학술논문

Opening Cabinets and Drawers in the Real World using a Commodity Mobile Manipulator
Document Type
Working Paper
Source
Subject
Computer Science - Robotics
Computer Science - Artificial Intelligence
Computer Science - Computer Vision and Pattern Recognition
Computer Science - Machine Learning
Language
Abstract
Pulling open cabinets and drawers presents many difficult technical challenges in perception (inferring articulation parameters for objects from onboard sensors), planning (producing motion plans that conform to tight task constraints), and control (making and maintaining contact while applying forces on the environment). In this work, we build an end-to-end system that enables a commodity mobile manipulator (Stretch RE2) to pull open cabinets and drawers in diverse previously unseen real world environments. We conduct 4 days of real world testing of this system spanning 31 different objects from across 13 different real world environments. Our system achieves a success rate of 61% on opening novel cabinets and drawers in unseen environments zero-shot. An analysis of the failure modes suggests that errors in perception are the most significant challenge for our system. We will open source code and models for others to replicate and build upon our system.
Comment: Project webpage: https://arjung128.github.io/opening-cabinets-and-drawers