Impact Crater Formation in Icy Layered Terrains on Mars

We present numerical simulations of crater formation under Martian conditions with a single near-surface icy layer to investigate changes in crater morphology between glacial and interglacial periods. The ice fraction, thickness, and depth to the icy layer are varied to understand the systematic effects oil observable crater features. To accurately model impact cratering into ice, a new equation of state table and strength model parameters for H2O are fitted to laboratory data. The presence of an icy layer significantly modifies the cratering mechanics. Observable features demonstrated by the modeling include variations in crater morphometry (depth and rim height) and icy infill of the crater floor during the late stages of crater formation. In addition, an icy layer modifies the velocities, angles, and volumes of ejecta, leading to deviations of ejecta blanket thickness from the predicted power law. The dramatic changes in crater excavation are a result of both the shock impedance and the strength mismatch between layers of icy and rocky materials. Our simulations suggest that many of the unusual features of Martian craters may be explained by the presence of icy layers, including shallow craters with well-preserved ejecta blankets, icy flow related features, some layered ejecta structures, and crater lakes. Therefore, the cratering record implies that near-surface icy layers are widespread on Mars.Earth and Planetary Science

SPARC: an efficient way to combine reinforcement learning and supervised autonomy

Shortcomings of reinforcement learning for robot control include the sparsity of the environmental reward function, the high number of trials required before reaching an efficient action policy and the reliance on exploration to gather information about the environment, potentially resulting in undesired actions. These limits can be overcome by adding a human in the loop to provide additional information during the learning phase. In this paper, we propose a novel way to combine human inputs and reinforcement by following the Supervised Progressively Autonomous Robot Competencies (SPARC) approach. We compare this method to the principles of Interactive Reinforcement Learning as proposed by Thomaz and Breazeal. Results from a study involving 40 participants show that using SPARC increases the performance of the learning, reduces the time and number of inputs required for teaching and faces fewer errors during the learning process. These results support the use of SPARC as an efficient method to teach a robot to interact with humans

Teaching robots social autonomy from in situ human guidance

Striking the right balance between robot autonomy and human control is a core challenge in social robotics, in both technical and ethical terms. On the one hand, extended robot autonomy offers the potential for increased human productivity and for the off-loading of physical and cognitive tasks. On the other hand, making the most of human technical and social expertise, as well as maintaining accountability, is highly desirable. This is particularly relevant in domains such as medical therapy and education, where social robots hold substantial promise, but where there is a high cost to poorly performing autonomous systems, compounded by ethical concerns. We present a field study in which we evaluate SPARC (supervised progressively autonomous robot competencies), an innovative approach addressing this challenge whereby a robot progressively learns appropriate autonomous behavior from in situ human demonstrations and guidance. Using online machine learning techniques, we demonstrate that the robot could effectively acquire legible and congruent social policies in a high-dimensional child-tutoring situation needing only a limited number of demonstrations while preserving human supervision whenever desirable. By exploiting human expertise, our technique enables rapid learning of autonomous social and domain-specific policies in complex and nondeterministic environments. Last, we underline the generic properties of SPARC and discuss how this paradigm is relevant to a broad range of difficult human-robot interaction scenarios

Space Plasma Testing of High-Voltage Thin-Film Solar Arrays with Protective Coatings

This paper gives an overview of the space plasma test program for thin-film photovoltaics (TFPV) technologies developed at the Air Force Research Laboratory (AFRL). The main objective of this program is to simulate the effects of space plasma characteristic of LEO and MEO environments on TFPV. Two types of TFPV, amorphous silicon (a-Si) and copper-indium-gallium-diselenide (CIGS), coated with two types of thin-film, multifunctional coatings were used for these studies. This paper reports the results of the first phase of this program, namely the results of preliminary electrostatic charging, arcing, dielectric breakdown, and collection current measurements carried out with a series of TFPV exposed to simulated space plasma at the NASA Glenn Plasma Interaction Facility. The experimental data demonstrate that multifunctional coatings developed for this program provide effective protection against the plasma environment while minimizing impact on power generation performance. This effort is part of an ongoing program led by the Space Vehicles Directorate at the AFRL devoted to the development and space qualification of TFPV and their protective coatings

Space Trumps Time When Talking About Objects

The nature of the relationship between the concepts of space and time in the human mind is much debated. Some claim that space is primary and that it structures time (cf. Lakoff & Johnson, 1980) while others (cf. Walsh, 2003) maintain no difference in status between them. Using fully immersive virtual reality (VR), we examined the influence of object distance and time of appearance on choice of demonstratives (this and that) to refer to objects. Critically, demonstratives can be used spatially (this/that red triangle) and temporally (this/that month). Experiment 1 showed a pattern of demonstrative usage in VR that is consistent with results found in real‐world studies. Experiments 2, 3, and 4 manipulated both when and where objects appeared, providing scenarios where participants were free to use demonstratives in either a temporal or spatial sense. Although we find evidence for time of presentation affecting object mention, the experiments found that demonstrative choice was affected only by distance. These results support the view that spatial uses of demonstratives are privileged over temporal uses

The PInSoRo dataset: supporting the data-driven study of child-child and child-robot social dynamics

The study of the fine-grained social dynamics between children is a methodological challenge, yet a good understanding of how social interaction between children unfolds is important not only to Developmental and Social Psychology, but recently has become relevant to the neighbouring field of Human-Robot Interaction (HRI). Indeed, child-robot interactions are increasingly being explored in domains which require longer-term interactions, such as healthcare and education. For a robot to behave in an appropriate manner over longer time scales, its behaviours have to be contingent and meaningful to the unfolding relationship. Recognising, interpreting and generating sustained and engaging social behaviours is as such an important—and essentially, open—research question. We believe that the recent progress of machine learning opens new opportunities in terms of both analysis and synthesis of complex social dynamics. To support these approaches, we introduce in this article a novel, open dataset of child social interactions, designed with data-driven research methodologies in mind. Our data acquisition methodology relies on an engaging, methodologically sound, but purposefully underspecified free-play interaction. By doing so, we capture a rich set of behavioural patterns occurring in natural social interactions between children. The resulting dataset, called the PInSoRo dataset, comprises 45+ hours of hand-coded recordings of social interactions between 45 child-child pairs and 30 child-robot pairs. In addition to annotations of social constructs, the dataset includes fully calibrated video recordings, 3D recordings of the faces, skeletal informations, full audio recordings, as well as game interactions

Leveraging Human Inputs in Interactive Machine Learning for Human Robot Interaction

A key challenge of HRI is allowing robots to be adaptable, especially as robots are expected to penetrate society at large and to interact in unexpected environments with non- technical users. One way of providing this adaptability is to use Interactive Machine Learning, i.e. having a human supervisor included in the learning process who can steer the action selection and the learning in the desired direction. We ran a study exploring how people use numeric rewards to evaluate a robot's behaviour and guide its learning. From the results we derive a number of challenges when design- ing learning robots: what kind of input should the human provide? How should the robot communicate its state or its intention? And how can the teaching process by made easier for human supervisors

The Free-play Sandbox: a Methodology for the Evaluation of Social Robotics and a Dataset of Social Interactions

conference paperEvaluating human-robot social interactions in a rigorous manner is notoriously difficult: studies are either conducted in labs with constrained protocols to allow for robust measurements and a degree of replicability, but at the cost of ecological validity; or in the wild, which leads to superior experimental realism, but often with limited replicability and at the expense of rigorous interaction metrics. We introduce a novel interaction paradigm, designed to elicit rich and varied social interactions while having desirable scientific properties (replicability, clear metrics, possibility of either autonomous or Wizard-of-Oz robot behaviours). This paradigm focuses on child-robot interactions, and builds on a sandboxed free-play environment. We present the rationale and design of the interaction paradigm, its methodological and technical aspects (including the open-source implementation of the software platform), as well as two large open datasets acquired with this paradigm, and meant to act as experimental baselines for future research