Archive |

MEAM Seminar: “Design, Fabrication, and Control of Biologically Inspired Soft Robots”

Robotics has the potential to address many of today’s pressing problems in fields ranging from healthcare to manufacturing to disaster relief. However, the traditional approaches used on the factory floor do not perform well in unstructured environments. The key to solving many of these challenges is to explore new, non-traditional designs. Fortunately, nature surrounds us […]

Spring 2021 GRASP Seminar: “Propelling Robot Manipulation of Unknown Objects using Learned Object Centric Models”

Abstract: There is a growing interest in using data-driven methods to scale up manipulation capabilities of robots for handling a large variety of objects. Many of these methods are oblivious to the notion of objects and they learn monolithic policies from the whole scene in image space. As a result, they don’t generalize well to […]

GRASP On Robotics: “Perspectives on Machine Learning for Adaptive Robotic Systems”

Abstract: Recent advances in machine learning are leading to new tools for designing intelligent robots: functions relied on to govern a robot’s behavior can be learned from a robot’s interaction with its environment rather than hand-designed by an engineer. Many machine learning methods assume little prior knowledge and are extremely flexible, they can model almost […]

Spring 2021 GRASP SFI: “Robotic Caregivers—Sensing, Simulation, and Physical Human-Robot Interaction”

Abstract: Autonomous robots have the potential to serve as versatile caregivers that improve quality of life for millions of people with disabilities worldwide. Yet, physical robotic assistance presents several challenges, including risks associated with physical human-robot interaction, difficulty sensing the human body, and a lack of tools for benchmarking and training physically assistive robots. In […]

GRASP/MEAM Seminar: “Towards Safe and Efficient Learning and Control for Physical Human Robot Interaction”

From factories to households, we envision a future where robots can work safely and efficiently alongside humans. For robots to truly be adopted in such dynamic environments, we must i) minimize human effort while communicating and transferring tasks to robots; ii) endow robots with the capabilities of adapting to changes in the environment, in the […]

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 […]

1 2 3 4
Archive |