  Human teams can be exceptionally efficient at adapting and collaborating
during manipulation tasks using shared mental models. However, the same shared
mental models that can be used by humans to perform robust low-level force and
motion control during collaborative manipulation tasks are non-existent for
robots. For robots to perform collaborative tasks with people naturally and
efficiently, understanding and predicting human intent is necessary. However,
humans are difficult to predict and model. We have completed an exploratory
study recording motion and force for 20 human dyads moving an object in tandem
in order to better understand how they move and how their movement can be
predicted. In this paper, we show how past motion data can be used to predict
human intent. In order to predict human intent, which we equate with the human
team's velocity for a short time horizon, we used a neural network. Using the
previous 150 time steps at a rate of 200 Hz, human intent can be predicted for
the next 50 time steps with a mean squared error of 0.02 (m/s)^2. We also show
that human intent can be estimated in a human-robot dyad. This work is an
important first step in enabling future work of integrating human intent
estimation on a robot controller to execute a short-term collaborative
trajectory.
