We explore online reinforcement learning techniques to find good policies to control the orientation of a mobile robot during social group conversations.
In this work, we develop a controller for realizing smooth and accurate motion of a robotic head with application to a teleoperation system for the Furhat robot head.
We propose a neural model to learn argument embeddings from the context by explicitly incorporating dependency relations as multiplicative factors, which bias argument embeddings according to their dependency roles.
We propose a solution for presenting movie quality graphics to the user while still allowing the sense of presence afforded by free viewpoint head motion.
We propose “Stereohaptics”, a framework to create, record, modify, and playback rich and dynamic haptic media using audio based tools.
In this work, we address the problem of mapping a real-world material to its nearest 3D printable counterpart by constructing a perceptual model for the compliance of nonlinearly elastic objects. We begin by building a perceptual space from experimentally obtained user comparisons of twelve 3D-printed metamaterials.
In our approach, we construct an acoustic filter comprised of a set of parameterized shape primitives, whose transmission matrices can be precomputed.
We present a compiler that can automatically turn assemblies of high-level shape primitives (tubes, sheets) into low-level machine instructions.
In this paper, we introduce a new direct skinning method that addresses this problem.
In order to examine the relationship between stylistic elements of animated characters and the target ages of their audiences, we performed a series of qualitative and quantitative studies.
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