Tactile sensing is fundamental to robotic systems, enabling interactions through physical contact in multiple tasks. Despite its importance, achieving high-resolution, large-area tactile sensing remains challenging. Electrical Impedance Tomography (EIT) has emerged as a promising approach for large-area, distributed tactile sensing with minimal electrode requirements which can lend itself to addressing complex contact problems in robotics. However, existing EIT-based tactile reconstruction methods often suffer from high computational costs or depend on extensive annotated simulation datasets, hindering its viability in real-world settings. To address this shortcoming, here we propose a Pre-trained Transformer for EIT-based Tactile Reconstruction (PTET), a learning-based framework that bridges the simulation-to-reality gap by leveraging self-supervised pretraining on simulation data and fine-tuning with limited real-world data. In simulations, PTET requires 99.44 percent fewer annotated samples than equivalent state-of-the-art approaches (2,500 vs. 450,000 samples) while achieving reconstruction performance improvements of up to 43.57 percent under identical data conditions. Fine-tuning with real-world data further enables PTET to overcome discrepancies between simulated and experimental datasets, achieving superior reconstruction and detail recovery in practical scenarios. The improved reconstruction accuracy, data efficiency, and robustness in real-world tasks establish it as a scalable and practical solution for tactile sensing systems in robotics, especially for object handling and adaptive grasping under varying pressure conditions.

This paper addresses the scarcity of large-scale datasets for accurate object-in-hand pose estimation, which is crucial for robotic in-hand manipulation within the ``Perception-Planning-Control" paradigm. Specifically, we introduce VinT-6D, the first extensive multi-modal dataset integrating vision, touch, and proprioception, to enhance robotic manipulation. VinT-6D comprises 2 million VinT-Sim and 0.1 million VinT-Real splits, collected via simulations in MuJoCo and Blender and a custom-designed real-world platform. This dataset is tailored for robotic hands, offering models with whole-hand tactile perception and high-quality, well-aligned data. To the best of our knowledge, the VinT-Real is the largest considering the collection difficulties in the real-world environment so that it can bridge the gap of simulation to real compared to the previous works. Built upon VinT-6D, we present a benchmark method that shows significant improvements in performance by fusing multi-modal information. The project is available at https://VinT-6D.github.io/.

Capacitive touch sensors capture the two-dimensional spatial profile (referred to as a touch heatmap) of a finger's contact with a mobile touchscreen. However, the research and design of touchscreen mobile keyboards -- one of the most speed and accuracy demanding touch interfaces -- has focused on the location of the touch centroid derived from the touch image heatmap as the input, discarding the rest of the raw spatial signals. In this paper, we investigate whether touch heatmaps can be leveraged to further improve the tap decoding accuracy for mobile touchscreen keyboards. Specifically, we developed and evaluated machine-learning models that interpret user taps by using the centroids and/or the heatmaps as their input and studied the contribution of the heatmaps to model performance. The results show that adding the heatmap into the input feature set led to 21.4% relative reduction of character error rates on average, compared to using the centroid alone. Furthermore, we conducted a live user study with the centroid-based and heatmap-based decoders built into Pixel 6 Pro devices and observed lower error rate, faster typing speed, and higher self-reported satisfaction score based on the heatmap-based decoder than the centroid-based decoder. These findings underline the promise of utilizing touch heatmaps for improving typing experience in mobile keyboards.

Large Language Models (LLMs) have showcased remarkable capabilities surpassing conventional NLP challenges, creating opportunities for use in production use cases. Towards this goal, there is a notable shift to building compound AI systems, wherein LLMs are integrated into an expansive software infrastructure with many components like models, retrievers, databases and tools. In this paper, we introduce a blueprint architecture for compound AI systems to operate in enterprise settings cost-effectively and feasibly. Our proposed architecture aims for seamless integration with existing compute and data infrastructure, with ``stream'' serving as the key orchestration concept to coordinate data and instructions among agents and other components. Task and data planners, respectively, break down, map, and optimize tasks and data to available agents and data sources defined in respective registries, given production constraints such as accuracy and latency.

This study presents a novel approach for touch sensing using semi-elastic textile surfaces that does not require the placement of additional sensors in the sensing area, instead relying on sensors located on the border of the textile. The proposed approach is demonstrated through experiments involving an elastic Jersey fabric and a variety of machine-learning models. The performance of one particular border-based sensor design is evaluated in depth. By using visual markers, the best-performing visual sensor arrangement predicts a single touch point with a mean squared error of 1.36 mm on an area of 125mm by 125mm. We built a textile only prototype that is able to classify touch at three indent levels (0, 15, and 20 mm) with an accuracy of 82.85%. Our results suggest that this approach has potential applications in wearable technology and smart textiles, making it a promising avenue for further exploration in these fields.

Tactile sensing in soft robots remains particularly challenging because of the coupling between contact and deformation information which the sensor is subject to during actuation and interaction with the environment. This often results in severe interference and makes disentangling tactile sensing and geometric deformation difficult. To address this problem, this paper proposes a soft capacitive e-skin with a sparse electrode distribution and deep learning for information decoupling. Our approach successfully separates tactile sensing from geometric deformation, enabling touch recognition on a soft pneumatic actuator subject to both internal (actuation) and external (manual handling) forces. Using a multi-layer perceptron, the proposed e-skin achieves 99.88\% accuracy in touch recognition across a range of deformations. When complemented with prior knowledge, a transformer-based architecture effectively tracks the deformation of the soft actuator. The average distance error in positional reconstruction of the manipulator is as low as 2.905$\pm$2.207 mm, even under operative conditions with different inflation states and physical contacts which lead to additional signal variations and consequently interfere with deformation tracking. These findings represent a tangible way forward in the development of e-skins that can endow soft robots with proprioception and exteroception.

Talkdesk, Inc., a global cloud contact center leader for customer-obsessed companies, and NetNordic Denmark, a system integrator in cloud and the only authorized Talkdesk partner in the Nordics, are collaborating to deliver a contact center solution for Nilfisk. The Danish pioneer in cleaning technology and premium cleaning products selected the Talkdesk solution – with implementation by NetNordic – because its unique artificial intelligence (AI)-powered workforce engagement features will allow Nilfisk to transform both the agent and customer experience (CX). "We aim to provide seamless experiences across every touch point for our customers" Danish engineer P.A. Fisker founded Nilfisk in 1906, inspired by a love of knowledge and his ambition to build a company driven by technology. In 1910, the company developed and patented the first electric vacuum cleaner in Europe, setting the foundations for a 100 year commitment to delivering innovative cleaning solutions. Today, Nilfisk continues to advance floorcare and cleaning technology across the globe.

Talkdesk, Inc., a global cloud contact center leader for customer-obsessed companies, and NetNordic Denmark, a system integrator in cloud and the only authorized Talkdesk partner in the Nordics, are collaborating to deliver a contact center solution for Nilfisk. The Danish pioneer in cleaning technology and premium cleaning products selected the Talkdesk solution – with implementation by NetNordic – because its unique artificial intelligence (AI)-powered workforce engagement features will allow Nilfisk to transform both the agent and customer experience (CX). AI and ML News: Why SMBs Shouldn't Be Afraid of Artificial Intelligence (AI) "With the Talkdesk solution, Nilfisk can make every customer conversation matter. We're proud to partner with NetNordic and Nilfisk to create customer experiences that delight." Danish engineer P.A. Fisker founded Nilfisk in 1906, inspired by a love of knowledge and his ambition to build a company driven by technology.

Healthcare is one of the most complex products our economy produces. Over the next 50 years, global health megatrends will change dramatically & we are headed to face increased risks of exposure to new, emerging and re-emerging diseases, new pandemics with surging globalisation, all putting a huge pressure on the healthcare system. Massive variations in health status, lack of access to quality health care, poor health outcomes and increasing cost of care are huge concerns globally. The Freaking future of healthcare pushes us to achieve a more intuitive, responsive, empathetic, cost effective and safer health systems. Only possible when the entire ecosystem & the stakeholders raise the collective expectations of how the system performs today.

Companies should embrace technological innovations, but always with a human touch. For the 2022 Global Marketing Trends report researchers interviewed over a thousand chief executives, marketers and others on a variety of marketing topics and polled 11.500 consumers across 19 countries. The researchers distinguish seven trends among high-growth companies that will continue to play a major role in 2022. The conclusions of the report are optimistic: if last year was responding to an unprecedented shock, 2022 is about recovering or even thriving in a customer centric world. Purpose driving new growth Perhaps the most important conclusion in the report is about purpose as a driving force for new growth.