IEEE Transactions on Cognitive and Developmental Systems, 2018
This paper introduces a cognitive architecture for a humanoid robot to engage in a proactive, mix... more This paper introduces a cognitive architecture for a humanoid robot to engage in a proactive, mixed-initiative exploration and manipulation of its environment, where the initiative can originate from both the human and the robot. The framework, based on a biologically-grounded theory of the brain and mind, integrates a reactive interaction engine, a number of state-of-the art perceptual and motor learning algorithms, as well as planning abilities and an autobiographical memory. The architecture as a whole drives the robot behavior to solve the symbol grounding problem, acquire language capabilities, execute goal-oriented behavior, and express a verbal narrative of its own experience in the world. We validate our approach in human-robot interaction experiments with the iCub humanoid robot, showing that the proposed cognitive architecture can be applied in real time within a realistic scenario and that it can be used with naive users.
ABSTRACT From automata to robots, the Human has always been fascinated by machines which could ex... more ABSTRACT From automata to robots, the Human has always been fascinated by machines which could execute tasks for him, in several domains like industry or services. Indeed, we have used a developmental approach, where the robot has to learn new tasks during his life. Inspired by theories in child development, we have extracted the interesting concepts to implement them on a humanoid robotic platform : the iCub. Language acquisition is a first step, where the capacity to classify closed and opened class words allows to obtain a syntax which help the children to make the link between a sentence and its meaning. This method has been implemented with a recurrent neural network, using a database provided from the human by interaction with the robot. The control of the language allows the children to participate in more complex actions, in particular cooperative tasks, where speech is required to negotiate the learning mode within several modalities. Implemented on the iCub and the Nao, this allows a real-time learning and to realize a shared plan. Eventually, we have studied the functioning of the autobiographical memory, crucial toremember episodes of his life, to extract predictions from and to apply them in the future. By recreating this memory in SQL, and by formatting the data in PDDL, the iCub is then capable of reasoning in function of his own experience, allowing him to solve the Tower of Hanoi problem without knowing the solution before
IEEE Transactions on Cognitive and Developmental Systems, 2018
This paper introduces a cognitive architecture for a humanoid robot to engage in a proactive, mix... more This paper introduces a cognitive architecture for a humanoid robot to engage in a proactive, mixed-initiative exploration and manipulation of its environment, where the initiative can originate from both the human and the robot. The framework, based on a biologically-grounded theory of the brain and mind, integrates a reactive interaction engine, a number of state-of-the art perceptual and motor learning algorithms, as well as planning abilities and an autobiographical memory. The architecture as a whole drives the robot behavior to solve the symbol grounding problem, acquire language capabilities, execute goal-oriented behavior, and express a verbal narrative of its own experience in the world. We validate our approach in human-robot interaction experiments with the iCub humanoid robot, showing that the proposed cognitive architecture can be applied in real time within a realistic scenario and that it can be used with naive users.
ABSTRACT From automata to robots, the Human has always been fascinated by machines which could ex... more ABSTRACT From automata to robots, the Human has always been fascinated by machines which could execute tasks for him, in several domains like industry or services. Indeed, we have used a developmental approach, where the robot has to learn new tasks during his life. Inspired by theories in child development, we have extracted the interesting concepts to implement them on a humanoid robotic platform : the iCub. Language acquisition is a first step, where the capacity to classify closed and opened class words allows to obtain a syntax which help the children to make the link between a sentence and its meaning. This method has been implemented with a recurrent neural network, using a database provided from the human by interaction with the robot. The control of the language allows the children to participate in more complex actions, in particular cooperative tasks, where speech is required to negotiate the learning mode within several modalities. Implemented on the iCub and the Nao, this allows a real-time learning and to realize a shared plan. Eventually, we have studied the functioning of the autobiographical memory, crucial toremember episodes of his life, to extract predictions from and to apply them in the future. By recreating this memory in SQL, and by formatting the data in PDDL, the iCub is then capable of reasoning in function of his own experience, allowing him to solve the Tower of Hanoi problem without knowing the solution before
Various research topics are emerging as the demand for intelligent lifelong interactions between ... more Various research topics are emerging as the demand for intelligent lifelong interactions between robot and humans increases. Among them, we can find the examination of persistent storage, the continuous unsupervised annotation of memories and the usage of data at high-frequency over long periods of time. We recently proposed a lifelong autobiographical memory architecture tackling some of these challenges, allowing the iCub humanoid robot to 1) create new memories for both actions that are self-executed and observed from humans, 2) continuously annotate these actions in an unsupervised manner, and 3) use reasoning modules to augment these memories a-posteriori. In this paper, we present a reasoning algorithm which generalises the robots' understanding of actions by finding the point of commonalities with the former ones. In particular, we generated and labelled templates of pointing actions in different directions. This represents a first step towards the emergence of a procedural memory within a long-term autobiographical memory framework for robots.
In this paper, we present a novel framework for finding the kinematic structure correspondence be... more In this paper, we present a novel framework for finding the kinematic structure correspondence between two objects in videos via hypergraph matching. In contrast to prior appearance and graph alignment based matching methods which have been applied among two similar static images, the proposed method finds correspondences between two dynamic kinematic structures of heterogeneous objects in videos. Our main contributions can be summarised as follows: (i) casting the kinematic structure correspondence problem into a hypergraph matching problem, incorporating multi-order similarities with normalising weights, (ii) a structural topology similarity measure by a new topology constrained subgraph isomorphism aggregation, (iii) a kinematic correlation measure between pairwise nodes, and (iv) a combinatorial local motion similarity measure using geodesic distance on the Riemannian manifold. We demonstrate the robustness and accuracy of our method through a number of experiments on complex articulated synthetic and real data.
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Papers by Maxime Petit