Transparency is a widely used but poorly defined term within the explainable artificial intelligence literature.This is due,in part,to the lack of an agreed definition and the overlap between the connected—sometimes ...Transparency is a widely used but poorly defined term within the explainable artificial intelligence literature.This is due,in part,to the lack of an agreed definition and the overlap between the connected—sometimes used synonymously—concepts of interpretability and explainability.We assert that transparency is the overarching concept,with the tenets of interpretability,explainability,and predictability subordinate.We draw on a portfolio of definitions for each of these distinct concepts to propose a human-swarm-teaming transparency and trust architecture(HST3-Architecture).The architecture reinforces transparency as a key contributor towards situation awareness,and consequently as an enabler for effective trustworthy human-swarm teaming(HST).展开更多
Background A large number of robots have put forward the new requirements for human robot interaction.One of the problems in human-swarm robot interaction is how to naturally achieve an efficient and accurate interact...Background A large number of robots have put forward the new requirements for human robot interaction.One of the problems in human-swarm robot interaction is how to naturally achieve an efficient and accurate interaction between humans and swarm robot systems.To address this,this paper proposes a new type of human-swarm natural interaction system.Methods Through the cooperation between three-dimensional(3D)gesture interaction channel and natural language instruction channel,a natural and efficient interaction between a human and swarm robots is achieved.Results First,A 3D lasso technology realizes a batch-picking interaction of swarm robots through oriented bounding boxes.Second,control instruction labels for swarm-oriented robots are defined.The instruction label is integrated with the 3D gesture and natural language through instruction label filling.Finally,the understanding of natural language instructions is realized through a text classifier based on the maximum entropy model.A head-mounted augmented reality display device is used as a visual feedback channel.Conclusions The experiments on selecting robots verify the feasibility and availability of the system.展开更多
基金United States Office of Naval Research-Global(ONR-G)(N629091812140)。
文摘Transparency is a widely used but poorly defined term within the explainable artificial intelligence literature.This is due,in part,to the lack of an agreed definition and the overlap between the connected—sometimes used synonymously—concepts of interpretability and explainability.We assert that transparency is the overarching concept,with the tenets of interpretability,explainability,and predictability subordinate.We draw on a portfolio of definitions for each of these distinct concepts to propose a human-swarm-teaming transparency and trust architecture(HST3-Architecture).The architecture reinforces transparency as a key contributor towards situation awareness,and consequently as an enabler for effective trustworthy human-swarm teaming(HST).
基金Key-Area Research and Development Program of Guangdong Province(2019B090915002).
文摘Background A large number of robots have put forward the new requirements for human robot interaction.One of the problems in human-swarm robot interaction is how to naturally achieve an efficient and accurate interaction between humans and swarm robot systems.To address this,this paper proposes a new type of human-swarm natural interaction system.Methods Through the cooperation between three-dimensional(3D)gesture interaction channel and natural language instruction channel,a natural and efficient interaction between a human and swarm robots is achieved.Results First,A 3D lasso technology realizes a batch-picking interaction of swarm robots through oriented bounding boxes.Second,control instruction labels for swarm-oriented robots are defined.The instruction label is integrated with the 3D gesture and natural language through instruction label filling.Finally,the understanding of natural language instructions is realized through a text classifier based on the maximum entropy model.A head-mounted augmented reality display device is used as a visual feedback channel.Conclusions The experiments on selecting robots verify the feasibility and availability of the system.
