general manipulation systems. View the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMeercb-kvGLUrOq4HR6BZD Learn more about our Robotics and Autonomous Systems Graduate Program: https://online.stanford.edu/programs/robotics-and-autonomous-systems-graduate-program