Self-supervised learning is a powerful approach for developing traversability models for off-road navigation, but these models often struggle with inputs unseen during training. Existing methods utilize techniques like evidential deep learning to quantify model uncertainty, helping to identify and avoid out-of-distribution terrain. However, always avoiding out-of-distribution terrain can be overly conservative, e.g., when novel terrain can be effectively analyzed using a physics-based model. To overcome this challenge, we introduce Physics-Informed Evidential Traversability (PIETRA), a self-supervised learning framework that integrates physics priors directly into the mathematical formulation of evidential neural networks and introduces physics knowledge implicitly through an uncertainty-aware, physics-informed training loss. Our evidential network seamlessly transitions between learned and physics-based predictions for out-of-distribution inputs. Additionally, the physics-informed loss regularizes the learned model, ensuring better alignment with the physics model. Extensive simulations and hardware experiments demonstrate that PIETRA improves both learning accuracy and navigation performance in environments with significant distribution shifts.

A common limitation of autonomous tissue manipulation in robotic minimally invasive surgery (MIS) is the absence of force sensing and control at the tool level. Recently, our team has developed haptics-enabled forceps that can simultaneously measure the grasping and pulling forces during tissue manipulation. Based on this design, here we further present a method to automate tissue traction with controlled grasping and pulling forces. Specifically, the grasping stage relies on a controlled grasping force, while the pulling stage is under the guidance of a controlled pulling force. Notably, during the pulling process, the simultaneous control of both grasping and pulling forces is also enabled for more precise tissue traction, achieved through force decoupling. The force controller is built upon a static model of tissue manipulation, considering the interaction between the haptics-enabled forceps and soft tissue. The efficacy of this force control approach is validated through a series of experiments comparing targeted, estimated, and actual reference forces. To verify the feasibility of the proposed method in surgical applications, various tissue resections are conducted on ex vivo tissues employing a dual-arm robotic setup. Finally, we discuss the benefits of multi-force control in tissue traction, evidenced through comparative analyses of various ex vivo tissue resections. The results affirm the feasibility of implementing automatic tissue traction using micro-sized forceps with multi-force control, suggesting its potential to promote autonomous MIS. A video demonstrating the experiments can be found at https://youtu.be/8fe8o8IFrjE.

A key challenge in off-road navigation is that even visually similar terrains or ones from the same semantic class may have substantially different traction properties. Existing work typically assumes no wheel slip or uses the expected traction for motion planning, where the predicted trajectories provide a poor indication of the actual performance if the terrain traction has high uncertainty. In contrast, this work proposes to analyze terrain traversability with the empirical distribution of traction parameters in unicycle dynamics, which can be learned by a neural network in a self-supervised fashion. The probabilistic traction model leads to two risk-aware cost formulations that account for the worst-case expected cost and traction. To help the learned model generalize to unseen environment, terrains with features that lead to unreliable predictions are detected via a density estimator fit to the trained network's latent space and avoided via auxiliary penalties during planning. Simulation results demonstrate that the proposed approach outperforms existing work that assumes no slip or uses the expected traction in both navigation success rate and completion time. Furthermore, avoiding terrains with low density-based confidence score achieves up to 30% improvement in success rate when the learned traction model is used in a novel environment.

New York City grocers are expressing outrage over a push by city council members to ban facial recognition technology stores rely on to deter shoplifting due to concerns of racial discrimination. Ferreira Foodtown CEO Jason Ferreira joined "Fox & Friends" Tuesday to call out the suggestion as thefts continue to rock businesses in the Big Apple. Ferreira, who has been in business for over 45 years, said the shoplifting has never been worse. "It's not only people that are doing it professionally. We have people that are doing it just because they can get away with it. And the gamut runs from children to people that are older."

Large Language Models (LLMs) have come a long way in recent years. From fluent dialogue generation to text summarisation, and article generation, language models have made it extremely easy for anyone to build an NLP-powered product. As a result, hundreds of apps have been popping up every day, predominantly relying on APIs such as OpenAI, Cohere, or Stable Diffusion. Looking at these developments, one might wonder: what is the disruptive potential of such apps? Are they poised to deliver transformative results to all industries?

The road to driverless cars has been a long and winding one. Look no further than the Oct. 26 news that Argo AI, the autonomous vehicle company backed by Ford Motors and Volkswagen, would be shutting down. Only a few weeks earlier, Argo AI had launched a partnership with Lyft to offer supervised autonomous rides around Austin. It had previously announced a partnership with Walmart to carry out deliveries. The company, which began operations in Austin in 2019, had about 20 vehicles as of October that could be seen around town.

Streamlined Ventures, led by Ullas Naik, secured $140 million in new capital commitments for its two newest funds. This brings the total funds managed to eight with the assets under management reaching about $325 million. Institutional investors, family offices and high net worth individuals pumped $102 million into the firm's fifth seed fund, which targets startups focused on data science, AI, software automation, APIs and Web 2.5. The second is $36 million into a third opportunity fund that invests in mid-stage financings of seed-stage companies from prior seed funds. Naik is a solo general partner who started Streamlined Ventures in 2011, but prior to starting his own firm, had been in both angel investing and venture capital for more than 25 years.

At first glance, generative AI might seem like more of a curiosity than an enterprise-technology tool, said Peter van der Putten, director of the AI Lab at software firm Pegasystems Inc. "Creating cute pictures of a corgi in a house made of sushi isn't exactly a profitable business case, at least not for large enterprises," Mr. van der Putten said. And yet, he said, "generative AI startups are popping up left and right, in areas such as marketing, support, service and other content creation." The Morning Download delivers daily insights and news on business technology from the CIO Journal team. Jasper, an Austin, Texas-based startup launched last year, has developed a generative AI platform designed to auto-generate promotional blog posts and other marketing materials. Amid a sharp decline in venture-capital investing deals, Jasper last week announced a $125 million Series A fundraising round, which set its private-market valuation above $1 billion, the company said.

Google said it will terminate services for Stadia, its troubled cloud gaming service, after it failed to gain traction with players almost three years after its launch. Stadia was an attempt from Alphabet Inc.'s Google to take on the video game console giants with a platform of its own. Unlike traditional consoles, Stadia allowed users to play games on devices such as Android phones and Chromecast apps for TV, by funneling data directly from Google's server clusters. "While Stadia's approach to streaming games for consumers was built on a strong technology foundation, it hasn't gained the traction with users that we expected," Phil Harrison, Stadia vice president and general manager, wrote in a blog post on Thursday. "So we've made the difficult decision to begin winding down our Stadia streaming service."

COVID-19 brought unprecedented disruptions in the operational models of businesses. In fact, the need for integrating Artificial Intelligence (AI) into daily operations became more and more evident during the pandemic. This is corroborated by the fact that over 75% of global enterprises have augmented their AI investments by 18% since 2020. This trend continues unabated as business leaders now realize AI is the green card to market leadership. Today more than ever, companies across industries are leveraging this advanced technology to execute tasks at accelerated speeds and capture lasting value at scale, while also providing a superior customer experience.