{"id":16890,"date":"2019-10-23T08:52:17","date_gmt":"2019-10-23T08:52:17","guid":{"rendered":"https:\/\/www.revoscience.com\/en\/?p=16890"},"modified":"2020-06-09T12:40:47","modified_gmt":"2020-06-09T12:40:47","slug":"giving-robots-a-faster-grasp","status":"publish","type":"post","link":"https:\/\/www.revoscience.com\/en\/giving-robots-a-faster-grasp\/","title":{"rendered":"Giving robots a faster grasp"},"content":{"rendered":"\n

An algorithm speeds up the planning process robots use to adjust their grip on objects, for picking and sorting, or tool use.<\/strong><\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u00a0If you\u2019re at a desk with a pen or pencil handy, try this move: Grab the pen by one end with your thumb and index finger, and push the other end against the desk. Slide your fingers down the pen, then flip it upside down, without letting it drop. Not too hard, right?<\/p>\n\n\n\n

But for a robot \u2014 say, one that\u2019s sorting through a bin of objects and attempting to get a good grasp on one of them \u2014 this is a computationally taxing maneuver. Before even attempting the move it must calculate a litany of properties and probabilities, such as the friction and geometry of the table, the pen, and its two fingers, and how various combinations of these properties interact mechanically, based on fundamental laws of physics.<\/p>\n\n\n\n

Now MIT engineers have found a way to significantly speed up the planning process required for a robot to adjust its grasp on an object by pushing that object against a stationary surface. Whereas traditional algorithms would require tens of minutes for planning out a sequence of motions, the new team\u2019s approach shaves this preplanning process down to less than a second.<\/p>\n\n\n\n

Alberto Rodriguez, associate professor of mechanical engineering at MIT, says the speedier planning process will enable robots, particularly in industrial settings, to quickly figure out how to push against, slide along, or otherwise use features in their environments to reposition objects in their grasp. Such nimble manipulation is useful for any tasks that involve picking and sorting, and even intricate tool use.<\/p>\n\n\n\n

\u201cThis is a way to extend the dexterity of even simple robotic grippers, because at the end of the day, the environment is something every robot has around it,\u201d Rodriguez says.<\/p>\n\n\n\n

The team\u2019s results are published today in\u00a0The International Journal of Robotics Research<\/a><\/em>. Rodriguez\u2019 co-authors are lead author Nikhil Chavan-Dafle, a graduate student in mechanical engineering, and Rachel Holladay, a graduate student in electrical engineering and computer science.<\/p>\n\n\n\n

Physics in a cone<\/strong><\/p>\n\n\n\n

Rodriguez\u2019 group works on enabling robots to leverage their environment to help them accomplish physical tasks, such as picking and sorting objects in a bin. \u00a0<\/p>\n\n\n\n

Existing algorithms typically take hours to preplan a sequence of motions for a robotic gripper, mainly because, for every motion that it considers, the algorithm must first calculate whether that motion would satisfy a number of physical laws, such as Newton\u2019s laws of motion and Coulomb\u2019s law describing frictional forces between objects.<\/p>\n\n\n\n

\u201cIt\u2019s a tedious computational process to integrate all those laws, to consider all possible motions the robot can do, and to choose a useful one among those,\u201d Rodriguez says.<\/p>\n\n\n\n

He and his colleagues found a compact way to solve the physics of these manipulations, in advance of deciding how the robot\u2019s hand should move. They did so by using \u201cmotion cones,\u201d which are essentially visual, cone-shaped maps of friction.<\/p>\n\n\n\n

The inside of the cone depicts all the pushing motions that could be applied to an object in a specific location, while satisfying the fundamental laws of physics and enabling the robot to keep hold of the object. The space outside of the cone represents all the pushes that would in some way cause an object to slip out of the robot\u2019s grasp.<\/p>\n\n\n\n

\u201cSeemingly simple variations, such as how hard robot grasps the object, can significantly change how the object moves in the grasp when pushed,\u201d Holladay explains. \u201cBased on how hard you\u2019re grasping, there will be a different motion. And that\u2019s part of the physical reasoning that the algorithm handles.\u201d<\/p>\n\n\n\n

The team\u2019s algorithm calculates a motion cone for different possible configurations between a robotic gripper, an object that it is holding, and the environment against which it is pushing, in order to select and sequence different feasible pushes to reposition the object.<\/p>\n\n\n\n

\u201cIt\u2019s a complicated process but still much faster than the traditional method \u2014 fast enough that planning an entire series of pushes takes half a second,\u201d Holladay says.<\/p>\n\n\n\n

Big plans<\/strong><\/p>\n\n\n\n

The researchers tested the new algorithm on a physical setup with a three-way interaction, in which a simple robotic gripper was holding a T-shaped block and pushing against a vertical bar. They used multiple starting configurations, with the robot gripping the block at a particular position and pushing it against the bar from a certain angle. For each starting configuration, the algorithm instantly generated the map of all the possible forces that the robot could apply and the position of the block that would result.<\/p>\n\n\n\n

\u201cWe did several thousand pushes to verify our model correctly predicts what happens in the real world,\u201d Holladay says. \u201cIf we apply a push that\u2019s inside the cone, the grasped object should remain under control. If it\u2019s outside, the object should slip from the grasp.\u201d<\/p>\n\n\n\n

The researchers found that the algorithm\u2019s predictions reliably matched the physical outcome in the lab, planning out sequences of motions \u2014 such as reorienting the block against the bar before setting it down on a table in an upright position \u2014 in less than a second, compared with traditional algorithms that take over 500 seconds to plan out.<\/p>\n\n\n\n

\u201cBecause we have this compact representation of the mechanics of this three-way-interaction between robot, object, and their environment, we can now attack bigger planning problems,\u201d Rodriguez says.<\/p>\n\n\n\n

The group is hoping to apply and extend its approach to enable a robotic gripper to handle different types of tools, for instance in a manufacturing setting.<\/p>\n\n\n\n

\u201cMost factory robots that use tools have a specially designed hand, so instead of having the ability to grasp a screwdriver and use it in a lot of different ways, they just make the hand a screwdriver,\u201d Holladay says. \u201cYou can imagine that requires less dexterous planning, but it\u2019s much more limiting. We\u2019d like a robot to be able to use and pick lots of different things up.\u201d<\/p>\n\n\n\n

This research was supported, in part, by Mathworks, the MIT-HKUST Alliance, and the National Science Foundation.<\/p>\n
","protected":false},"excerpt":{"rendered":"

If you\u2019re at a desk with a pen or pencil handy, try this move: Grab the pen by one end with your thumb and index finger, and push the other end against the desk<\/p>\n","protected":false},"author":2,"featured_media":16891,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[],"class_list":["post-16890","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news"],"featured_image_urls":{"full":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2019\/10\/MIT-Robot-Gripper_1.jpg",640,426,false],"thumbnail":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2019\/10\/MIT-Robot-Gripper_1-200x200.jpg",200,200,true],"medium":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2019\/10\/MIT-Robot-Gripper_1-300x200.jpg",300,200,true],"medium_large":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2019\/10\/MIT-Robot-Gripper_1.jpg",640,426,false],"large":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2019\/10\/MIT-Robot-Gripper_1.jpg",640,426,false],"1536x1536":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2019\/10\/MIT-Robot-Gripper_1.jpg",640,426,false],"2048x2048":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2019\/10\/MIT-Robot-Gripper_1.jpg",640,426,false],"ultp_layout_landscape_large":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2019\/10\/MIT-Robot-Gripper_1.jpg",640,426,false],"ultp_layout_landscape":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2019\/10\/MIT-Robot-Gripper_1.jpg",640,426,false],"ultp_layout_portrait":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2019\/10\/MIT-Robot-Gripper_1.jpg",600,399,false],"ultp_layout_square":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2019\/10\/MIT-Robot-Gripper_1.jpg",600,399,false],"newspaper-x-single-post":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2019\/10\/MIT-Robot-Gripper_1.jpg",640,426,false],"newspaper-x-recent-post-big":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2019\/10\/MIT-Robot-Gripper_1-550x360.jpg",550,360,true],"newspaper-x-recent-post-list-image":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2019\/10\/MIT-Robot-Gripper_1-95x65.jpg",95,65,true],"web-stories-poster-portrait":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2019\/10\/MIT-Robot-Gripper_1.jpg",640,426,false],"web-stories-publisher-logo":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2019\/10\/MIT-Robot-Gripper_1.jpg",96,64,false],"web-stories-thumbnail":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2019\/10\/MIT-Robot-Gripper_1.jpg",150,100,false]},"author_info":{"info":["RevoScience"]},"category_info":"News<\/a>","tag_info":"News","comment_count":"0","_links":{"self":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts\/16890","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/comments?post=16890"}],"version-history":[{"count":0,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts\/16890\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/media\/16891"}],"wp:attachment":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/media?parent=16890"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/categories?post=16890"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/tags?post=16890"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}