Vernaza, Paul

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2008 - 20092
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  • Publication
    Online, self-supervised terrain classification via discriminatively trained submodular Markov random fields
    (2008-05-19) Vernaza, Paul; Taskar, Ben; Lee, Daniel D
    The authors present a novel approach to the task of autonomous terrain classification based on structured prediction. We consider the problem of learning a classifier that will accurately segment an image into "obstacle" and "ground" patches based on supervised input. Previous approaches to this problem have focused mostly on local appearance; typically, a classifier is trained and evaluated on a pixel-by-pixel basis, making an implicit assumption of independence in local pixel neighborhoods. We relax this assumption by modeling correlations between pixels in the submodular MRF framework. We show how both the learning and inference tasks can be simply and efficiently implemented-exact inference via an efficient max flow computation; and learning, via an averaged-subgradient method. Unlike most comparable MRF-based approaches, our method is suitable for implementation on a robot in real-time. Experimental results are shown that demonstrate a marked increase in classification accuracy over standard methods in addition to real-time performance.
  • Publication
    Search-based Planning for a Legged Robot over Rough Terrain
    (2009-05-12) Vernaza, Paul; Likhachev, Maxim; Bhattacharya, Subhrajit; Kushleyev, Aleksandr; Lee, Daniel D; Chitta, Sachin
    We present a search-based planning approach for controlling a quadrupedal robot over rough terrain. Given a start and goal position, we consider the problem of generating a complete joint trajectory that will result in the legged robot successfully moving from the start to the goal. We decompose the problem into two main phases: an initial global planning phase, which results in a footstep trajectory; and an execution phase, which dynamically generates a joint trajectory to best execute the footstep trajectory. We show how R* search can be employed to generate high-quality global plans in the high-dimensional space of footstep trajectories. Results show that the global plans coupled with the joint controller result in a system robust enough to deal with a variety of terrains.