Esophageal cancer remains a highly aggressive malignancy with low survival rates, requiring advanced surgical interventions like esophagectomy. Traditional manual techniques, including circular staplers, face challenges such as limited precision, prolonged recovery times, and complications like leaks and tissue misalignment. This paper presents a novel robotic circular stapler designed to enhance the dexterity in confined spaces, improve tissue alignment, and reduce post-operative risks. Integrated with a cognitive robot that serves as a surgeon's assistant, the surgical stapler uses three actuators to perform anvil motion, cutter/stapler motion and allows a 75-degree bending of the cartridge (distal tip). Kinematic analysis is used to compute the stapler tip's position, ensuring synchronization with a robotic system.

The problem of cleaning a messy table using Deep Neural Networks is a very interesting problem in both social and industrial robotics. This project focuses on the social application of this technology. A neural network model that is capable of detecting and recognizing common tabletop objects, such as a mug, mouse, or stapler is developed. The model also predicts the angle at which these objects are placed on a table,with respect to some reference. Assuming each object has a fixed intended position and orientation on the tabletop, the orientation of a particular object predicted by the deep learning model can be used to compute the transformation matrix to move the object from its initial position to the intended position. This can be fed to a pick and place robot to carry out the transfer.This paper talks about the deep learning approaches used in this project for object detection and orientation estimation.

Assistive robots operating in household environments would require items to be available in the house to perform assistive tasks. However, when these items run out, the assistive robot must remind its user to buy the missing items. In this paper, we present a computational architecture that can allow a robot to learn personalized contextual knowledge of a household through interactions with its user. The architecture can then use the learned knowledge to make predictions about missing items from the household over a long period of time. The architecture integrates state-of-the-art perceptual learning algorithms, cognitive models of memory encoding and learning, a reasoning module for predicting missing items from the household, and a graphical user interface (GUI) to interact with the user. The architecture is integrated with the Fetch mobile manipulator robot and validated in a large indoor environment with multiple contexts and objects. Our experimental results show that the robot can adapt to an environment by learning contextual knowledge through interactions with its user. The robot can also use the learned knowledge to correctly predict missing items over multiple weeks and it is robust against sensory and perceptual errors.

Medical devices used outside of acute care settings, such as point-of-care ultrasound, and those whose rapid development has outpaced safety assessments, namely robots, are among the top health technology hazards that nonprofit ECRI Institute has identified in a new report. Leading the organization's list, however, are accidents associated with a decades-old technology, surgical staplers. Problems associated with staplers have led FDA to propose reclassifying the devices from Class I to Class II, a higher-risk category that would allow the agency to establish special controls and labeling requirements for the devices. An advisory panel for the agency endorsed the proposal in May, and Medtronic and the Society of American Gastrointestinal and Endoscopic Surgeons both have voiced support for formal reclassification. ECRI annually compiles a list of its 10 biggest safety concerns from incident investigations and device testing as well as public and private event reporting databases.

Computers have transformed work and play, transportation and medicine, entertainment and sports. Yet for all their power, these machines still cannot perform simple tasks that a child can do, such as navigating an unknown room or using a pencil. The solution is finally coming within reach. It will emerge from the intersection of two major pursuits: the reverse engineering of the brain and the burgeoning field of artificial intelligence. Over the next 20 years, these two pursuits will combine to usher in a new epoch of intelligent machines. Why do we need to know how the brain works to build intelligent machines? Although machine-learning techniques such as deep neural networks have recently made impressive gains, they are still a world away from being intelligent, from being able to understand and act in the world the way that we do. The only example of intelligence, of the ability to learn from the world, to plan and to execute, is the brain.

A 6-foot-tall, one-armed robot named Stair 1.0 balances on a modified Segway platform in the doorway of a Stanford University conference room. It has an arm, cameras and laser scanners for eyes, and a tangle of electrical intestines stuffed into its base. From his seat at a polished table, roboticist Morgan Quigley sends the bot on a mission. "Stair, please fetch the stapler from the lab." After the third attempt, Stair responds in an inflectionless voice: "I will go fetch the stapler for you."

Children quickly learn to predict what will happen if they turn a cup filled with juice upside down. Robots, on the other hand, don't have a clue. Researchers at the Allen Institute for Artificial Intelligence (Ai2) in Seattle have developed a computer program that shows how machines determine how the objects captured by a camera will most likely behave. This could help make robots and other machines less prone to error, and might help self-driving cars navigate unfamiliar scenes more safely. The system, developed by Roozbeh Mottaghi and colleagues, draws conclusions about the physical properties of a scene using a combination of machine learning and 3-D modeling.

The concept of common ground — the mutual understanding of context and conventions — is central to philosophical accounts of mendacity; its use is to determine the meaning of linguistic expressions and the significance of physical acts, and to distinguish certain statements as conveying a conventional promise, warranty, or expectation of sincerity. Lying necessarily involves an abuse of common ground, namely the willful violation of conventions regulating sincerity. The ‘lying machine’ is an AI system that purposely abuses common ground as an effective means for practicing mendacity and lesser deceptions. The machine's method is to conceive and articulate sophisms — perversions of normative reason and communication — crafted to subvert its audience's beliefs. Elements of this paper (i) explain the described use of common ground in philosophical accounts of mendacity, (ii) motivate arguments and illusions as stratagem for deception, (iii) encapsulate the lying machine's design and operation, and (iv) summarize human-subject experiments that confirm the lying machine's arguments are, in fact, deceptive.