Human-Agent Interaction Model Learning based on Crowdsourcing

Missions involving humans interacting with automated systems become increasingly common. Due to the non-deterministic behavior of the human and possibly high risk of failing due to human factors, such an integrated system should react smartly by adapting its behavior when necessary. A promise avenue to design an efficient interaction-driven system is the mixed-initiative paradigm. In this context, this paper proposes a method to learn the model of a mixed-initiative human-robot mission. The first step to set up a reliable model is to acquire enough data. For this aim a crowdsourcing campaign was conducted and learning algorithms were trained on the collected data in order to model the human-robot mission and to optimize a supervision policy with a Markov Decision Process (MDP). This model takes into account the actions of the human operator during the interaction as well as the state of the robot and the mission. Once such a model has been learned, the supervision strategy can be optimized according to a criterion representing the goal of the mission. In this paper, the supervision strategy concerns the robot’s operating mode. Simulations based on the MDP model show that planning under uncertainty solvers can be used to adapt robot’s mode according to the state of the human-robot system. The optimization of the robot’s operation mode seems to be able to improve the team’s performance. The dataset that comes from crowdsourcing is therefore a material that can be useful for research in human-machine interaction, that is why it has been made available on our website

An Embodied Multi-Sensor Fusion Approach to Visual Motion Estimation Using Unsupervised Deep Networks

Aimed at improving size, weight, and power (SWaP)-constrained robotic vision-aided state estimation, we describe our unsupervised, deep convolutional-deconvolutional sensor fusion network, Multi-Hypothesis DeepEfference (MHDE). MHDE learns to intelligently combine noisy heterogeneous sensor data to predict several probable hypotheses for the dense, pixel-level correspondence between a source image and an unseen target image. We show how our multi-hypothesis formulation provides increased robustness against dynamic, heteroscedastic sensor and motion noise by computing hypothesis image mappings and predictions at 76–357 Hz depending on the number of hypotheses being generated. MHDE fuses noisy, heterogeneous sensory inputs using two parallel, inter-connected architectural pathways and n (1–20 in this work) multi-hypothesis generating sub-pathways to produce n global correspondence estimates between a source and a target image. We evaluated MHDE on the KITTI Odometry dataset and benchmarked it against the vision-only DeepMatching and Deformable Spatial Pyramids algorithms and were able to demonstrate a significant runtime decrease and a performance increase compared to the next-best performing method

<title>Understanding mechanics and stress effects in RAINBOW and THUNDER stress-biased actuators</title>

Rainbow and Thunder actuators constitute a family of \u27stress-biased\u27 devices that display enhanced strain and load-bearing capabilities in comparison to traditional flextensional devices. For both of these actuators, doming occurs during the cooling phase of the fabrication process to relieve thermal expansion mismatch between the metallic and piezoelectric layers. Accompanying dome formation is the development of a tensile stress within the surface region of the piezoelectric layer that can approach 400 MPa. This tensile stress affects the ferroelectric domain configuration and improves the 90 percent domain wall movement within the surface region of the piezoelectric under an applied electric field. It has been reported that this effect is responsible for the enhanced electromechanical performance of these devices. The results of the presented study, however, suggest that in addition to stress, other mechanical and mass loading effects may also play a role in the enhanced performance of these devices. Equivalent circuit and finite element modeling studies of these stress-biased actuators are reported, and in particular, the effects of specimen geometry on internal stress in the piezoelectric layer are discussed. Finite element analysis shows that in the surface region of the piezoelectric, the highest tensile stresses are, in fact, predicted for those devices that display the greatest displacement performance, i.e., devices that have a piezoelectric layer that is approximately twice as thick as the \u27metallic\u27 layer. However, equivalent circuit studies show that the highest predicted strains should also be observed for samples with similar geometries yet this approach does not include stress effects. This implies that not only stress, but also mass loading and other mechanical effects must also be considered in predicting optimum design geometries