The problem of Adaptation from Participation
(AfP) aims to improve the efficiency of a human-robot team by
adapting a robot’s autonomous systems and behaviors based on
command-level input from a human supervisor. As a solution
to AfP, the Adaptive Parameter EXploration (APEX) algorithm
continuously explores the space of all possible parameter
configurations for the robot’s autonomous system in an online
and anytime manner. Guided by information deduced from
the human’s latest intervening commands, APEX is capable
of adapting an arbitrary robot system to dynamic changes in
task objectives and conditions during a session. We explore this
framework within visual navigation contexts where the human-
robot team is tasked with covering or patrolling over multiple
terrain boundaries such as coastlines and roads. We present
empirical evaluations of two separate APEX-enabled systems:
the first, deployed on an aerial robot within a controlled
environment, and the second, on a wheeled robot operating
within a challenging university campus setting.