Learning from demonstration(LfD)is an appealing method of helping robots learn new skills.Numerous papers have presented methods of LfD with good performance in robotics.However,complicated robot tasks that need to ca...Learning from demonstration(LfD)is an appealing method of helping robots learn new skills.Numerous papers have presented methods of LfD with good performance in robotics.However,complicated robot tasks that need to carefully regulate path planning strategies remain unanswered.Contact or non-contact constraints in specific robot tasks make the path planning problem more difficult,as the interaction between the robot and the environment is time-varying.In this paper,we focus on the path planning of complex robot tasks in the domain of LfD and give a novel perspective for classifying imitation learning and inverse reinforcement learning.This classification is based on constraints and obstacle avoidance.Finally,we summarize these methods and present promising directions for robot application and LfD theory.展开更多
Autonomous planning is a significant development direction of the space manipulator,and learning from demonstrations(LfD)is a potential strategy for complex tasks in the field.However,separating control from planning ...Autonomous planning is a significant development direction of the space manipulator,and learning from demonstrations(LfD)is a potential strategy for complex tasks in the field.However,separating control from planning may cause large torque fluctuations and energy consumptions,even instability or danger in control of space manipulators,especially for the planning based on the human demonstrations.Therefore,we present an autonomous planning and control strategy for space manipulators based on LfD and focus on the dynamics uncertainty problem,a common problem of actual manipulators.The process can be divided into three stages:firstly,we reproduced the stochastic directed trajectory based on the Gaussian process-based LfD;secondly,we built the model of the stochastic dynamics of the actual manipulator with Gaussian process;thirdly,we designed an optimal controller based on the dynamics model to obtain the improved commanded torques and trajectory,and used the separation theorem to deal with stochastic characteristics during control.We evaluated the strategy with locating pre-screwed bolts experiment by Tiangong-2 manipulator system on the ground.The result showed that,compared with other strategies,the strategy proposed in this paper could significantly reduce torque fluctuations and energy consumptions,and its precision can meet the task requirements.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.91848202)the Foundation for Innovative Research Groups of the National Natural Science Foundation of China(Grant No.51521003)。
文摘Learning from demonstration(LfD)is an appealing method of helping robots learn new skills.Numerous papers have presented methods of LfD with good performance in robotics.However,complicated robot tasks that need to carefully regulate path planning strategies remain unanswered.Contact or non-contact constraints in specific robot tasks make the path planning problem more difficult,as the interaction between the robot and the environment is time-varying.In this paper,we focus on the path planning of complex robot tasks in the domain of LfD and give a novel perspective for classifying imitation learning and inverse reinforcement learning.This classification is based on constraints and obstacle avoidance.Finally,we summarize these methods and present promising directions for robot application and LfD theory.
基金the Foundation for Innovative Research Groups of the National Natural Science Foundation of China(Grant No.51521003)the National Natural Science Foundation of China(Grant No.61803124)the Post-doctor Research Startup Foundation of Heilongjiang Province。
文摘Autonomous planning is a significant development direction of the space manipulator,and learning from demonstrations(LfD)is a potential strategy for complex tasks in the field.However,separating control from planning may cause large torque fluctuations and energy consumptions,even instability or danger in control of space manipulators,especially for the planning based on the human demonstrations.Therefore,we present an autonomous planning and control strategy for space manipulators based on LfD and focus on the dynamics uncertainty problem,a common problem of actual manipulators.The process can be divided into three stages:firstly,we reproduced the stochastic directed trajectory based on the Gaussian process-based LfD;secondly,we built the model of the stochastic dynamics of the actual manipulator with Gaussian process;thirdly,we designed an optimal controller based on the dynamics model to obtain the improved commanded torques and trajectory,and used the separation theorem to deal with stochastic characteristics during control.We evaluated the strategy with locating pre-screwed bolts experiment by Tiangong-2 manipulator system on the ground.The result showed that,compared with other strategies,the strategy proposed in this paper could significantly reduce torque fluctuations and energy consumptions,and its precision can meet the task requirements.
