Scene Editing as Teleoperation: A Case Study in 6DoF Kit Assembly

• URL: http://arxiv.org/abs/2110.04450v1
• Date: Sat, 9 Oct 2021 04:22:21 GMT
• Title: Scene Editing as Teleoperation: A Case Study in 6DoF Kit Assembly
• Authors: Shubham Agrawal, Yulong Li, Jen-Shuo Liu, Steven K. Feiner, Shuran Song
• Abstract summary: We propose the framework "Scene Editing as Teleoperation" (SEaT) Instead of controlling the robot, users focus on specifying the task's goal. A user can perform teleoperation without any expert knowledge of the robot hardware.
• Score: 18.563562557565483
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Studies in robot teleoperation have been centered around action specifications -- from continuous joint control to discrete end-effector pose control. However, these robot-centric interfaces often require skilled operators with extensive robotics expertise. To make teleoperation accessible to non-expert users, we propose the framework "Scene Editing as Teleoperation" (SEaT), where the key idea is to transform the traditional "robot-centric" interface into a "scene-centric" interface -- instead of controlling the robot, users focus on specifying the task's goal by manipulating digital twins of the real-world objects. As a result, a user can perform teleoperation without any expert knowledge of the robot hardware. To achieve this goal, we utilize a category-agnostic scene-completion algorithm that translates the real-world workspace (with unknown objects) into a manipulable virtual scene representation and an action-snapping algorithm that refines the user input before generating the robot's action plan. To train the algorithms, we procedurally generated a large-scale, diverse kit-assembly dataset that contains object-kit pairs that mimic real-world object-kitting tasks. Our experiments in simulation and on a real-world system demonstrate that our framework improves both the efficiency and success rate for 6DoF kit-assembly tasks. A user study demonstrates that SEaT framework participants achieve a higher task success rate and report a lower subjective workload compared to an alternative robot-centric interface. Video can be found at https://www.youtube.com/watch?v=-NdR3mkPbQQ .

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