Despite the multitude of excellent software components and tools available in the robotics and broader software engineering communities, successful integration of software for robotic systems remains a time-consuming and challenging task for users of all knowledge and skill levels. And with robotics software often being built into tightly coupled, monolithic systems, even minor alterations to improve performance, adjust to changing task requirements, or deploy to new hardware can require significant engineering investment. To help solve this problem, this paper presents Coral, an abstraction layer for building, deploying, and coordinating independent software components that maximizes composability to allow for rapid system integration without modifying low-level code. Rather than replacing existing tools, Coral complements them by introducing a higher-level abstraction that constrains the integration process to semantically meaningful choices, reducing the configuration burden without limiting adaptability to diverse domains, systems, and tasks. We describe Coral in detail and demonstrate its utility in integrating software for scenarios of increasing complexity, including LiDAR-based SLAM and multi-robot corrosion mitigation tasks. By enabling practical composability in robotics software, Coral offers a scalable solution to a broad range of robotics system integration challenges, improving component reusability, system reconfigurability, and accessibility to both expert and non-expert users. We release Coral open source.

Scripting interfaces enable users to automate tasks and customize software workflows, but creating scripts traditionally requires programming expertise and familiarity with specific APIs, posing barriers for many users. While Large Language Models (LLMs) can generate code from natural language queries, runtime code generation is severely limited due to unverified code, security risks, longer response times, and higher computational costs. To bridge the gap, we propose an offline simulation framework to curate a software-specific skillset, a collection of verified scripts, by exploiting LLMs and publicly available scripting guides. Our framework comprises two components: (1) task creation, using top-down functionality guidance and bottom-up API synergy exploration to generate helpful tasks; and (2) skill generation with trials, refining and validating scripts based on execution feedback. To efficiently navigate the extensive API landscape, we introduce a Graph Neural Network (GNN)-based link prediction model to capture API synergy, enabling the generation of skills involving underutilized APIs and expanding the skillset's diversity. Experiments with Adobe Illustrator demonstrate that our framework significantly improves automation success rates, reduces response time, and saves runtime token costs compared to traditional runtime code generation. This is the first attempt to use software scripting interfaces as a testbed for LLM-based systems, highlighting the advantages of leveraging execution feedback in a controlled environment and offering valuable insights into aligning AI capabilities with user needs in specialized software domains.

Empowerment has the potential to help agents learn large skillsets, but is not yet a scalable solution for training general-purpose agents. Recent empowerment methods learn diverse skillsets by maximizing the mutual information between skills and states; however, these approaches require a model of the transition dynamics, which can be challenging to learn in realistic settings with high-dimensional and stochastic observations. We present Latent-Predictive Empowerment (LPE), an algorithm that can compute empowerment in a more practical manner. LPE learns large skillsets by maximizing an objective that is a principled replacement for the mutual information between skills and states and that only requires a simpler latent-predictive model rather than a full simulator of the environment. We show empirically in a variety of settings--including ones with high-dimensional observations and highly stochastic transition dynamics--that our empowerment objective (i) learns similar-sized skillsets as the leading empowerment algorithm that assumes access to a model of the transition dynamics and (ii) outperforms other model-based approaches to empowerment.

Conditional Tabular Generative Adversarial Networks (CTGAN) and their various derivatives are attractive for their ability to efficiently and flexibly create synthetic tabular data, showcasing strong performance and adaptability. However, there are certain critical limitations to such models. The first is their inability to preserve the semantic integrity of contextually correlated words or phrases. For instance, skillset in freelancer profiles is one such attribute where individual skills are semantically interconnected and indicative of specific domain interests or qualifications. The second challenge of traditional approaches is that, when applied to generate contextually correlated tabular content, besides generating semantically shallow content, they consume huge memory resources and CPU time during the training stage. To address these problems, we introduce a novel framework, CTGKrEW (Conditional Tabular GAN with KMeans Clustering and Word Embedding), which is adept at generating realistic synthetic tabular data where attributes are collections of semantically and contextually coherent words. CTGKrEW is trained and evaluated using a dataset from Upwork, a realworld freelancing platform. Comprehensive experiments were conducted to analyze the variability, contextual similarity, frequency distribution, and associativity of the generated data, along with testing the framework's system feasibility. CTGKrEW also takes around 99\% less CPU time and 33\% less memory footprints than the conventional approach. Furthermore, we developed KrEW, a web application to facilitate the generation of realistic data containing skill-related information. This application, available at https://riyasamanta.github.io/krew.html, is freely accessible to both the general public and the research community.

Generation Z workers and workers and younger will exist in a very different world, where humans work alongside machines. That means that the future workforce have to excel at different things - including constant reinvention, and being noticed in the office, said Jim Moore, employment expert at HR consultants Hamilton Nash. Moore said: 'Elon Musk's recent claim that AI will create a future of no jobs will have struck fear into the hearts of many workers. Below, Moore reveals his strategy for showing you're more valuable than a computer program. How do you stop a robot from taking your job? (Rob Waugh/Midjourney) 'A report by Goldman Sachs suggested that AI could replace the equivalent of 300 million full-time jobs, and roles in the copywriting, voiceover and call center industries have already been affected,' Moore added.

The unprecedented changes witnessed globally in the recent past have transformed the way we think, interact, and work. Enterprises across the globe are going through cultural and structural shifts that requires them to reimagine and restructure their business processes. New Age technologies such as AI, ML, cloud, with their ability to connect processes, data, and people are revolutionizing the work culture. In such a scenario, enterprises need to reshape their operating models to accommodate this transition. Here are the five trends that are impacting the future of work globally.

Try it for free today and experience the power of AI-powered insights and guidance for yourself. No credit card or subscription required. Do more, faster, for less. Here are just a few examples of how Draftpad.ai We have a free trial available to get you started right away.

Jobs are changing – to the point that it's been predicted that 85% of the jobs college leavers of 2030 will have, haven't been invented yet. This means that skills will have to change, too. AI and automation will be a big driver of this as machines become capable of taking on more work. Rather than just automation of manual jobs, smart, artificial intelligence (AI)-powered machines will increasingly do jobs that require thought and decision-making. So, where does this leave humans?

In the early 19th century, textile workers in Nottingham rebelled against their factory owners As factory owners began to use new machinery that reduced the number of employees and factories they needed, workers felt that their skillset was being wasted and their livelihoods threatened. This rebellion was the Luddite movement. The term ‘Luddite’ has since been used to describe those who opposed industrialisation, automation, and in more recent times some cutting-edge technologies threatening to disrupt the mainstream. When it comes to artificial intelligence (AI), you can sympathise with the Luddite philosophy to an extent. The idea that we can teach

The need for high-level autonomy and robustness of autonomous systems for missions in dynamic and remote environment has pushed developers to come up with new software architectures. A common architecture style is to summarize the capabilities of the robotic system into elementary actions, called skills, on top of which a skill management layer is implemented to structure, test and control the functional layer. However, current available verification tools only provide either mission-specific verification or verification on a model that does not replicate the actual execution of the system, which makes it difficult to ensure its robustness to unexpected events. To that end, a tool, SkiNet, has been developed to transform the skill-based architecture of a system into a Petri net modeling the state-machine behaviors of the skills and the resources they handle. The Petri net allows the use of model-checking, such as Linear Temporal Logic (LTL) or Computational Tree Logic (CTL), for the user to analyze and verify the model of the system.