Our team is pioneering high-risk, high-reward research in revolutionary robotic systems, robotic intuition, robust robotic super agility, and teaming and trust.
Program Manager: Kapil Katyal
Focus Areas
REVOLUTIONARY ROBOTIC SYSTEMS
Advancing contributing capabilities to significantly improve robot system capabilities
The capability of a robotic system is very much a function of the independent capabilities of its component capabilities, including mobility, perception, intelligence, awareness, communications, and state space characterization. Research here centers on significantly advancing component capabilities to overcome inherent shortcomings that up to now have limited functionality in complex environments.
ROBOTIC INTUITION
Enabling robots with human-quality decision-making
Robotic systems must be able to perform many tasks in highly dynamic and unstructured environments. This capability can be enabled by developing algorithms that allow the robot to continually learn, adapt, predict, and reason about how its actions will affect the physical world. In addition, robots must be able to learn generalized concepts that can transfer across tasks and environments. This intuition is what will enable them to choose reasonable actions even in situations with high uncertainty.
ROBUST ROBOTIC SUPER AGILITY
Enabling air and ground platforms with an ability to maneuver aggressively through complex control
Today’s robots fall far short of the speed and robust agility required for battlefield dominance. This is not because they are physically incapable of such performance but because their control systems cannot reason intelligently about these complex dynamic regimes where underactuation dominates. We believe advances in three areas—algorithmic control theory, learning for control, and output feedback—are required to achieve robust robotic super agility.
TEAMING AND TRUST
Optimizing human-machine teaming, including assurance considerations
A new paradigm is necessary that results in an adequate level of confidence and assurance that a system is behaving in an acceptable and expected way. Further, any human associated with the system (e.g., a teammate) will have to be able to attain a psychologically based level of trust enabled through an understanding of the underlying capabilities, experience operating with the autonomous system, and real-time state awareness.
Research Highlights
News Story
Johns Hopkins APL Researchers Aim to Set Safer Paths for Swift-Flying Robots
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Despite significant advances in drone technology and autonomous systems over the last couple of decades, a world where small, swift robots dart and weave through pedestrians, bikes and cars in the urban landscape remains firmly in the realm of science fiction. But researchers at APL have made significant progress on two key technical problems that stand in the way of fiction becoming a reality.
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News Story
Johns Hopkins APL Helps DARPA OFFSET Program Take Flight
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As part of DARPA’s OFFSET program, an APL team proved fixed-wing unmanned aerial vehicles (UAVs) could have quadcopter UAV agility and mobility but add greater range, endurance and speed.
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Press Release
Six From Johns Hopkins APL Earn Johns Hopkins Diversity Leadership Awards
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Six staff members from APL are among this year’s recipients of Johns Hopkins Diversity Leadership Council Diversity Recognition Awards, which honor individuals and groups for their efforts to advance diversity and inclusion in the Johns Hopkins community.
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Press Release
APL Discovery Projects to Explore Disaster Health, Ethical Robots, School Safety
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Three APL principal investigators were among researchers from across the Johns Hopkins enterprise to earn funding in the fifth round of the JHU Discovery Awards program, created in 2015 to spark synergistic interactions across JHU.
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Press Release
APL Researchers Know the CODE to Unmanned Aircraft Program’s Success
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A multidisciplinary team of researchers from APL is helping to solve one of the Defense Department’s most significant challenges: the test and evaluation of autonomous unmanned aerial systems.
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