  Collaborative manipulation task often requires negotiation using explicit or
implicit communication. An important example is determining where to move when
the goal destination is not uniquely specified, and who should lead the motion.
This work is motivated by the ability of humans to communicate the desired
destination of motion through back-and-forth force exchanges. Inherent to these
exchanges is also the ability to dynamically assign a role to each participant,
either taking the initiative or deferring to the partner's lead. In this paper,
we propose a hierarchical robot control framework that emulates human behavior
in communicating a motion destination to a human collaborator and in responding
to their actions. At the top level, the controller consists of a set of
finite-state machines corresponding to different levels of commitment of the
robot to its desired goal configuration. The control architecture is loosely
based on the human strategy observed in the human-human experiments, and the
key component is a real-time intent recognizer that helps the robot respond to
human actions. We describe the details of the control framework, and feature
engineering and training process of the intent recognition. The proposed
controller was implemented on a UR10e robot (Universal Robots) and evaluated
through human studies. The experiments show that the robot correctly recognizes
and responds to human input, communicates its intent clearly, and resolves
conflict. We report success rates and draw comparisons with human-human
experiments to demonstrate the effectiveness of the approach.
