Browsing by Author "Hauser, K"

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    Analysis and Observations From the First Amazon Picking Challenge
    (IEEE Transactions on Automation Science and Engineering, 2016) Correll, N; Bekris, KE; Berenson, D; Brock, O; Causo, A; Hauser, K; Okada, K; Rodriguez, A; Romano, JM; Wurman, PR
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    Consumer Cloud Robotics and the Fair Information Practice Principles: Recognizing the Challenges and Opportunities Ahead
    (Minnesota Journal of Law, Science & Technology, 2015) Proia, A; Simshaw, D; Hauser, K
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    Fast interpolation and time-optimization with contact
    (International Journal of Robotics Research, 2014-01-01) Hauser, K
    © The Author(s) 2014.This paper presents a method for generating dynamically feasible, keyframe-interpolating motions for robots undergoing contact, such as in legged locomotion and manipulation. The first stage generates a twice-differentiable interpolating path that obeys kinematic contact constraints up to a user-specified tolerance. The second stage optimizes speeds along the path to minimize time while satisfying dynamic constraints. The method supports velocity, acceleration, and torque constraints, and polyhedral contact friction constraints at an arbitrary number of contact points. The method is numerically stable, and empirical running time is weakly linear in the number of degrees of freedom and polynomial in the time-domain grid resolution. Experiments demonstrate that full-body motions for robots with 100 degrees of freedom and dozens of contact points are calculated in seconds.
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    The minimum constraint removal problem with three robotics applications
    (International Journal of Robotics Research, 2014-01-01) Hauser, K
    This paper formulates a new minimum constraint removal (MCR) motion planning problem in which the objective is to remove the fewest geometric constraints necessary to connect a start and goal state with a free path. It describes a probabilistic roadmap motion planner for MCR in continuous configuration spaces that operates by constructing increasingly refined roadmaps, and efficiently solves discrete MCR problems on these networks. A number of new theoretical results are given for discrete MCR, including a proof that it is NP-hard by reduction from SET-COVER. Two search algorithms are described that perform well in practice. The motion planner is proven to produce the optimal MCR with probability approaching 1 as more time is spent, and its convergence rate is improved with various efficient sampling strategies. It is demonstrated on three example applications: generating human-interpretable excuses for failure, motion planning under uncertainty, and rearranging movable obstacles. © The Author(s) 2013.