Anusha Nagabandi

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Our goal is to enable robots to time their motion in a way that is purposefully expressive of their internal states, making them more transparent to people. We start by investigating what types of states motion timing is capable of expressing, focusing on robot manipulation and keeping the path constant while systematically varying the timing. We find that(More)
Model-free deep reinforcement learning methods have successfully learned complex behavioral strategies for a wide range of tasks, but typically require many samples to achieve good performance. Model-based algorithms, in principle, can provide for much more efficient learning, but have proven difficult to extend to expressive, high-capacity models such as(More)
In this paper we address the problem of multi-robot localization with a heterogeneous team of low-cost mobile robots. The team consists of a single centralized observer with an inertial measurement unit (IMU) and monocular camera, and multiple picket robots with only IMUs and Red Green Blue (RGB) light emitting diodes (LED). This team cooperatively(More)
Ribbon folding is a new approach to structure formation that forms higher dimensional structures using a lower dimensional primitive, namely a ribbon. In this paper, we present a novel algorithm to address path planning for ribbon folding of multi-link planar structures. We first represent the desired structure with a graph-based representation of edges and(More)
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