The growing demand for performance, energy efficiency, and domain-specific acceleration has driven a shift toward heterogeneous computing platforms. While hyperscaler cloud platforms offer broad access to compute resources, they often lack the flexibility required by advanced research workloads. In response, research institutions are increasingly deploying self-managed heterogeneous clusters, which remain difficult to maintain, configure, and use effectively. The Heterogeneous Accelerated Compute Clusters (HACCs) initiative, supported by the AMD University Program, addresses this by equipping select institutions with clusters composed of CPUs, GPUs, and adaptive devices for high-performance computing and AI research. This article presents two complementary tools developed for such systems: HACC Management and Development. HACC Management is an Ansible-based automation framework for provisioning and maintaining HACC-like infrastructure in a reproducible and scalable way. HACC Development ( hdev ) is a lightweight Bash-based CLI that streamlines the development and deployment of heterogeneous applications. It orchestrates both environment and infrastructure setup and provides ready-to-use templates—example projects for user reference—that enable rapid iteration on real hardware. We demonstrate the design and capabilities of both tools using ETH Zurich’s HACC as a case study. Our approach reduces operational overhead, enables faster prototyping, and provides a foundation for community-driven expansion of heterogeneous workflow support.

The appropriate management of fall situations - i.e. fast instability detection, avoidance of unintentional falls, falling without damaging the body, fast recovering of the standing position after a fall - is an essential ability of biped humanoid robots. This issue is especially important for humanoid robots carrying out demanding movements such as walking in irregular surfaces, running or practicing a given sport (e.g. soccer). In a former contribution we have addressed the design of low-damage fall sequences, which can be activated/triggered by the robot in case of a detected unintentional fall or an intentional fall (common situation in robot soccer). In this article we tackle the detection of instability and the avoidance of falls in biped humanoids, as well as the integration of all components in a single framework. In this framework a fall can be avoided or a falling sequence can be triggered depending on the detected instability's degree. The proposed fall detection and fall avoidance subsystems are validated in real world-experiments with biped humanoid robots.

This paper is a step forward in my research work about creating more intelligence in wireless teleoperation on hydraulics mobile machines. This paper deals with the importance of synchronization in the data transference between different elements to provide a reliable connection during the teleoperation process. A new module (G.P.S. receiver) is installed into the system to provide information about location and heading of the hydraulic mobile machine. In the future this information will be used to enable more autonomous teleoperation.