29,637 research outputs found
Early Turn-taking Prediction with Spiking Neural Networks for Human Robot Collaboration
Turn-taking is essential to the structure of human teamwork. Humans are
typically aware of team members' intention to keep or relinquish their turn
before a turn switch, where the responsibility of working on a shared task is
shifted. Future co-robots are also expected to provide such competence. To that
end, this paper proposes the Cognitive Turn-taking Model (CTTM), which
leverages cognitive models (i.e., Spiking Neural Network) to achieve early
turn-taking prediction. The CTTM framework can process multimodal human
communication cues (both implicit and explicit) and predict human turn-taking
intentions in an early stage. The proposed framework is tested on a simulated
surgical procedure, where a robotic scrub nurse predicts the surgeon's
turn-taking intention. It was found that the proposed CTTM framework
outperforms the state-of-the-art turn-taking prediction algorithms by a large
margin. It also outperforms humans when presented with partial observations of
communication cues (i.e., less than 40% of full actions). This early prediction
capability enables robots to initiate turn-taking actions at an early stage,
which facilitates collaboration and increases overall efficiency.Comment: Submitted to IEEE International Conference on Robotics and Automation
(ICRA) 201
Prediction of Human Trajectory Following a Haptic Robotic Guide Using Recurrent Neural Networks
Social intelligence is an important requirement for enabling robots to
collaborate with people. In particular, human path prediction is an essential
capability for robots in that it prevents potential collision with a human and
allows the robot to safely make larger movements. In this paper, we present a
method for predicting the trajectory of a human who follows a haptic robotic
guide without using sight, which is valuable for assistive robots that aid the
visually impaired. We apply a deep learning method based on recurrent neural
networks using multimodal data: (1) human trajectory, (2) movement of the
robotic guide, (3) haptic input data measured from the physical interaction
between the human and the robot, (4) human depth data. We collected actual
human trajectory and multimodal response data through indoor experiments. Our
model outperformed the baseline result while using only the robot data with the
observed human trajectory, and it shows even better results when using
additional haptic and depth data.Comment: 6 pages, Submitted to IEEE World Haptics Conference 201
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