Sequence-Agnostic Multi-Object Navigation
Nandiraju Gireesh Ayush Agrawal Ahana Datta Snehasis Banerjee Mohan Sridharan Brojeshwar Bhowmick Madhava Krishna
The Multi-Object Navigation (MultiON) task requires a robot to localize an
instance (each) of multiple object classes. It is a fundamental task for an
assistive robot in a home or a factory. Existing methods for MultiON have
viewed this as a direct extension of Object Navigation (ON), the task of
localising an instance of one object class, and are pre-sequenced, i.e., the
sequence in which the object classes are to be explored is provided in advance.
This is a strong limitation in practical applications characterized by dynamic
changes. This paper describes a deep reinforcement learning framework for
sequence-agnostic MultiON based on an actor-critic architecture and a suitable
reward specification. Our framework leverages past experiences and seeks to
reward progress toward individual as well as multiple target object classes. We
use photo-realistic scenes from the Gibson benchmark dataset in the AI Habitat
3D simulation environment to experimentally show that our method performs
better than a pre-sequenced approach and a state of the art ON method extended
to MultiON.