Existing biomimetic robots can perform some basic rat-like movement primitives(MPs)and simple behavior with stiff combinations of these MPs.To mimic typical rat behavior with high similarity,we propose parameterizing ...Existing biomimetic robots can perform some basic rat-like movement primitives(MPs)and simple behavior with stiff combinations of these MPs.To mimic typical rat behavior with high similarity,we propose parameterizing the behavior using a probabilistic model and movement characteristics.First,an analysis of fifteen 10 min video sequences revealed that an actual rat has six typical behaviors in the open field,and each kind of behavior contains different bio-inspired combinations of eight MPs.We used the softmax classifier to obtain the behavior-movement hierarchical probability model.Secondly,we specified the MPs using movement parameters that are static and dynamic.We obtained the predominant values of the static and dynamic movement parameters using hierarchical clustering and fuzzy C-means clustering,respectively.These predominant parameters were used for fitting the rat spinal joint trajectory using a second-order Fourier series,and the joint trajectory was generalized using a back propagation neural network with two hidden layers.Finally,the hierarchical probability model and the generalized joint trajectory were mapped to the robot as control policy and commands,respectively.We implemented the six typical behaviors on the robot,and the results show high similarity when compared with the behaviors of actual rats.展开更多
In this paper,we propose a novel method for emulating rat-like behavioral interactions in robots using reinforcement learning.Specifically,we develop a state decision method to optimize the interaction process among 6...In this paper,we propose a novel method for emulating rat-like behavioral interactions in robots using reinforcement learning.Specifically,we develop a state decision method to optimize the interaction process among 6 known behavior types that have been identified in previous research on rat interactions.The novelty of our method lies in using the temporal difference(TD)algorithm to optimize the state decision process,which enables the robots to make informed decisions about their behavior choices.To assess the similarity between robot and rat behavior,we use Pearson correlation.We then use TD-λto update the state value function and make state decisions based on probability.The robots execute these decisions using our dynamics-based controller.Our results demonstrate that our method can generate rat-like behaviors on both short-and long-term timescales,with interaction information entropy comparable to that between real rats.Overall,our approach shows promise for controlling robots in robot-rat interactions and highlights the potential of using reinforcement learning to develop more sophisticated robotic systems.展开更多
基金supported in part by the National Natural Science Foundation of China(62022014)in part by the National Key Research and Development Program of China(2017YFE0117000)。
文摘Existing biomimetic robots can perform some basic rat-like movement primitives(MPs)and simple behavior with stiff combinations of these MPs.To mimic typical rat behavior with high similarity,we propose parameterizing the behavior using a probabilistic model and movement characteristics.First,an analysis of fifteen 10 min video sequences revealed that an actual rat has six typical behaviors in the open field,and each kind of behavior contains different bio-inspired combinations of eight MPs.We used the softmax classifier to obtain the behavior-movement hierarchical probability model.Secondly,we specified the MPs using movement parameters that are static and dynamic.We obtained the predominant values of the static and dynamic movement parameters using hierarchical clustering and fuzzy C-means clustering,respectively.These predominant parameters were used for fitting the rat spinal joint trajectory using a second-order Fourier series,and the joint trajectory was generalized using a back propagation neural network with two hidden layers.Finally,the hierarchical probability model and the generalized joint trajectory were mapped to the robot as control policy and commands,respectively.We implemented the six typical behaviors on the robot,and the results show high similarity when compared with the behaviors of actual rats.
基金supported by the National Natural Science Foundation of China under grant number 62022014the Science and Technology Innovation Program of Beijing Institute of Technology under grant number 2022CX01010.
文摘In this paper,we propose a novel method for emulating rat-like behavioral interactions in robots using reinforcement learning.Specifically,we develop a state decision method to optimize the interaction process among 6 known behavior types that have been identified in previous research on rat interactions.The novelty of our method lies in using the temporal difference(TD)algorithm to optimize the state decision process,which enables the robots to make informed decisions about their behavior choices.To assess the similarity between robot and rat behavior,we use Pearson correlation.We then use TD-λto update the state value function and make state decisions based on probability.The robots execute these decisions using our dynamics-based controller.Our results demonstrate that our method can generate rat-like behaviors on both short-and long-term timescales,with interaction information entropy comparable to that between real rats.Overall,our approach shows promise for controlling robots in robot-rat interactions and highlights the potential of using reinforcement learning to develop more sophisticated robotic systems.
