![]() Based on information obtained from functional imagination it decides whether it is necessary to adapt the executed plan. This integration is done by a Semantic Execution Monitor that generates information necessary for functional imagination from the planning domain. In essence we present a fully integrated system performing functional imagination. In this work the authors present an integrated system using a physics- based simulation to predict robot action results and durations, combined with a Hierarchical Task Network (HTN) planner and semantic execution monitoring. Some aspects of the world - such as object dynamics - are inherently difficult to capture in an abstract symbolic form, and yet they influence whether the executed action will succeed or fail. State-of-the-art cognitive robotic systems use an abstract symbolic representation of the real-world, which is used for high level reasoning. Real-world robotic systems have to deal with uncertain and dynamic environments in order to reliably perform tasks. This article outlines problems in different research areas related to mobile manipulation from the cognitive perspective, reviews recently published works and the state-of-the-art approaches to address these problems, and discusses open problems to be solved to realize robot assistants that can be used in manipulation tasks in unstructured human environments. In spite of the recent progress in these fields, there are still challenges to tackle. Designing skills for robots working in uncontrolled human environments raises many potential challenges in various subdisciplines, such as computer vision, automated planning, and human-robot interaction. Robots have been used successfully for manipulation tasks in wellstructured and controlled factory environments for decades. An important requirement for fulfilling this expectation is to equip robots with skills to perform everyday manipulation tasks, the success of which is crucial for most home chores, such as cooking, cleaning, and shopping. Service robots are expected to play an important role in our daily lives as our companions in home and work environments in the near future. This research presents a novel solution of how to merge activity experiences from multiple situations and generate an intelligent and efficient plan that could adapt to a dynamically changing environment. Owing to the improved autonomy, the proposed SBOP exhibits increased efficiency in dealing with tasks containing loops and multiple activity schema instances. The results show that the robot can generate a plan to recover from failure automatically using the novel learning and planning method, given that the experienced exception has been merged in the activity schema. A simulation with a PR2 robot and a physical experiment is conducted to validate the proposed method. ![]() Furthermore, a novel planner called the schema-based optimized planner (SBOP) is developed based on the learned activity schema, in which actions merging optimization and partially backtracking techniques are adopted. In this paper, an abstract method is introduced to integrate the empirical activity schemas of multiple situations, and a novel algorithm is presented to learn activity schema with abstract methods. Such techniques could potentially simplify the algorithmic complexity of programming multi-jointed robots, and also be capable of dynamically adjusting the “mental” simulation of the robot when it encounters environments with different gravity, viscosity or traction, merely by adjusting parameters of the simulated environment.įor a service robot, learning appropriate behaviours to acquire task knowledge and deliberation in various situations is essential, but the existing methods do not support merging the plan-based activity experiences from multiple situations in the same task. Moreover, as the complexity of motion increases with each degree of freedom of the robot’s joints, this paper also explores the utility of uniform pseudo-randomness to explore the fitness landscape of robot motility, and compares it with Computational Intelligence algorithms. The robot chooses the best possible action from multiple simulations of movement, and executes it in the real world. The physics of the environment simulates the movement of robot parts and its interaction with the objects in the environment and the terrain, thus avoiding the need for explicitly programming many calculations. ![]() This paper investigates the possibility of utilizing a physics simulation environment as the imagination of a robot, where it creates a replica of the detected terrain in a physics simulation environment in its memory, and “imagines” a simulated version of itself in that memory, performing actions and navigation on the terrain.
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