Makondo, NdivhuwoRosman, Benjamin SHasegawa, O2018-08-022018-08-022018-05Makondo, N., Rosman, B.S. and Hasegawa, O. 2018. Accelerating model learning with inter-robot knowledge transfer. IEEE International Conference on Robotics and Automation (ICRA2018), 21-25 May 2018, Brisbane, Australiahttps://www.youtube.com/watch?v=P9P8eBvYxoIhttp://hdl.handle.net/10204/10343Paper presented at the IEEE International Conference on Robotics and Automation (ICRA2018), 21-25 May 2018, Brisbane, AustraliaOnline learning of a robot’s inverse dynamics model for trajectory tracking necessitates an interaction between the robot and its environment to collect training data. This is challenging for physical robots in the real world, especially for humanoids and manipulators due to their large and high dimensional state and action spaces, as a large amount of data must be collected over time. This can put the robot in danger when learning tabula rasa and can also be a time-intensive process especially in a multi-robot setting, where each robot is learning its model from scratch. We propose accelerating learning of the inverse dynamics model for trajectory tracking tasks in this multi-robot setting using knowledge transfer, where robots share and re-use data collected by preexisting robots, in order to speed up learning for new robots. We propose a scheme for collecting a sample of correspondences from the robots for training transfer models, and demonstrate, in simulations, the benefit of knowledge transfer in accelerating online learning of the inverse dynamics model between several robots, including between a low-cost Interbotix PhantomX Pincher arm, and a more expensive and relatively heavier Kuka youBot arm. We show that knowledge transfer can save up to 63% of training time of the youBot arm compared to learning from scratch, and about 58% for the lighter Pincher arm.enRobot modelsModel learningModel transferManifold learningConference PresentationMakondo, N., Rosman, B. S., & Hasegawa, O. (2018). Accelerating model learning with inter-robot knowledge transfer. http://hdl.handle.net/10204/10343Makondo, Ndivhuwo, Benjamin S Rosman, and O Hasegawa. "Accelerating model learning with inter-robot knowledge transfer." (2018): http://hdl.handle.net/10204/10343Makondo N, Rosman BS, Hasegawa O, Accelerating model learning with inter-robot knowledge transfer; 2018. http://hdl.handle.net/10204/10343 .TY - Conference Presentation AU - Makondo, Ndivhuwo AU - Rosman, Benjamin S AU - Hasegawa, O AB - Online learning of a robot’s inverse dynamics model for trajectory tracking necessitates an interaction between the robot and its environment to collect training data. This is challenging for physical robots in the real world, especially for humanoids and manipulators due to their large and high dimensional state and action spaces, as a large amount of data must be collected over time. This can put the robot in danger when learning tabula rasa and can also be a time-intensive process especially in a multi-robot setting, where each robot is learning its model from scratch. We propose accelerating learning of the inverse dynamics model for trajectory tracking tasks in this multi-robot setting using knowledge transfer, where robots share and re-use data collected by preexisting robots, in order to speed up learning for new robots. We propose a scheme for collecting a sample of correspondences from the robots for training transfer models, and demonstrate, in simulations, the benefit of knowledge transfer in accelerating online learning of the inverse dynamics model between several robots, including between a low-cost Interbotix PhantomX Pincher arm, and a more expensive and relatively heavier Kuka youBot arm. We show that knowledge transfer can save up to 63% of training time of the youBot arm compared to learning from scratch, and about 58% for the lighter Pincher arm. DA - 2018-05 DB - ResearchSpace DP - CSIR KW - Robot models KW - Model learning KW - Model transfer KW - Manifold learning LK - https://researchspace.csir.co.za PY - 2018 T1 - Accelerating model learning with inter-robot knowledge transfer TI - Accelerating model learning with inter-robot knowledge transfer UR - http://hdl.handle.net/10204/10343 ER -