With recent advancements in Large Language Models, task planning methods that interpret human commands have garnered significant attention. However, as home robots become more common, specifying every daily task could become impractical. This paper introduces a novel semantic map called the Task-Aware Semantic Map (TASMap), which enables robots to autonomously assign and propose necessary tasks in a scene without explicit human commands. The core innovation of this approach is the ability of TASMap to comprehend the context of objects within a scene and autonomously generate task proposals. This capability significantly advances autonomous robotic assistance, reducing the dependency on specific commands and enhancing interaction with environments. We present two key applications of TASMap: contextual task proposal and spatial task proposal. Our results, verified across 35 diverse and realistically disordered scenes, underscore the effectiveness of TASMap in both simulation and real-world environments.

Real-to-sim-to-real systems have been studied to overcome the challenges of robot policy learning in the real world by creating a virtual environment that mimics the actual workspace. However, previous studies have limitations, requiring human assistance, such as observing the workspace with a hand-held camera or manipulating objects with a hand. To solve these limitations, we propose a novel real-to-sim-to-real framework, ARIC, that performs without human help. First, ARIC observes real objects by repeatedly changing the object poses through the pre-trained robot policy via reinforcement learning. Through iterative interactions between the robot and the environment, ARIC gradually improves the accuracy of 3D object reconstruction. Next, ARIC learns task-specific robot policies in simulation using replicated objects and applies the policies to real-world scenarios without fine-tuning. We confirm that ARIC efficiently learns robotic tasks by achieving a success rate of 83.3% on average for three real-world tasks.1

Recent systems biological studies of cardiac systems have greatly advanced our understanding of cardiac physiology with a particular focus on the excitation-contraction coupling. With these advancements, there is a growing interest in systems analysis of the cardiac signaling network because its dynamical property is closely associated with cardiac diseases. In this article, we review recent attempts at computational modeling of the cardiac signaling network and provide a system-level perspective on the analysis of the large-scale cardiac signaling network. We discuss why the systems biological approach is useful and what novel insights it can provide for the development of personalized therapeutic strategies for cardiac diseases in the post-genomic era.