BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Robotics Institute Carnegie Mellon University - ECPv6.15.12.1//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Robotics Institute Carnegie Mellon University
X-ORIGINAL-URL:https://www.ri.cmu.edu
X-WR-CALDESC:Events for Robotics Institute Carnegie Mellon University
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20170312T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20171105T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20180311T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20181104T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20190310T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20191103T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20200308T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20201101T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20210314T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20211107T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20220313T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20221106T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20230312T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20231105T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20240310T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20241103T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20250309T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20251102T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240621T120000
DTEND;TZID=America/New_York:20240621T130000
DTSTAMP:20260917T210452
CREATED:20240617T122809Z
LAST-MODIFIED:20240617T122918Z
UID:141249-1718971200-1718974800@www.ri.cmu.edu
SUMMARY:Composable Optimization for Robotic Motion Planning and Control
DESCRIPTION:Abstract:\n \n\n\n\nContact interactions are pervasive in real-world robotics tasks like manipulation and walking. However\, the non-smooth dynamics associated with impacts and friction remain challenging to model\, and motion planning and control algorithms that can fluently and efficiently reason about contact remain elusive. In this talk\, I will share recent work from my research group that takes an “optimization-first” approach to these challenges: collision detection\, physics\, motion planning\, state estimation\, and control are all posed as constrained optimization problems.  We then build a set of algorithmic and numerical tools that allow us to flexibly compose these optimization sub-problems to solve complex robotics tasks involving discontinuous\, unplanned\, and uncertain contact mechanics. \nBio:\nZac Manchester is an Assistant Professor of Robotics at Carnegie Mellon University. He holds a Ph.D. in aerospace engineering and a B.S. in applied physics from Cornell University. Zac was a postdoc in the Agile Robotics Lab at Harvard University and previously worked at Stanford\, NASA Ames Research Center and Analytical Graphics\, Inc. He received a NASA Early Career Faculty Award in 2018\, a Google Faculty Research Award in 2019\, and has led four satellite missions. His research interests include motion planning\, control\, and numerical optimization\, particularly with application to robotic locomotion and spacecraft guidance\, navigation\, and control.
URL:https://www.ri.cmu.edu/event/ri-meeting/
LOCATION:Newell-Simon Hall 4305
CATEGORIES:Faculty Events,Special Talk
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230712T143000
DTEND;TZID=America/New_York:20230712T153000
DTSTAMP:20260917T210452
CREATED:20230627T171151Z
LAST-MODIFIED:20230627T171151Z
UID:136758-1689172200-1689175800@www.ri.cmu.edu
SUMMARY:Special RI Seminar
DESCRIPTION:Title: Testing\, Analysis\, and Specification for Robust and Reliable Robot Software \nAbstract: Building robust and reliable robotic software is an inherently challenging feat that requires substantial expertise across a variety of disciplines. Despite that\, writing robot software has never been easier thanks to software frameworks such as ROS: At its best\, ROS allows newcomers to assemble simple\, autonomous robots within a matter of hours or days\, and domain experts working in a particular area of robotics (e.g.\, perception) to share their latest algorithms with the community as a reusable software component. In practice\, however\, truly reusable robotics software components are difficult to build and even harder to integrate. Due to a lack of tools and best practices and given inherent challenges\, developers often rely on field testing to evaluate their systems and identify potentially dangerous behavior. This approach to testing and evaluation quickly becomes an expensive\, time consuming\, and dangerous bottleneck that severely constrains the rate at which changes can be sufficiently tested and safely deployed. As such\, it is essential that we develop drastically faster\, cheaper\, and more scalable approaches for assuring the quality of robot software that do not require developers to possess highly specialized knowledge or dramatically change their existing workflows. In this talk\, I will discuss the state of testing and evaluation within robotics\, highlight key research and engineering challenges\, and show how testing\, analysis\, and specification can identify potentially catastrophic bugs faster\, cheaper\, and earlier. \nBio: Chris Timperley is a Senior Systems Scientist based in the Software and Societal Systems Department at Carnegie Mellon University. His research interests lie at the intersection of software engineering\, robotics\, and program analysis\, with a focus on developing and evaluating new languages\, tools\, and techniques for building\, testing\, and debugging robotics software. He teaches classes on software engineering\, design\, testing and analysis at both the undergraduate and graduate level. His work has featured at industrial and academic conferences\, in the popular press\, and within the open-source robotics community. He holds PhD and MEng degrees from the University of York.
URL:https://www.ri.cmu.edu/event/special-ri-seminar/
LOCATION:NSH 4305
CATEGORIES:RI Seminar,Seminar,Special Talk
ATTACH;FMTTYPE=image/jpeg:https://www.ri.cmu.edu/app/uploads/2023/06/timperly-chris.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230217T120000
DTEND;TZID=America/New_York:20230217T130000
DTSTAMP:20260917T210452
CREATED:20230213T193011Z
LAST-MODIFIED:20230213T193011Z
UID:135053-1676635200-1676638800@www.ri.cmu.edu
SUMMARY:Perceiving Objects and Interactions in 3D
DESCRIPTION:Abstract: We observe and interact with myriad of objects in our everyday lives\, from cups and bottles to hammers and tennis rackets. In this talk\, I will outline our group’s efforts towards understanding these objects and our everyday interactions with them in 3D. I will first focus on scaling 3D prediction for isolated objects across generic categories\, and describe approaches that allow coarse prediction from single view and detailed inference given multi-view. Moving beyond isolated objects\, I will present approaches aimed to understanding hand-object interactions in 3D — both in terms of reconstructing and imagining them from images. Finally\, I will highlight applications of this 3D inference in robot manipulation task\, and outline some interesting future directions in this space.\n\nBio: Shubham Tulsiani is an Assistant Professor in the CMU School of Computer Science. Prior to this\, he was research scientist at Facebook AI Research (FAIR). He received a PhD. in Computer Science from UC Berkeley in 2018. He is interested in building perception systems that can infer the spatial and physical structure of the world they observe.
URL:https://www.ri.cmu.edu/event/perceiving-objects-and-interactions-in-3d/
LOCATION:Newell-Simon Hall 4305
CATEGORIES:Faculty Events,Special Talk
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230207T163000
DTEND;TZID=America/New_York:20230207T180000
DTSTAMP:20260917T210452
CREATED:20230206T194617Z
LAST-MODIFIED:20230206T194617Z
UID:135001-1675787400-1675792800@www.ri.cmu.edu
SUMMARY:Hamlyn Centre for Robotic Surgery and the Mechatronics in Medicine Laboratory
DESCRIPTION:An Overview of the Hamlyn Centre and the Mechatronics in Medicine Laboratory\, Imperial College\n  \n\n\n\nStarting with a brief overview of robotics at Imperial College\, the lecture will introduce the Hamlyn Centre for Robotic Surgery\, which is an interdisciplinary research hub run jointly between the Engineering and Medical faculties\, with an overview of academic themes\, running programs and key infrastructure. It will then survey some of the latest work by Prof. Rodriguez y Baena’s own group\, the Mechatronics in Medicine Laboratory\, before concluding with his thoughts about the present and future of the centre and this technology\, including an overview of new academic positions open in the centre. \n\nBio: Ferdinando Rodriguez y Baena is Professor of Medical Robotics in the Department of Mechanical Engineering at Imperial College London\, where he leads the Mechatronics in Medicine Laboratory and the Applied Mechanics Division. He has been the Engineering Co-Director of the Hamlyn Centre for Robotic Surgery\, which is part of the Institute of Global Health Innovation\, since July 2020. He is a founding member and great advocate of the Imperial College Robotics Forum\, now the first point of contact for roboticists at Imperial College.\nAdditional Information on Positions at the Hamlyn Centre. \nFaculty Host:  Cameron Riviere – criviere@andrew.cmu.edu\n\n\n\n\n\nSCS/ECE Careers\n\n\n\n\nKeywords: \n\nHuman-Computer Interaction Institute (HCII)\, Robotics Institute (RI)\, Biomedical Engineering\, Electrical and Computer Engineering\, Mechanical Engineering
URL:https://www.ri.cmu.edu/event/hamlyn-centre-for-robotic-surgery-and-the-mechatronics-in-medicine-laboratory/
LOCATION:GHC 4405
CATEGORIES:Faculty Events,Special Talk
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221216T120000
DTEND;TZID=America/New_York:20221216T130000
DTSTAMP:20260917T210452
CREATED:20221213T154317Z
LAST-MODIFIED:20221213T154317Z
UID:134510-1671192000-1671195600@www.ri.cmu.edu
SUMMARY:Making AI trustworthy and understandable by clinicians
DESCRIPTION:Abstract:  Understandable-AI techniques facilitate to use of AI as a tool by human experts\, giving humans insight into how AI decisions are made thereby helping experts discern which AI predictions should or shouldn’t be trusted.  Understandable techniques may be especially useful for applications with insufficient validation data for regulatory approval\, for which human experts must remain the final decision makers.  One understandable-AI approach is to optimize a latent space representation to have specific human meaning\, e.g. training each dimension of the latent space to represent a different human-labeled feature.  A combination of learned classifiers and/or semantic segmentation systems could be trained to produce such a latent space.  These latest-space features can optionally be refined with expert-provided heuristic functions.  Relatively simple classifiers such as decision trees or tiny MLPs are a natural choice to infer predictions from such a (refined) latent space\, since their classification logic can be easily traced by humans from inputs of understandable features to the output predictions.  Recent causal discovery algorithms can yield simple MLP classifiers that potentially better model the physical realities underlying the data.  Combined\, human-understandable techniques such as these may make AI more trustworthy\, facilitate knowledge discovery\, and produce AI systems that teach the humans who are supervising the AI.
URL:https://www.ri.cmu.edu/event/making-ai-trustworthy-and-understandable-by-clinicians/
LOCATION:Newell-Simon Hall 4305
CATEGORIES:Faculty Events,Special Talk
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220715T120000
DTEND;TZID=America/New_York:20220715T130000
DTSTAMP:20260917T210452
CREATED:20220711T190941Z
LAST-MODIFIED:20220711T190941Z
UID:132316-1657886400-1657890000@www.ri.cmu.edu
SUMMARY:Five Traps for Robots in Human Environments....And How to Avoid Them
DESCRIPTION:Abstract:  Robotics today is moving beyond fixed environments and into human spaces like homes\, restaurants\, and hospitals. In these new spaces\, robots will necessarily have to interact with people. In some sense\, every recent robotics problem is partly a human-robot interaction problem. Thus\, the field of HRI can offer insights to the broader robotics community about how to create effective and beneficial robot systems that interact well with people. In this talk\, I cover five common assumptions about humans and robots\, explain why they fail\, and describe how we address them through examples from my own work.
URL:https://www.ri.cmu.edu/event/five-traps-for-robots-in-human-environments-and-how-to-avoid-them/
LOCATION:Newell-Simon Hall 4305
CATEGORIES:Faculty Events,Special Talk
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200303T120000
DTEND;TZID=America/New_York:20200303T130000
DTSTAMP:20260917T210452
CREATED:20200227T203034Z
LAST-MODIFIED:20200227T203034Z
UID:119440-1583236800-1583240400@www.ri.cmu.edu
SUMMARY:Numerical Methods for Things That Move: From Quadrupeds to Starships
DESCRIPTION:Abstract:  Recent advances in motion planning and control have led to dramatic successes like SpaceX’s rocket landings and Boston Dynamics’ humanoid robot acrobatics. However\, the underlying numerical methods used in these applications are typically decades old\, not tuned for high performance on planning and control problems\, and are often unable to cope with the types of optimization problems that arise naturally in modern robotics applications like legged locomotion and autonomous driving. This talk will introduce new numerical optimization tools being built to enable robotic systems that move with the same agility\, efficiency\, and safety as humans and animals. Some target applications include legged locomotion; autonomous driving; distributed control of satellite swarms; and spacecraft entry\, descent\, and landing. I will also discuss hardware platforms being developed to test real-time embedded implementations of key algorithms in challenging environments\, including quadrupeds\, teams of quadrotors\, and tiny satellites. \n  \nBio:  Zac Manchester is an Assistant Professor of Aeronautics and Astronautics at Stanford University\, founder of the KickSat project\, and member of the Breakthrough Starshot Advisory Committee. He holds a Ph.D. in aerospace engineering and a B.S. in applied physics from Cornell University. Zac was a postdoc in the Agile Robotics Lab at Harvard University and previously worked at NASA Ames Research Center and Analytical Graphics\, Inc. He received a NASA Early Career Faculty Award in 2018 and has led three satellite missions. His research interests include motion planning\, control\, and numerical optimization\, particularly with application to robotic locomotion and small spacecraft.
URL:https://www.ri.cmu.edu/event/numerical-methods-for-things-that-move-from-quadrupeds-to-starships/
LOCATION:Newell-Simon Hall 4305
CATEGORIES:Faculty Events,Special Talk
ATTACH;FMTTYPE=image/jpeg:https://www.ri.cmu.edu/app/uploads/2020/02/Pic-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190429T150000
DTEND;TZID=America/New_York:20190429T160000
DTSTAMP:20260917T210452
CREATED:20190425T164305Z
LAST-MODIFIED:20190425T164305Z
UID:112737-1556550000-1556553600@www.ri.cmu.edu
SUMMARY:Human-guided Task Transfer for Interactive Robots
DESCRIPTION:Abstract: \nAdaptability is an essential skill in human cognition\, enabling us to draw from our extensive\, life-long experiences with various objects and tasks in order to address novel problems. To date\, most robots do not have this kind of adaptability\, and yet\, as our expectations of robots’ interactive and assistive capacity grows\, it will be increasingly important for them to adapt to unpredictable environments in a similar manner as humans. \nIn this talk I will describe my approaches to the problem of task transfer\, enabling a robot to transfer a known task model to address scenarios containing differences in the objects used\, object configurations\, and task constraints. The primary contribution of my work is a series of algorithms for deriving and modeling domain-specific task information from structured interaction with a human teacher. In doing so\, this work enables the robot to leverage the teacher’s domain knowledge of the task (such as the contextual use of an object or tool) in order to address a range of tasks without requiring extensive exploration or re-training of the task. By enabling a robot to ask for help in addressing unfamiliar problems\, my work contributes toward a future of adaptive\, collaborative robots. \n  \nBiography: \nTesca Fitzgerald is a Computer Science PhD candidate in the School of Interactive Computing at the Georgia Institute of Technology. In her PhD\, she has been developing algorithms and knowledge representations for robots to learn\, adapt\, and reuse task knowledge through interaction with a human teacher. In doing so\, she applies concepts of social learning and cognition to develop a robot which adapts to human environments.
URL:https://www.ri.cmu.edu/event/human-guided-task-transfer-for-interactive-robots/
LOCATION:GATES-HILLMAN 4405
CATEGORIES:Faculty Events,Special Talk
ATTACH;FMTTYPE=image/jpeg:https://www.ri.cmu.edu/app/uploads/2019/04/fitzgerald_headshot.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181116T100000
DTEND;TZID=America/New_York:20181116T110000
DTSTAMP:20260917T210452
CREATED:20181109T204524Z
LAST-MODIFIED:20181109T205126Z
UID:109867-1542362400-1542366000@www.ri.cmu.edu
SUMMARY:Fully Autonomous Drones for Wind Power Turbine Inspection
DESCRIPTION:Abstract: The wind energy industry is growing rapidly. In the U.S. alone\, the wind industry invested more than $11 billion in new plants in 2017 and added more than 7\,000 megawatts of new capacity\, representing 25% of all electric capacity added. One of the biggest challenges to growth remains the high costs of constructing wind farms\, as well as the ongoing operations and maintenance costs.  We present a fully autonomous drone system for capturing inspection images and analyzing them for defects.  In less than half an hour\, the drone is able to perform an inspection where it used to take a crew of workers climbing ropes half a day to complete.  We present some of the obstacles and solutions we found while designing the complete system. \nBio: Dr. Yan Ke is a co-founder and CTO of Clobotics\, a computer vision company and drone data analytics company.  He is responsible for the overall research and development of the company’s products.  Previously\, Dr. Ke was the Chief Software Development Officer of EHang\, Inc.\, a technological innovation company specializing in R&D\, manufacturing\, and sales of intelligent aerial vehicles. Dr. Ke is an expert in computer vision\, data mining\, machine learning\, and distributed systems. Previously\, he spent eight years at Microsoft leading the Bing Entity Understanding Group\, where he architected and developed the core algorithms for Bing’s Knowledge Pane\, Question Answering System\, Satori Knowledge Graph\, and Web Index Selection.\nDr. Ke earned his bachelor’s degree in Computer Science\, master’s degree in Electrical and Computer Engineering\, and Ph.D. in Computer Science\, all from Carnegie Mellon University.  He has published in top international computer vision conferences such as IEEE CVPR\, ICCV\, and holds 8 US patents. \nHost: Martial Hebert\nContact: Lynnetta Miller
URL:https://www.ri.cmu.edu/event/fully-autonomous-drones-for-wind-power-turbine-inspection/
LOCATION:3305 Newell-Simon Hall
CATEGORIES:Faculty Events,Special Talk
ATTACH;FMTTYPE=image/jpeg:https://www.ri.cmu.edu/app/uploads/2018/11/Yan-Ke-Headshot.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180403T130000
DTEND;TZID=America/New_York:20180403T143000
DTSTAMP:20260917T210452
CREATED:20180201T152033Z
LAST-MODIFIED:20180403T164812Z
UID:104315-1522760400-1522765800@www.ri.cmu.edu
SUMMARY:Speaker: Jia Deng
DESCRIPTION:Areas of Interest: \n\n\n\n\n\n\nArtificial Intelligence\, vision\, knowledge\, reasoning\nHost: Abhinav Gupta \nAdmin Contact: Chris Downey cdowney@andrew.cmu.edu
URL:https://www.ri.cmu.edu/event/faculty-candidate-jia-deng/
LOCATION:GHC 4405
CATEGORIES:Faculty Events,Special Talk
ATTACH;FMTTYPE=image/jpeg:https://www.ri.cmu.edu/app/uploads/2018/02/jiadeng.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180319T100000
DTEND;TZID=America/New_York:20180319T113000
DTSTAMP:20260917T210452
CREATED:20180301T182755Z
LAST-MODIFIED:20180301T182755Z
UID:104784-1521453600-1521459000@www.ri.cmu.edu
SUMMARY:Special Talk: Building Robots For Long-Term Autonomy\, And Keeping Them Autonomous
DESCRIPTION:Abstract:\nWe seek the ultimate goal of having self-sufficient autonomous service mobile robots working in human environments\, performing tasks accurately and robustly.  Successfully deploying such robots requires simultaneously addressing challenges in a number of subproblems spanning the complete perception-cognition-actuation stack. In this talk\, I shall present our recent research along two broad themes: algorithms for perception\, navigation\, and control to enable computationally efficient autonomy; and algorithms to ensure that they remain autonomous over extended periods of time\, despite changes in the environment. In particular\, I shall present several algorithms for time-optimal control\, joint perception and planning\, localization in changing environments\, and non-expert guided failure recovery. These algorithms have enabled robust autonomy for our robots in real human environments\, with limited computational resources\, and while overcoming inevitable unexpected failures in the real world. \nBio:\nJoydeep Biswas is an Assistant Professor in the College of Information and\nComputer Sciences at the University of Massachusetts Amherst. He earned his\nPh.D. in Robotics from Carnegie Mellon University in 2014\, and prior to that\na B.Tech. in Engineering Physics from the Indian Institute of Technology\,\nBombay in 2008. Professor Biswas’ research on autonomous service mobile robots\nand robot soccer has been covered by several media and news articles.
URL:https://www.ri.cmu.edu/event/special-talk-building-robots-long-term-autonomy-keeping-autonomous/
LOCATION:GHC 6115
CATEGORIES:Faculty Events,Special Talk
ATTACH;FMTTYPE=image/jpeg:https://www.ri.cmu.edu/app/uploads/2018/03/joydeepb.jpg
END:VEVENT
END:VCALENDAR