Human-guided Trajectory Adaptation for Tool Transfer Robotics Track

We introduce “transfer by correction": a method for transferring a robot’s tool-based task models to use unfamiliar tools. By having the robot receive corrections from a human teacher when repeating a known task with a new tool, it can learn the relationship between the two tools, allowing it to transfer additional tasks learned with the original tool to the new tool. The goal is to enable the robot to generalize its task knowledge to accommodate tool replacements and thus be more robust to changes in its environment. We demonstrate how the tool transform models learned from one episode of task corrections can be used to perform that task with ≥ 85% of maximum performance in 83% of tool/task combinations. Furthermore, these transformations generalize to unseen tool/task combinations in 27.8% of our transfer evaluations, and up to 41% of transfer problems when the source and replacement tool share tooltip similarities. Overall, these results indicate that successful task adaptation for a new tool is dependent on the the tool’s usage within that task, and that the transform model learned from interactive corrections can be generalized to other tasks providing a similar context for the new tool.

[1]  J. Sinapov,et al.  Detecting the functional similarities between tools using a hierarchical representation of outcomes , 2008, 2008 7th IEEE International Conference on Development and Learning.

[2]  Silvio Savarese,et al.  Learning task-oriented grasping for tool manipulation from simulated self-supervision , 2018, Robotics: Science and Systems.

[3]  Maya Cakmak,et al.  Keyframe-based Learning from Demonstration , 2012, Int. J. Soc. Robotics.

[4]  Patrick Beeson,et al.  TRAC-IK: An open-source library for improved solving of generic inverse kinematics , 2015, 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids).

[5]  Charles C. Kemp,et al.  Challenges for robot manipulation in human environments [Grand Challenges of Robotics] , 2007, IEEE Robotics & Automation Magazine.

[6]  Marcin Andrychowicz,et al.  One-Shot Imitation Learning , 2017, NIPS.

[7]  Bipin Indurkhya,et al.  Adapting Everyday Manipulation Skills to Varied Scenarios , 2018, 2019 International Conference on Robotics and Automation (ICRA).

[8]  Finale Doshi-Velez,et al.  Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes , 2017, AAAI.

[9]  C. Sammut,et al.  An Architecture for Tool Use and Learning in Robots , 2007 .

[10]  Eric L. Sauser,et al.  Tactile guidance for policy refinement and reuse , 2010, 2010 IEEE 9th International Conference on Development and Learning.

[11]  2013 , 2018, Eu minha tía e o golpe do atraso.

[12]  Peter Stone,et al.  Transfer Learning for Reinforcement Learning Domains: A Survey , 2009, J. Mach. Learn. Res..

[13]  Brett Browning,et al.  A survey of robot learning from demonstration , 2009, Robotics Auton. Syst..

[14]  C. Kemp,et al.  Robot Manipulation of Human Tools : Autonomous Detection and Control of Task Relevant Features , 2006 .

[15]  S. Schaal.  Dynamic Movement Primitives -A Framework for Motor Control in Humans and Humanoid Robotics , 2006 .

[16]  Andrea Lockerd Thomaz,et al.  Robot Learning from Human Teachers , 2014, Robot Learning from Human Teachers.

[17]  Ashok K. Goel,et al.  Human-Guided Object Mapping for Task Transfer , 2018, ACM Transactions on Human-Robot Interaction.

[18]  Y. Oshman,et al.  Averaging Quaternions , 2007 .

[19]  Sergey Levine,et al.  One-shot learning of manipulation skills with online dynamics adaptation and neural network priors , 2015, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[20]  Sergey Levine,et al.  One-Shot Visual Imitation Learning via Meta-Learning , 2017, CoRL.

[21]  Stefan Schaal,et al.  Learning and generalization of motor skills by learning from demonstration , 2009, 2009 IEEE International Conference on Robotics and Automation.

[22]  Anca D. Dragan,et al.  Learning from Physical Human Corrections, One Feature at a Time , 2018, 2018 13th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[23]  Aude Billard,et al.  Iterative learning of grasp adaptation through human corrections , 2012, Robotics Auton. Syst..

[24]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[25]  Heiko Hoffmann,et al.  Adaptive robotic tool use under variable grasps , 2014, Robotics Auton. Syst..