  Collaborative manipulation is inherently multimodal, with haptic
communication playing a central role. When performed by humans, it involves
back-and-forth force exchanges between the participants through which they
resolve possible conflicts and determine their roles. Much of the existing work
on collaborative human-robot manipulation assumes that the robot follows the
human. But for a robot to match the performance of a human partner it needs to
be able to take initiative and lead when appropriate. To achieve such
human-like performance, the robot needs to have the ability to (1) determine
the intent of the human, (2) clearly express its own intent, and (3) choose its
actions so that the dyad reaches consensus. This work proposes a framework for
recognizing human intent in collaborative manipulation tasks using force
exchanges. Grounded in a dataset collected during a human study, we introduce a
set of features that can be computed from the measured signals and report the
results of a classifier trained on our collected human-human interaction data.
Two metrics are used to evaluate the intent recognizer: overall accuracy and
the ability to correctly identify transitions. The proposed recognizer shows
robustness against the variations in the partner's actions and the confounding
effects due to the variability in grasp forces and dynamic effects of walking.
The results demonstrate that the proposed recognizer is well-suited for
implementation in a physical interaction control scheme.
