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Research Area

Robotics

Delivering a more convenient and consistent customer experience through a variety of technologies, including autonomous mobile robots, sophisticated control software, language perception, power management, computer vision, depth sensing, machine learning, object recognition, and semantic understanding of commands.

Recent publications

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  • Andrew Kramer, Chris Heckman
    Field Robotics
    2024
    For autonomous ground vehicles (AGVs) deployed in suburban neighborhoods and other human-centric environments the problem of localization remains a fundamental challenge. There are well established methods for localization with GPS, lidar, and cameras. But even in ideal conditions these have limitations. GPS is not always available and is often not accurate enough on its own, visual methods have difficulty
  • ICRA 2024
    2024
    Robotic manipulation is a key enabler for automation in the fulfillment logistics sector. Such robotic systems require perception and manipulation capabilities to handle a wide variety of objects. Existing systems either operate on a closed set of objects or perform object-agnostic manipulation which lacks the capability for deliberate and reliable manipulation at scale. Object identification (ID) unlocks
  • Senthil Hariharan Arul, Jong Jin Park, Vishnu Prem, Yang Zhang, Dinesh Manocha
    ICRA 2024
    2024
    In this paper, we present a probabilistic and unconstrained model predictive control formulation for robot navigation under uncertainty. We present (1) a closed-form approximation of the probability of collision that naturally models the propagation of uncertainty over the planning horizon and is computationally cheap to evaluate, and (2) a collision-cost formulation which provably preserves forward invariance
  • ICRA 2024
    2024
    When a mobile robot autonomously explores an indoor space to produce a localization and navigation map, it is important to create both a stable pose graph and a high-quality occupancy map that covers all the navigable areas. In this work, we propose a novel probabilistic active loop closure framework which attempts to maximally reduce pose graph uncertainty during exploration and improves occupancy map
  • Yinan Pei, Yuri Ivanov
    IEEE Robotics and Automation Letters 2024, IROS 2024
    2024
    In this paper we propose an approach to trajectory planning based on the purpose of the task. For a redundant manipulator, many end effector poses in the task space can be achieved with multiple joint configurations. In planning the motion, we are free to choose the configuration that is optimal for the particular task requirement. Many previous motion-planning approaches have been proposed for the sole

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