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Task space retrieval using inverse feedback control
- In ICML
, 2011
"... Learning complex skills by repeating and generalizing expert behavior is a fundamental problem in robotics. A common approach is learning from demonstration: given examples of correct motions, learn a policy mapping state to action consistent with the training data. However, the usual approaches do ..."
Abstract
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Cited by 14 (2 self)
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Learning complex skills by repeating and generalizing expert behavior is a fundamental problem in robotics. A common approach is learning from demonstration: given examples of correct motions, learn a policy mapping state to action consistent with the training data. However, the usual approaches do not answer the question of what are appropriate representations to generate motions for specific tasks. Inspired by Inverse Optimal Control, we present a novel method to learn latent costs, imitate and generalize demonstrated behavior, and discover a task relevant
Using a Model of the Reachable Workspace to Position Mobile Manipulators for 3-d Trajectories
"... Abstract — Humanoid robots are envisioned in general household tasks. To be able to fulfill a given task the robot needs to be equipped with knowledge concerning the manipulation and interaction in the environment and with knowledge about its own capabilities. When performing actions, e.g. opening d ..."
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Cited by 4 (2 self)
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Abstract — Humanoid robots are envisioned in general household tasks. To be able to fulfill a given task the robot needs to be equipped with knowledge concerning the manipulation and interaction in the environment and with knowledge about its own capabilities. When performing actions, e.g. opening doors or imitating human reach to grasp movements special 3-d trajectories are followed with the robot’s end-effector. These trajectories can not be executed in every part of the robot’s arm workspace. Therefore a task planner has to determine if and how additional degrees of freedom such as the robot’s upper body or the robot’s base can be moved in order to execute the task-specific trajectory. An approach is presented that computes placements for a mobile manipulator online given a task-related 3-d trajectory. A discrete representation of the robot arm’s reachable workspace is used. Task-specific trajectories are interpreted as patterns and searched in the reachability model using multi-dimensional correlation. The relevance of the presented approach is demonstrated in simulated positioning tasks. I.
Integrated motor control
"... planning, grasping and high-level reasoning in a blocks world using probabilistic inference ..."
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planning, grasping and high-level reasoning in a blocks world using probabilistic inference
The Facilitatory Role of Linguistic Instructions on Developing Manipulation Skills
"... In this paper, we show how a simulated humanoid robot controlled by an artificial neural network can acquire the ability to manipulate spherical objects located over a table by reaching, grasping, and lifting them. The robot controller is developed through an adaptive process in which the free param ..."
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In this paper, we show how a simulated humanoid robot controlled by an artificial neural network can acquire the ability to manipulate spherical objects located over a table by reaching, grasping, and lifting them. The robot controller is developed through an adaptive process in which the free parameters encode the control rules that regulate the fine-grained interaction between the agent and the environment, and the variations of these free parameters are retained or discarded on the basis of their effects at the level of the behaviour exhibited by the agent. The robot develops the sensory-motor coordination required to carry out the task in two different conditions; that is, with or without receiving as input a linguistic instruction that specifies the type of behaviour to be exhibited during the current phase. The obtained results shown that the linguistic instructions facilitate the development of the required behavioural skills. I.
Learning
"... Learning complex skills by repeating and generalizing expert behavior is a fundamental problem in robotics. However, the usual approaches do not answer the ques-tion of what are appropriate representations to generate motion for a specific task. Since it is time-consuming for a human expert to manua ..."
Abstract
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Learning complex skills by repeating and generalizing expert behavior is a fundamental problem in robotics. However, the usual approaches do not answer the ques-tion of what are appropriate representations to generate motion for a specific task. Since it is time-consuming for a human expert to manually design the motion con-trol representation for a task, we propose to uncover such structure from data – observed motion trajecto-ries. Inspired by Inverse Optimal Control, we present a novel method to learn a latent value function, im-itate and generalize demonstrated behavior, and dis-cover a task relevant motion representation. We test our method, called Task Space Retrieval Using Inverse Feedback Control (TRIC), on several challenging high-dimensional tasks. TRIC learns the important control dimensions for the tasks from a few example movements and is able to robustly generalize to new situations.
