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IRIM Seminar Series

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  • Item
    Virtual Reality: Full Steam Ahead
    ( 2014-09-05) LaValle, Steven M. ; Institute for Robotics and Intelligent Machines ; University of Illinois at Urbana-Champaign. Dept. of Computer Science ; Oculus VR
    Using the latest technology, we can safely hijack your most trusted senses, thereby fooling your brain into believing you are in another world. Virtual reality (VR) has been around for a long time, but due to the recent convergence of sensing, display, and computation technologies, there is an unprecedented opportunity to explore this form of human augmentation with lightweight, low-cost materials and simple software platforms. This is an intense form of human-computer interaction (HCI) that requires re-examining core engineering principles with a direct infusion of perceptual psychology research. Developing systems that optimize classical criteria might lead to overcomplicated solutions that are too slow or costly in practice, and yet could make no perceptible difference to users. Simple adaptation of techniques that were developed for on-screen viewing, such as cinematography and first-person shooter game play, often lead to unpleasant VR experiences due the presentation of unusual stimuli or due to mismatches between the human vestibular system and other senses. With the rapid rise in consumer VR, fundamental research questions are popping up everywhere, slicing across numerous disciplines from engineering to sociology, to film, to medicine. This talk will provide some perspective on where we have been and where we might be going next.
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    University of Michigan’s Work Toward Autonomous Cars
    ( 2016-04-20) Eustice, Ryan M. ; Institute for Robotics and Intelligent Machines ; University of Michigan. Perceptual Robotics Laboratory
    Self-driving test vehicles have become a reality on roadways, and there is an ever-present push toward making them a consumer product in the not-so-distant future. In this talk, I will give an overview of some of our previous work in collaboration with Ford Motor Company in full-scale automated driving. In particular, we’ll look at some of our successes in high-definition map building and precision localization, including our recent work in cross-modality localization using vision within a priori LIDAR maps as well as localization in snow using Gaussian mixture maps. We’ll also discuss our new unique Mcity test facility for connected and automated driving.
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    Robots for Physical Interaction
    (Georgia Institute of Technology, 2019-01-23) Kim, Sangbae ; Institute for Robotics and Intelligent Machines ; Massachusetts Institute of Technology. Department of Mechanical Engineering
    While robots dominate repetitive works in factories, the design and control of these robots are not suitable for relatively complex tasks that humans do easily. These tasks typically require force sensing and interaction force control. Conventional robots are not built to control force or to be flexible to perform like human arms.
 This talk will discuss how the new design paradigm allows dynamic interactive force control within environments. As an embodiment of such a robot design paradigm, the latest version of the Cheetah robot and force-feedback teleoperation arms will be presented. This new class of robots will play a crucial role in future robot applications such as elderly care, home service, delivery, and services in environments unfavorable for humans.
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    From Learning Movement Primitives to Associative Skill Memories
    (Georgia Institute of Technology, 2013-08-21) Schaal, Stefan ; Institute for Robotics and Intelligent Machines ; Georgia Institute of Technology. Center for Robotics and Intelligent Machines
    Skillful and goal-directed interaction with a dynamically changing world is among the hallmarks of human perception and motor control. Understanding the mechanisms of such skills and how they are learned is a long-standing question in both neuroscience and technology. This talk develops a general framework of how motor skills can be learned. At the heart of our work is a general representation of motor skills in terms of movement primitives as nonlinear attractor systems, the ability to generalize a motor skill to novel situations and to adjust it to sudden perturbations, and the ability to employ imitation learning, trial-and-error learning, and model-based learning to improve planning and control of a motor skill. Our framework has close connections to known phenomena in behavioral and neurosciences, and it also intuitively bridges between dynamic systems theory and optimization theory in motor control, two rather disjoint approaches. We evaluate our approach in various behavioral and robotic studies with anthropomorphic and humanoid robots. Finally, we discuss how to go beyond simple movement primitives to a more complete perception-action-learning system, and speculate on the concept of Associative Skill Memories as an interesting approach.
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    Shared Control of Functional Electrical Stimulation and an Electric Motor in a Hybrid Neuroprosthesis
    (Georgia Institute of Technology, 2017-03-08) Sharma, Nitin ; Institute for Robotics and Intelligent Machines ; University of Pittsburgh
    Functional Electrical Stimulation (FES) can be used to artificially activate paralyzed lower limb muscles to restore walking and standing function in persons with neurological disorders. Despite its potential, FES-based walking neuroprosthesis has achieved limited acceptability among persons with paraplegia. This low acceptance is primarily due to the early onset of muscle fatigue during FES and difficulty in obtaining a consistent and reliable response from the paralyzed muscle using traditional control methods. We are employing a hybrid strategy that integrates FES with a powered exoskeleton to overcome these hurdles. This hybrid strategy has several advantages. The main advantage is that the effects of muscle fatigue and any inconsistent response from FES can be compensated by the active exoskeleton, which can potentially lead to improved functional mobility in users with neurological impairments. Other advantages include a reduction in the overall weight of the exoskeleton and neuroplastic improvements in the neuromuscular system due to FES. However, closed-loop control methods are required to effectively integrate FES with a powered exoskeleton because the hybrid combination leads to redundancy in actuation and needs criteria to allocate control between FES and an electric motor. I will present algorithms and models recently developed by our research group to control the hybrid exoskeleton. These methods include: 1) a muscle fatigue model to inform the onset of muscle fatigue and muscle recovery during FES, 2) Shared control of FES and electric motor based on the fatigue model, and 3) muscle synergy inspired control of a hybrid walking exoskeleton.
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    Robots with Privacy Stipulations
    (Georgia Institute of Technology, 2018-02-14) Shell, Dylan ; Institute for Robotics and Intelligent Machines ; Texas A & M University
    In late July last year, it came to light that iRobot Corp. intended to sell the maps that modern Roomba vacuum cleaning robots build to help them navigate. This caused a public furor among consumers. This situation and several others (e.g., nuclear inspection, use of untrusted cloud computing infrastructure) suggest that we might be interested in limiting what information a robot might divulge. How should we think about robotic privacy? In this talk I’ll describe a line of research that is concerned with this question, starting by showing that cryptography doesn’t solve the problem. I’ll begin by examining a privacy-preserving tracking task, then look at how one might think about estimators that are constrained to ensure they never know too much. Finally, I’ll talk about planning subject to information disclosure constraints and introduce a useful structure that we call a “plan closure.”
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    Data-to-Decisions for Safe Autonomous Flight
    (Georgia Institute of Technology, 2018-11-07) Atkins, Ella ; Machine Learning Center ; College of Computing ; Institute for Robotics and Intelligent Machines
    Traditional sensor data can be augmented with new data sources such as roadmaps and geographical information system (GIS) Lidar/video to offer emerging unmanned aircraft systems (UAS) and urban air mobility (UAM) a new level of situational awareness. This presentation will summarize my group’s research to identify, process, and utilize GIS, map, and other real-time data sources during nominal and emergency flight planning. Specific efforts have utilized machine learning to automatically map flat rooftops as urban emergency landing sites, incorporate cell phone data into an occupancy map for risk-aware flight planning, and extend airspace geofencing into a framework capable of managing all traffic types in complex airspace and land-use environments. The presentation will end with videos illustrating recent work to experimentally validate the continuum deformation cooperative control strategy in the University of Michigan’s new M-Air netted flight facility.
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    Institute for Robotics and Intelligent Machines (IRIM) Industry Day Panel - Career Options in Robotics
    (Georgia Institute of Technology, 2021-03-17) Burnstein, Jeff ; Battles, Jon ; Joppru, Mark ; Balakirsky, Stephen ; Institute for Robotics and Intelligent Machines ; Association for Advancing Automation (A3) ; Amazon.com (Firm) ; ABB Robotics ; Georgia Tech Research Institute. Aerospace, Transportation, and Advanced Systems Laboratory
    The robotics industry is one of the fastest growing industries in the world. The industry is rapidly changing as robotics have moved from the manufacturing floor into almost every segment of our lives. This seminar will be a panel discussion with industry leaders to discuss the career opportunities in companies and the breath of opportunities available to the students. They will discuss internship opportunities as well as full-time positions with their respective companies.
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    Creating Robots That See
    (Georgia Institute of Technology, 2018-08-29) Corke, Peter ; Institute for Robotics and Intelligent Machines ; Queensland University of Technology
    This talk will define and motivate the problem of robotic vision, the challenges as well as recent progress at the Australian Centre for Robotic Vision. This includes component technologies such as novel cameras, deep learning for computer vision, transfer learning for manipulation, evaluation methodologies, and also end-to-end systems for applications such as logistics, agriculture, environmental remediation and asset inspection.
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    Closing the Gap Between Machine Learning and Robotics
    ( 2016-10-05) Boots, Byron ; Institute for Robotics and Intelligent Machines ; Georgia Institute of Technology. School of Interactive Computing
    Given a stream of multimodal sensory data, an autonomous robot must continuously refine its understanding of itself and its environment as it makes decisions on how to act to achieve a goal. These are difficult problems that roboticists have attacked using classical tools from mechanics and controls and, more recently, machine learning. However, classical methods and machine learning algorithms are often seen to be at odds, and researchers continue to debate the merits of engineering vs. learning. A recurring theme in this talk will be that prior knowledge and domain insights can make learning and inference easier. I will discuss several fundamental robotics problems including continuous-time motion planning, localization, and mapping from a unified probabilistic inference perspective. I will show how models from statistical machine learning like Gaussian Processes can be tightly integrated with insights from engineering expressed as differential equations to solve these problems efficiently. Finally, I will demonstrate the effectiveness of these algorithms on several existent robotics platforms.