Directed acyclic graph (DAG) tasks are currently adopted in the real-time domain to model complex applications from the automotive, avionics, and industrial domains that implement their functionalities through chains of intercommunicating tasks. This paper studies the problem of scheduling real-time DAG tasks by presenting a novel schedulability test based on the concept of trivial schedulability . Using this schedulability test, we propose a new DAG scheduling framework ( edge generation scheduling—EGS ) that attempts to minimize the DAG width by iteratively generating edges while guaranteeing the deadline constraint. We study how to efficiently solve the problem of generating edges by developing a deep reinforcement learning algorithm combined with a graph representation neural network to learn an efficient edge generation policy for EGS. We evaluate the effectiveness of the proposed algorithm by comparing it with state-of-the-art DAG scheduling heuristics and an optimal mixed-integer linear programming baseline. Experimental results show that the proposed algorithm outperforms the state-of-the-art by requiring fewer processors to schedule the same DAG tasks. https://github.com/binqi-sun/egs

Modern cyber-physical systems are becoming increasingly complex to model, thus motivating data-driven techniques such as reinforcement learning (RL) to find appropriate control agents. However, most systems are subject to hard constraints such as safety or operational bounds. Typically, to learn to satisfy these constraints, the agent must violate them systematically, which is computationally prohibitive in most systems. Recent efforts aim to utilize feasibility models that assess whether a proposed action is feasible to avoid applying the agent’s infeasible action proposals to the system. However, these efforts focus on guaranteeing constraint satisfaction rather than the agent’s learning efficiency. To improve the learning process, we introduce action mapping , a novel approach that divides the learning process into two steps: first learn feasibility and subsequently, the objective by mapping actions into the sets of feasible actions. This paper focuses on the feasibility part by learning to generate all feasible actions through self-supervised querying of the feasibility model. We train the agent by formulating the problem as a distribution matching problem and deriving gradient estimators for different divergences. Through an illustrative example, a robotic path planning scenario, and a robotic grasping simulation, we demonstrate the agent’s proficiency in generating actions across disconnected feasible action sets. By addressing the feasibility step, this paper makes it possible to focus future work on the objective part of action mapping, paving the way for an RL framework that is both safe and efficient.

6D pose recognition has been a crucial factor in the success of robotic grasping, and recent deep learning based approaches have achieved remarkable results on benchmarks. However, their generalization capabilities in real-world applications remain unclear. To overcome this gap, we introduce 6IMPOSE, a novel framework for sim-to-real data generation and 6D pose estimation. 6IMPOSE consists of four modules: First, a data generation pipeline that employs the 3D software suite Blender to create synthetic RGBD image datasets with 6D pose annotations. Second, an annotated RGBD dataset of five household objects generated using the proposed pipeline. Third, a real-time two-stage 6D pose estimation approach that integrates the object detector YOLO-V4 and a streamlined, real-time version of the 6D pose estimation algorithm PVN3D optimized for time-sensitive robotics applications. Fourth, a codebase designed to facilitate the integration of the vision system into a robotic grasping experiment. Our approach demonstrates the efficient generation of large amounts of photo-realistic RGBD images and the successful transfer of the trained inference model to robotic grasping experiments, achieving an overall success rate of 87% in grasping five different household objects from cluttered backgrounds under varying lighting conditions. This is made possible by the fine-tuning of data generation and domain randomization techniques, and the optimization of the inference pipeline, overcoming the generalization and performance shortcomings of the original PVN3D algorithm. Finally, we make the code, synthetic dataset, and all the pretrained models available on Github.

With the increasing popularity of photovoltaic (PV) equipment in residential and commercial buildings, there is a pressing need for systems that maximize energy efficiency and self-consumption. This paper introduces an integrated management framework for retrofitting existing infrastructures, enabling high photovoltaic (PV) self-consumption in residential buildings through a rule-based control strategy. The framework supports three service classes—defined by user-level Quality of Service (QoS) parameters—and monitors battery voltage along with grid power exchange to coordinate heat pumps, batteries, and hot water cylinders. Experimental deployment in a residential testbed achieved up to 89% PV self-consumption while keeping daily grid usage below 0.5 kWh. Ablation experiments on battery size further demonstrated the approach’s robustness to reduced storage capacities. The use of Commercial-Off-The-Shelf (COTS) components underscores the minimal intrusiveness of this solution, highlighting its potential for seamlessly integrating diverse, vendor-specific equipment into a coordinated control system.

As photovoltaic (PV) equipment gains traction in residential and commercial buildings, the need for systems optimizing energy efficiency and self-consumption becomes critical. This paper presents an integrated management framework that utilizes Commercial-Off-The-Shelf (COTS) components to maximize PV self-consumption in residential settings. The framework employs reactive activation rules based on sensor readings and battery charge levels, modulating consumption across three quality of service levels. Preliminary experimental results indicate a self-consumption rate exceeding 65%, while simulations suggest rates up to 98% are achievable with optimized battery capacity.