RESUMEN
Autonomous indoor service robots are affected by multiple factors when they are directly involved in manipulation tasks in daily life, such as scenes, objects, and actions. It is of self-evident importance to properly parse these factors and interpret intentions according to human cognition and semantics. In this study, the design of a semantic representation framework based on a knowledge graph is presented, including (1) a multi-layer knowledge-representation model, (2) a multi-module knowledge-representation system, and (3) a method to extract manipulation knowledge from multiple sources of information. Moreover, with the aim of generating semantic representations of entities and relations in the knowledge base, a knowledge-graph-embedding method based on graph convolutional neural networks is proposed in order to provide high-precision predictions of factors in manipulation tasks. Through the prediction of action sequences via this embedding method, robots in real-world environments can be effectively guided by the knowledge framework to complete task planning and object-oriented transfer.
RESUMEN
The rechargeable lithium-sulfur battery is recognized as a promising candidate for electrochemical energy storage system because of their exceptional advance in energy density. However, the fast capacity decay of sulfur cathode caused by polysulfide dissolution and low specific capacity caused by poor electrical conductivity still impede the further development of lithium-sulfur battery. To address above issues, this study reports the synthesis of feather duster-like TiO2 architecture by in situ growth of TiO2 nanowires on carbon cloth and further evaluates as sulfur host material. The strong chemical binding interaction between the polysulfides and TiO2 feather duster efficiently restrains the shuttle effect, leading to enhanced electrochemical kinetics. Besides, the in situ grown TiO2 NWs array also supply high surface for sulfur-loading and fast path for electron transfer and ion diffusion. As results, the novel CC/TiO2 /S composite cathode exhibits a high capacity of 608 mA h g-1 at 1.0 C after 700 cycles corresponding to capacity decay as low as 0.045% per cycle with excellent Coulombic efficiency higher than 99.5%.
RESUMEN
The utilization of robots in computer, communication, and consumer electronics (3C) assembly has the potential to significantly reduce labor costs and enhance assembly efficiency. However, many typical scenarios in 3C assembly, such as the assembly of flexible printed circuits (FPCs), involve complex manipulations with long-horizon steps and high-precision requirements that cannot be effectively accomplished through manual programming or conventional skill-learning methods. To address this challenge, this article proposes a learning-based framework for the acquisition of complex 3C assembly skills assisted by a multimodal digital-twin environment. First, we construct a fully equivalent digital-twin environment based on the real-world counterpart, equipped with visual, tactile force, and proprioception information, and then collect multimodal demonstration data using virtual reality (VR) devices. Next, we construct a skill knowledge base through multimodal skill parsing of demonstration data, resulting in primitive policy sequences for achieving 3C assembly tasks. Finally, we train primitive policies via a combination of curriculum learning, residual reinforcement learning, and domain randomization methods and transfer the learned skill from the digital-twin environment to the real-world environment. The experiments are conducted to verify the effectiveness of our proposed method.