113 research outputs found
On human motion prediction using recurrent neural networks
Human motion modelling is a classical problem at the intersection of graphics
and computer vision, with applications spanning human-computer interaction,
motion synthesis, and motion prediction for virtual and augmented reality.
Following the success of deep learning methods in several computer vision
tasks, recent work has focused on using deep recurrent neural networks (RNNs)
to model human motion, with the goal of learning time-dependent representations
that perform tasks such as short-term motion prediction and long-term human
motion synthesis. We examine recent work, with a focus on the evaluation
methodologies commonly used in the literature, and show that, surprisingly,
state-of-the-art performance can be achieved by a simple baseline that does not
attempt to model motion at all. We investigate this result, and analyze recent
RNN methods by looking at the architectures, loss functions, and training
procedures used in state-of-the-art approaches. We propose three changes to the
standard RNN models typically used for human motion, which result in a simple
and scalable RNN architecture that obtains state-of-the-art performance on
human motion prediction.Comment: Accepted at CVPR 1
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
Anticipation in Human-Robot Cooperation: A Recurrent Neural Network Approach for Multiple Action Sequences Prediction
Close human-robot cooperation is a key enabler for new developments in
advanced manufacturing and assistive applications. Close cooperation require
robots that can predict human actions and intent, and understand human
non-verbal cues. Recent approaches based on neural networks have led to
encouraging results in the human action prediction problem both in continuous
and discrete spaces. Our approach extends the research in this direction. Our
contributions are three-fold. First, we validate the use of gaze and body pose
cues as a means of predicting human action through a feature selection method.
Next, we address two shortcomings of existing literature: predicting multiple
and variable-length action sequences. This is achieved by introducing an
encoder-decoder recurrent neural network topology in the discrete action
prediction problem. In addition, we theoretically demonstrate the importance of
predicting multiple action sequences as a means of estimating the stochastic
reward in a human robot cooperation scenario. Finally, we show the ability to
effectively train the prediction model on a action prediction dataset,
involving human motion data, and explore the influence of the model's
parameters on its performance. Source code repository:
https://github.com/pschydlo/ActionAnticipationComment: IEEE International Conference on Robotics and Automation (ICRA) 2018,
Accepte
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