Articulated movements are fundamental in many human and robotic tasks.While humans can learn and generalise arbitrarily long sequences of movements,and particularly can optimise them to ft the constraints and features...Articulated movements are fundamental in many human and robotic tasks.While humans can learn and generalise arbitrarily long sequences of movements,and particularly can optimise them to ft the constraints and features of their body,robots are often programmed to execute point-to-point precise but fxed patterns.This study proposes a new approach to interpreting and reproducing articulated and complex trajectories as a set of known robot-based primitives.Instead of achieving accurate reproductions,the proposed approach aims at interpreting data in an agent-centred fashion,according to an agent s primitive movements.The method improves the accuracy of a reproduction with an incremental process that seeks frst a rough approximation by capturing the most essential features of a demonstrated trajectory.Observing the discrepancy between the demonstrated and reproduced trajectories,the process then proceeds with incremental decompositions and new searches in sub-optimal parts of the trajectory.The aim is to achieve an agent-centred interpretation and progressive learning that fts in the frst place the robots capability,as opposed to a data-centred decomposition analysis.Tests on both geometric and human generated trajectories reveal that the use of own primitives results in remarkable robustness and generalisation properties of the method.In particular,because trajectories are understood and abstracted by means of agent-optimised primitives,the method has two main features： 1） Reproduced trajectories are general and represent an abstraction of the data.2） The algorithm is capable of reconstructing highly noisy or corrupted data without pre-processing thanks to an implicit and emergent noise suppression and feature detection.This study suggests a novel bio-inspired approach to interpreting,learning and reproducing articulated movements and trajectories.Possible applications include drawing,writing,movement generation,object manipulation,and other tasks where the performance requires human-like interpretation and generalisation capabilities.展开更多
基金supported by European Community s Seventh Framework Programme FP7/2007-2013,Challenge 2,Cognitive Systems,Interaction,Robotics(No.248311AMARSi)
文摘Articulated movements are fundamental in many human and robotic tasks.While humans can learn and generalise arbitrarily long sequences of movements,and particularly can optimise them to ft the constraints and features of their body,robots are often programmed to execute point-to-point precise but fxed patterns.This study proposes a new approach to interpreting and reproducing articulated and complex trajectories as a set of known robot-based primitives.Instead of achieving accurate reproductions,the proposed approach aims at interpreting data in an agent-centred fashion,according to an agent s primitive movements.The method improves the accuracy of a reproduction with an incremental process that seeks frst a rough approximation by capturing the most essential features of a demonstrated trajectory.Observing the discrepancy between the demonstrated and reproduced trajectories,the process then proceeds with incremental decompositions and new searches in sub-optimal parts of the trajectory.The aim is to achieve an agent-centred interpretation and progressive learning that fts in the frst place the robots capability,as opposed to a data-centred decomposition analysis.Tests on both geometric and human generated trajectories reveal that the use of own primitives results in remarkable robustness and generalisation properties of the method.In particular,because trajectories are understood and abstracted by means of agent-optimised primitives,the method has two main features： 1） Reproduced trajectories are general and represent an abstraction of the data.2） The algorithm is capable of reconstructing highly noisy or corrupted data without pre-processing thanks to an implicit and emergent noise suppression and feature detection.This study suggests a novel bio-inspired approach to interpreting,learning and reproducing articulated movements and trajectories.Possible applications include drawing,writing,movement generation,object manipulation,and other tasks where the performance requires human-like interpretation and generalisation capabilities.
