We introduce a new approach for robotic manipulation tasks in human settings that necessitates understanding the 3D geometric connections between a pair of objects. Conventional end-to-end training approaches, which convert pixel observations directly into robot actions, often fail to effectively understand complex pose relationships and do not easily adapt to new object configurations. To overcome these issues, our method focuses on learning the 3D geometric relationships, particularly how critical parts of one object relate to those of another. We employ Weighted SVD in our standalone model to analyze pose relationships both in articulated parts and in free-floating objects.

Recent consumer demand for home robots has accelerated performance of robotic grasping. However, a key component of the perception pipeline, the depth camera, is still expensive and inaccessible to most consumers. In addition, grasp planning has significantly improved recently, by leveraging large datasets and cloud robotics, and by limiting the state and action space to top-down grasps with 4 degrees of freedom (DoF). By leveraging multi-view geometry of the object using inexpensive equipment such as off-the-shelf RGB cameras and state-of-the-art algorithms such as Learn Stereo Machine (LSM\cite{kar2017learning}), the robot is able to generate more robust grasps from different angles with 6-DoF. In this paper, we present a modification of LSM to graspable objects, evaluate the grasps, and develop a 6-DoF grasp planner based on Grasp-Quality CNN (GQ-CNN\cite{mahler2017dex}) that exploits multiple camera views to plan a robust grasp, even in the absence of a possible top-down grasp.

Robotic manipulation of deformable, one-dimensional objects (DOOs) like ropes or cables has important potential applications in manufacturing, agriculture, and surgery. In such environments, the task may involve threading through or avoiding becoming tangled with objects like racks or frames. Grasping with multiple grippers can create closed loops between the robot and DOO, and If an obstacle lies within this loop, it may be impossible to reach the goal. However, prior work has only considered the topology of the DOO in isolation, ignoring the arms that are manipulating it. Searching over possible grasps to accomplish the task without considering such topological information is very inefficient, as many grasps will not lead to progress on the task due to topological constraints. Therefore, we propose a grasp loop signature which categorizes the topology of these grasp loops and show how it can be used to guide planning. We perform experiments in simulation on two DOO manipulation tasks to show that using the signature is faster and succeeds more often than methods that rely on local geometry or finite-horizon planning. Finally, we demonstrate using the signature in the real world to manipulate a cable in a scene with obstacles using a dual-arm robot.

A robot that can pick up several bits of clothing at once from a pile strewn across the floor could be used to help tidy messy bedrooms. Picking up piles of clothes and grasping multiple items simultaneously may be straightforward for a human, but actions such as working out where the clothes' edges are and how to group them together pose problems for a robot.

Did you have the chance to attend the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022) in Kyoto? Here we bring you the papers that received an award this year in case you missed them.

The current wave of artificial intelligence can be traced back to 2012, and an academic contest that measured how well algorithms could recognize objects in photographs. That year, researchers found that feeding thousands of images into an algorithm inspired loosely by the way neurons in a brain respond to input produced a huge leap in accuracy. The breakthrough sparked an explosion in academic research and commercial activity that is transforming some companies and industries. Now a new trick, which involves training the same kind of AI algorithm to turn 2D images into a rich 3D view of a scene, is sparking excitement in the worlds of both computer graphics and AI. The technique has the potential to shake up video games, virtual reality, robotics, and autonomous driving.

The current wave of artificial intelligence can be traced back to 2012, and an academic contest that measured how well algorithms could recognize objects in photographs. That year, researchers found that feeding thousands of images into an algorithm inspired loosely by the way neurons in a brain respond to input produced a huge leap in accuracy. The breakthrough sparked an explosion in academic research and commercial activity that is transforming some companies and industries. Now a new trick, which involves training the same kind of AI algorithm to turn 2D images into a rich 3D view of a scene, is sparking excitement in the worlds of both computer graphics and AI. The technique has the potential to shake up video games, virtual reality, robotics, and autonomous driving.

Wired Magazine recently called for us to, post pandemic, "ditch our tech enabled tools of social distancing". But are our telepresence robots creating emotional distancing or are they actually improving our emotional lives. This week in our weekly "COVID-19, robots and us" discussion with experts, we're looking at the topic of virtual presence and emotional contact as well as many other practical ways that robotics can make a difference in pandemic times. Robin Murphy, Raytheon Professor at Texas A&M University and founder of the field of Rescue Robotics, was involved in the very first use of robots in a disaster scenario in 9/11. Since then she's been involved in multiple disaster responses worldwide, including the Ebola outbreak in 2014-2016.

The spread of the COVID-19 coronavirus is a fluid situation changing by the day, and even by the hour. The growing worldwide public health emergency is threatening lives, but it's also impacting businesses and disrupting travel around the world. The OECD warns that coronavirus could cut global economic growth in half, and the Federal Reserve will cut the federal interest rates following the worst week for the stock market since 2008. Just how the COVID-19 coronavirus will affect the way we live and work is unclear because it's a novel disease spreading around the world for the first time, but it appears that AI may help fight the virus and its economic impact. A World Health Organization report released last month said that AI and big data are a key part of the response to the disease in China.

The spread of the COVID-19 coronavirus is a fluid situation changing by the day, and even by the hour. The growing worldwide public health emergency is threatening lives, but it's also impacting businesses and disrupting travel around the world. The OECD warns that coronavirus could cut global economic growth in half, and the Federal Reserve will cut the federal interest rates following the worst week for the stock market since 2008. Just how the COVID-19 coronavirus will affect the way we live and work is unclear because it's a novel disease spreading around the world for the first time, but it appears that AI may help fight the virus and its economic impact. A World Health Organization report released last month said that AI and big data are a key part of the response to the disease in China.