4 research outputs found

    DREAM Architecture: a Developmental Approach to Open-Ended Learning in Robotics

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    Robots are still limited to controlled conditions, that the robot designer knows with enough details to endow the robot with the appropriate models or behaviors. Learning algorithms add some flexibility with the ability to discover the appropriate behavior given either some demonstrations or a reward to guide its exploration with a reinforcement learning algorithm. Reinforcement learning algorithms rely on the definition of state and action spaces that define reachable behaviors. Their adaptation capability critically depends on the representations of these spaces: small and discrete spaces result in fast learning while large and continuous spaces are challenging and either require a long training period or prevent the robot from converging to an appropriate behavior. Beside the operational cycle of policy execution and the learning cycle, which works at a slower time scale to acquire new policies, we introduce the redescription cycle, a third cycle working at an even slower time scale to generate or adapt the required representations to the robot, its environment and the task. We introduce the challenges raised by this cycle and we present DREAM (Deferred Restructuring of Experience in Autonomous Machines), a developmental cognitive architecture to bootstrap this redescription process stage by stage, build new state representations with appropriate motivations, and transfer the acquired knowledge across domains or tasks or even across robots. We describe results obtained so far with this approach and end up with a discussion of the questions it raises in Neuroscience

    Real-time optimization of working memory in autonomous reasoning for high-level control of cognitive robots deployed in dynamic environments

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    High-level, real-time mission control of autonomous and semi-autonomous robots, deployed in remote and dynamic environments, remains a research challenge. Robots operating in these environments require some cognitive ability, provided by a simple, but robust, cognitive architecture. The most important process in a cognitive architecture is the working memory, with core functions being memory representation, memory recall, action selection and action execution, performed by the central executive. The cognitive reasoning process uses a memory representation, based on state flows, governed by state transitions with simple, quantified propositional transition formulae. In this thesis, real-time working memory quantification and optimization is performed using a novel adaptive entropy-based fitness quantification (AEFQ) algorithm and particle swarm optimization (PSO), respectively. A cognitive architecture, using an improved set-based PSO is developed for real-time, high-level control of single-task robots and a novel coalitional games-theoretic PSO (CG-PSO) algorithm extends the cognitive architecture for real-time, high-level control in multi-task robots. The performance of the cognitive architecture is evaluated by simulation, where a UAV executesfour use cases: Firstly, for real-time high-level, single-task control: 1) relocating the UAV to a charging station and 2) collecting and delivering medical equipment. Performance is measured by inspecting the success and completeness of the mission and the accuracy of autonomous flight control. Secondly, for real-time high-level control of multi-task autonomous vehicle control: 3) delivering medical equipment to an incident and 4) provide aerial security surveillance support. The performance of the architecture is measured in terms of completeness and cognitive processing time and cue processing time. The results show that coalitions correctly represent optimal memory and action selection in real-time, while overall processing time is within a feasible time limit, arbitrarily set to 2 seconds in this study
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