Uploaded May 2018 | Updated September 2026, 2 weeks ago
ICRA 2018 Spotlight Video
Interactive Session Thu PM Pod K.7
Authors: Nemec, Bojan; Yasuda, Ken'ichi; Mullennix, Nathaneal; Likar, Nejc; Ude, Ales
Title: Learning by Demonstration and Adaptation of Finishing Operations Using Virtual Mechanism Approach
Abstract:
In this paper we propose a novel technology for efficient programming of grinding and polishing tool-paths. The initial policy is recorded with a passive digitizer by a skilled operator. The demonstrated policy comprehends both position and force data. The optimal robot execution of the task is provided by applying virtual mechanism approach, which models the polishing/grinding tool as a serial kinematc chain. By joining the robot and the virtual mechanism in a unique augmented system, additional degrees of freedom are obtained and the redundancy resolution is applied to optimize the demonstrated motion. Another benefit of the proposed approach is that the same policy can be transfered to different combination of robots and grinding/polishing tools without any modification of the captured tool-path. The proposed approach requires known contact point between the treated object and the polishing/grinding tool. We propose a novel approach for accurate estimation of this point using data of the force/torque sensor. Finally, the demonstrated path is refined to compensate for inaccurate calibration and different dynamics of a robot and the human demonstrator using Iterative Learning Controller. The proposed method was verified in real industrial environment.
ICRA 2018 Spotlight Video
Interactive Session Thu PM Pod K.7
Authors: Nemec, Bojan; Yasuda, Ken'ichi; Mullennix, Nathaneal; Likar, Nejc; Ude, Ales
Title: Learning by Demonstration and Adaptation of Finishing Operations Using Virtual Mechanism Approach
Abstract:
In this paper we propose a novel technology for efficient programming of grinding and polishing tool-paths. The initial policy is recorded with a passive digitizer by a skilled operator. The demonstrated policy comprehends both position and force data. The optimal robot execution of the task is provided by applying virtual mechanism approach, which models the polishing/grinding tool as a serial kinematc chain. By joining the robot and the virtual mechanism in a unique augmented system, additional degrees of freedom are obtained and the redundancy resolution is applied to optimize the demonstrated motion. Another benefit of the proposed approach is that the same policy can be transfered to different combination of robots and grinding/polishing tools without any modification of the captured tool-path. The proposed approach requires known contact point between the treated object and the polishing/grinding tool. We propose a novel approach for accurate estimation of this point using data of the force/torque sensor. Finally, the demonstrated path is refined to compensate for inaccurate calibration and different dynamics of a robot and the human demonstrator using Iterative Learning Controller. The proposed method was verified in real industrial environment.
