Opening Cabinets and Drawers in the Real World using a Commodity Mobile
  Manipulator
Arjun Gupta Michelle Zhang Rishik Sathua Saurabh Gupta
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.