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Spring 2021 GRASP SFI: “Considerations for Human-Robot Collaboration”

Abstract: The field of robotics has evolved over the past few decades. We’ve seen robots progress from the automation of repetitive tasks in manufacturing to the autonomy of mobilizing in unstructured environments to the cooperation of swarm robots that are centralized or decentralized. These abilities have required advances in robotic hardware, modeling, and artificial intelligence. […]

GRASP On Robotics: “Advancing Innovations for Robotic Teams in Complex Environments”

Abstract: Complex real-world environments continue to present significant challenges for fielding robotic teams, which often face expansive spatial scales, difficult and dynamic terrain, degraded environmental conditions, and severe communication constraints. Breakthrough technologies call for integrated solutions across autonomy, perception, networking, mobility, and human teaming thrusts. As such, the DARPA OFFSET program and the DARPA Subterranean […]

Spring 2021 GRASP SFI: “Safe and Data-efficient Learning for Robotics”

Abstract: For successful integration of autonomous systems such as drones and self-driving cars in our day-to-day life, they must be able to quickly adapt to ever-changing environments, and actively reason about their safety and that of other users and autonomous systems around them. Even though control-theoretic approaches have been used for decades now for the […]

MEAM Ph.D. Thesis Defense: “Reactive Planning with Legged Robots in Unknown Environments”

Unlike the problem of safe task and motion planning in a completely known environment, the setting where the obstacles in a robot’s workspace are not initially known and are incrementally revealed online has so far received little theoretical interest, with existing algorithms usually demanding constant deliberative replanning in the presence of unanticipated conditions. Moreover, even […]

MEAM Seminar: “Fusion for Robot Perception and Controls”

Machine learning has led to powerful advances in robotics: deep learning for visual perception from raw images and deep reinforcement learning (RL) for learning controls from trial and error. Yet, these black-box techniques can often require large amounts of data, have results difficult to interpret, and fail catastrophically when dealing with out-of-distribution data. In this […]

GRASP On Robotics: “Photoacoustic Vision for Surgical Robots”

Abstract: The concept of “x-ray vision” is widely understood to be the ability to see through structures that are not transparent to the human eye. This concept would be a useful feature for surgeons and surgical robots, particularly when navigating complex anatomy. The Photoacoustic & Ultrasonic Systems Engineering (PULSE) Lab is developing imaging systems to […]

Spring 2021 GRASP SFI: “Hunting for Unknown Unknowns: AI and Ethics in Society”

Abstract: Homo Sapiens is considered a “hyper-cooperative species,” and this aptitude for cooperation may be responsible for our dominance over the Earth. Cooperation promises great benefits, but each participant is vulnerable to exploitation by their partners. Successful cooperation requires trust: acceptance of vulnerability, with confidence that it will not be exploited. The culture of any society […]

GRASP On Robotics: “Trajectory Planning Using Dynamic and Power Models: a Heuristics-Based Approach”

Abstract: Robot planning is needed for robots to perform purposeful missions in their environments. In realistic situations, planning does not simply involve getting to a desired destination without collisions, but often requires achieving a desired goal configuration in an optimal or near-optimal fashion. Common optimality criteria include minimum distance, minimum time, and minimum energy. To […]

Spring 2021 GRASP SFI: “Model-Based Deep RL for Robotics”

Abstract: Deep learning has shown promising results in robotics, but we are still far from having intelligent systems that can operate in the unstructured settings of the real world, where disturbances, variations, and unobserved factors lead to a dynamic environment. In the first part of the talk, I will show that model-based deep RL can […]

GRASP On Robotics: “Designing Human Interaction with Agents and Robots”

Abstract: Over the last decade, the idea that robots and agents might participate meaningfully in complex group and organizational contexts has developed from a promising vision into a reality. Robots now assist humans in simple tasks such as delivery through complex, high-stakes tasks such as disaster response and surgery. In this talk, I will introduce […]

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