  This paper considers a scenario where a robot and a human operator share the
same workspace, and the robot is able to both carry out autonomous tasks and
physically interact with the human in order to achieve common goals. In this
context, both intentional and accidental contacts between human and robot might
occur due to the complexity of tasks and environment, to the uncertainty of
human behavior, and to the typical lack of awareness of each other actions.
Here, a two stage strategy based on Recurrent Neural Networks (RNNs) is
designed to detect intentional and accidental contacts: the occurrence of a
contact with the human is detected at the first stage, while the classification
between intentional and accidental is performed at the second stage. An
admittance control strategy or an evasive action is then performed by the
robot, respectively. The approach also works in the case the robot
simultaneously interacts with the human and the environment, where the
interaction wrench of the latter is modeled via Gaussian Mixture Models (GMMs).
Control Barrier Functions (CBFs) are included, at the control level, to
guarantee the satisfaction of robot and task constraints while performing the
proper interaction strategy. The approach has been validated on a real setup
composed of a Kinova Jaco2 robot.
