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Using the Gamma-Ray Spectrometer on the Mars Odyssey, we have identified two regions near the poles that are enriched in hydrogen. The data indicate the presence of a subsurface layer enriched in hydrogen overlain by a hydrogen-poor layer. The thickness of the upper layer decreases with decreasing distance to the pole, ranging from a column density of about(More)
Learning policies that generalize across multiple tasks is an important and challenging research topic in reinforcement learning and robotics. Training individual policies for every single potential task is often impractical, especially for continuous task variations, requiring more principled approaches to share and transfer knowledge among similar tasks.(More)
Efficient skill acquisition is crucial for creating versatile robots. One intuitive way to teach a robot new tricks is to demonstrate a task and enable the robot to imitate the demonstrated behavior. This approach is known as imitation learning. Classical methods of imitation learning, such as inverse reinforcement learning or behavioral cloning, suffer(More)
Visible to infrared reflectance spectroscopic analyses (0.3-25 micromoles) have been performed on sediments from the Dry Valleys region of Antarctica. Sample characterization for these sediments includes extensive geochemical analyses and X-ray diffraction (XRD). The reflectance spectra and XRD indicate major amounts of quartz, feldspar, and pyroxene in(More)
One of the most elegant ways of teaching new skills to robots is to provide demonstrations of a task and let the robot imitate this behavior. Such imitation learning is a non-trivial task: Different anatomies of robot and teacher, and reduced robustness towards changes in the control task are two major difficulties in imitation learning. We present an(More)
Efficient manipulation requires contact to reduce uncertainty. The manipulation literature refers to this as funneling: a methodology for increasing reliability and robustness by leveraging haptic feedback and control of environmental interaction. However, there is a fundamental gap between traditional approaches to trajectory optimization and this concept(More)
Inverse Optimal Control (IOC) assumes that demonstrations are the solution to an optimal control problem with unknown underlying costs, and extracts parameters of these underlying costs. We propose the framework of Inverse KKT, which assumes that the demonstrations fulfill the Karush-Kuhn-Tucker conditions of an unknown underlying constrained optimization(More)
An essential aspect for making robots succeed in real-world environments is to give them the ability to robustly perform motions in continuously changing situations. Classical motion planning methods usually create plans for static environments. The direct execution of such plans in dynamic environments often becomes problematic. We present an approach that(More)
—This work addresses the problem of how a robot can improve a manipulation skill in a sample-efficient and secure manner. As an alternative to the standard reinforcement learning formulation where all objectives are defined in a single reward function, we propose a generalized formulation that consists of three components: 1) A known analytic control cost(More)