Learning Force‐Relevant Skills from Human Demonstration
Tóm tắt
Many human manipulation skills are force relevant, such as opening a bottle cap and assembling furniture. However, it is still a difficult task to endow a robot with these skills, which largely is due to the complexity of the representation and planning of these skills. This paper presents a learning‐based approach of transferring force‐relevant skills from human demonstration to a robot. First, the force‐relevant skill is encapsulated as a statistical model where the key parameters are learned from the demonstrated data (motion, force). Second, based on the learned skill model, a task planner is devised which specifies the motion and/or the force profile for a given manipulation task. Finally, the learned skill model is further integrated with an adaptive controller that offers task‐consistent force adaptation during online executions. The effectiveness of the proposed approach is validated with two experiments, i.e., an object polishing task and a peg‐in‐hole assembly.
Từ khóa
Tài liệu tham khảo
GuS. HollyE. LillicrapT. andLevineS. Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2017 IEEE 3389–3396 2-s2.0-85027967014.
Levine S., 2016, International Symposium on Experimental Robotics, 173
KalakrishnanM. RighettiL. PastorP. andSchaalS. Learning force control policies for compliant manipulation Proceedings of the IEEE International Conference on Intelligent Robots and Systems: Celebrating 50 Years of Robotics (IROS) 2011 IEEE 4639–4644 2-s2.0-84455188451.
LeeA. X. LuH. GuptaA. LevineS. andAbbeelP. Learning force-based manipulation of deformable objects from multiple demonstrations Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2015 IEEE 177–184 2-s2.0-84938252581.
LiM. YinH. TaharaK. andBillardA. Learning object-level impedance control for robust grasping and dexterous manipulation Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2014 IEEE 6784–6791 https://doi.org/10.1109/icra.2014.6907861 2-s2.0-84929208215.
LiM. BekirogluY. KragicD. andBillardA. Learning of grasp adaptation through experience and tactile sensing Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2014 IEEE 3339–3346 2-s2.0-84911463955.
KronanderK.andBillardA. Online learning of varying stiffness through physical human-robot interaction Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2012 IEEE 1842–1849 2-s2.0-84864429731.
Siciliano B., 2012, Robot Force Control
LinY. RenS. ClevengerM. andSunY. Learning grasping force from demonstration Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) IEEE 1526–1531 2-s2.0-84864483234.
VogtD. StepputtisS. GrehlS. JungB. andBen AmorH. A system for learning continuous human-robot interactions from human-human demonstrations Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2017 IEEE 2882–2889 2-s2.0-85028018545.
Kronander K., 2014, Workshop on Human-Robot Interaction for Industrial Manufacturing, Robotics, Science and Systems
Calinon S., 2009, Robot programming by demonstration
Andersen T. T., 2015, Optimizing the universal robots ros driver
Rasmussen C. E., 2010, Gaussian processes for machine learning (GPML) toolbox, Journal of Machine Learning Research, 11, 3011