In this article, we present a novel approach for continuous operator authentication in teleoperated robotic processes based on Hidden Markov Models (HMM). While HMMs were originally developed and widely used in speech recognition, they have shown great performance in human motion and activity modeling. We make an analogy between human language and teleoperated robotic processes (i.e., words are analogous to a teleoperator’s gestures, sentences are analogous to the entire teleoperated task or process) and implement HMMs to model the teleoperated task. To test the continuous authentication performance of the proposed method, we conducted two sets of analyses. We built a virtual reality (VR) experimental environment using a commodity VR headset (HTC Vive) and haptic feedback enabled controller (Sensable PHANToM Omni) to simulate a real teleoperated task. An experimental study with 10 subjects was then conducted. We also performed simulated continuous operator authentication by using the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS). The performance of the model was evaluated based on the continuous (real-time) operator authentication accuracy as well as resistance to a simulated impersonation attack. The results suggest that the proposed method is able to achieve 70% (VR experiment) and 81% (JIGSAWS dataset) continuous classification accuracy with as short as a 1-second sample window. It is also capable of detecting an impersonation attack in real-time.

While numerous deep approaches to the problem of vision-aided localization have been recently proposed, systems operating in the real world will undoubtedly experience novel sensory states previously unseen even under the most prodigious training regimens. We address the localization problem with online error correction (OEC) modules that are trained to correct a vision-aided localization network's mistakes. We demonstrate the generalizability of the OEC modules and describe our unsupervised deep neural network approach to the fusion of RGB-D imagery with inertial measurements for absolute trajectory estimation. Our network, dubbed the Visual-Inertial-Odometry Learner (VIOLearner), learns to perform visual-inertial odometry (VIO) without inertial measurement unit (IMU) intrinsic parameters or the extrinsic calibration between an IMU and camera. The network learns to integrate IMU measurements and generate hypothesis trajectories which are then corrected online according to the Jacobians of scaled image projection errors with respect to spatial grids of pixel coordinates. We evaluate our network against state-of-the-art (SoA) VIO, visual odometry (VO), and visual simultaneous localization and mapping (VSLAM) approaches on the KITTI Odometry dataset as well as a micro aerial vehicle (MAV) dataset that we collected in the AirSim simulation environment. We demonstrate better than SoA translational localization performance against comparable SoA approaches on our evaluation sequences.

Hand gestures are a natural component of human-human communication. Simple hand gestures are intuitive and can exhibit great lexical variety. It stands to reason that such a user input mechanism can have many benefits, including seamless interaction, intuitive control and robustness to physical constraints and ambient electrical, light and sound interference. However, while semantic and logical information encoded via hand gestures is readily decoded by humans, leveraging this communication channel in human-machine interfaces remains a challenge. Recent data-driven deep learning approaches are promising towards uncovering abstract and complex relationships that manual and direct rule-based classification schemes fail to discover. Such an approach is amenable towards hand gesture recognition, but requires myriad data which can be collected physically via user experiments. This process, however, is onerous and tedious. A streamlined approach with less overhead is sought. To that end, this work presents a novel method of synthetic hand gesture dataset generation that leverages modern gaming engines. Furthermore, preliminary results indicate that the dataset, despite being synthetic and requiring no physical data collection, is both accurate and rich enough to train a real-world hand gesture classifier that operates in real-time.