Abstract

Purpose Autonomous robots must be able to understand long-term manipulation tasks described by humans and perform task analysis and planning based on the current environment in a variety of scenes, such as daily manipulation and industrial assembly. However, both classical task and motion planning algorithms and single data-driven learning planning methods have limitations in practicability, generalization and interpretability. The purpose of this work is to overcome the limitations of the above methods and achieve generalized and explicable long-term robot manipulation task planning. Design/methodology/approach The authors propose a planning method for long-term manipulation tasks that combines the advantages of existing methods and the prior cognition brought by the knowledge graph. This method integrates visual semantic understanding based on scene graph generation, regression planning based on deep learning and multi-level representation and updating based on a knowledge base. Findings The authors evaluated the capability of this method in a kitchen cooking task and tabletop arrangement task in simulation and real-world environments. Experimental results show that the proposed method has a significantly improved success rate compared with the baselines and has excellent generalization performance for new tasks. Originality/value The authors demonstrate that their method is scalable to long-term manipulation tasks with varying complexity and visibility. This advantage allows their method to perform better in new manipulation tasks. The planning method proposed in this work is meaningful for the present robot manipulation task and can be intuitive for similar high-level robot planning.

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-In virtual reality, the fast rendering of trees is always a difficult part of scene rendering, so it is a challenge to render a large scale of trees with rich geometric details. We present a new method to get fast and real rendering. The LOD technique and Billboard technique are both combined in applying in the rendering method based on OSG to realize the fast requirement, and GPU technique is adopted to realize the swinging effect, in order to be more real, shadow map technique is also utilized in the method. The method is proved to be feasible to simulate large scale of tree scene.