Toward fieldable human scale mobile manipulation using RoMan

Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II(2020)

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摘要
Robots are ideal surrogates for performing tasks that are dull, dirty, and dangerous. To fully achieve this ideal, a robotic teammate should be able to autonomously perform human-level tasks in unstructured environments where we do not want humans to go. In this paper, we take a step toward realizing that vision by introducing the integration of state of the art advancements in intelligence, perception, and manipulation on the RoMan (Robotic Manipulation) platform. RoMan is comprised of two 7 degree of freedom (DoF) limbs connected to a 1 DoF torso and mounted on a tracked base. Multiple lidars are used for navigation, and a stereo depth camera visualizes point clouds for grasping. Each limb has a 6 DoF force-torque sensor at the wrist, with a dexterous 3-finger gripper on one limb and a stronger 4-finger claw-like hand on the other. Tasks begin with an operator specifying a mission type, a desired final …
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关键词
Autonomy, Mobile Manipulation, Grasping, Field Robotics, Unstructured, Debris, Robust, Full-stack
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