George D Konidaris
· Associate Professor of Computer ScienceBrown University · Computer Science
Active 2002–2025
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About
George D. Konidaris is an Associate Professor of Computer Science at Brown University and the director of the Intelligent Robot Lab, which is part of Brown's Integrative, General AI initiative (bigAI). His research is driven by the overarching scientific goal of understanding the fundamental computational processes that generate intelligence and using this understanding to design generally intelligent robots. Konidaris focuses on building intelligent, autonomous, general-purpose robots capable of performing a wide variety of tasks and operating in diverse environments. His work centers on designing agents that learn abstraction hierarchies to enable fast, goal-oriented planning. He develops and applies techniques from machine learning, reinforcement learning, optimal control, and planning to construct well-grounded hierarchies that facilitate rapid planning in common cases while maintaining robustness to uncertainty at every level of control. He emphasizes that solving the AI problem requires advances in all these areas as well as in their integration. In addition to his academic role, Konidaris is a co-founder of two technology startups. Realtime Robotics commercializes research on robot motion planning to simplify and improve robotic automation, while Lelapa AI is a commercial AI research lab based in Johannesburg, South Africa, focused on technology developed by and for Africans. Konidaris lives in Providence, Rhode Island, where he continues to lead research efforts…
Research topics
- Artificial Intelligence
- Computer Science
- Machine Learning
- Computer Security
- Computer vision
- Engineering
- Human–computer interaction
- Mathematics
- Medicine
- Mathematical analysis
Selected publications
A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms
Journal of Machine Learning Research · 2021 · 162 citations
Senior authorCorrespondingA key challenge in intelligent robotics is creating robots that are capable of directly interacting with the world around them to achieve their goals. The last decade has seen substantial growth in research on the problem of robot manipulation, which aims to exploit the increasing availability of affordable robot arms and grippers to create robots capable of directly interacting with the world to achieve their goals. Learning will be central to such autonomous systems, as the real world contains…
IEEE Transactions on Robotics · 2020 · 74 citations
Ophthalmic microsurgery is technically difficult because the scale of required surgical tool manipulations challenge the limits of the surgeon's visual acuity, sensory perception, and physical dexterity. Intraoperative optical coherence tomography (OCT) imaging with micrometer-scale resolution is increasingly being used to monitor and provide enhanced real-time visualization of ophthalmic surgical maneuvers, but surgeons still face physical limitations when manipulating instruments inside the ey…
RMPs for Safe Impedance Control in Contact-Rich Manipulation
2022 International Conference on Robotics and Automation (ICRA) · 2022 · 10 citations
Senior authorCorrespondingVariable impedance control in operation-space is a promising approach to learning contact-rich manipulation behaviors. One of the main challenges with this approach is producing a manipulation behavior that ensures the safety of the arm and the environment. Such behavior is typically implemented via a reward function that penalizes unsafe actions (e.g. obstacle collision, joint limit extension), but that approach is not always effective and does not result in behaviors that can be reused in slig…
2024-05-13 · 4 citations
articleSenior authorWe propose a new policy class, Composable Interaction Primitives (CIPs), specialized for learning sustained-contact manipulation skills like opening a drawer, pulling a lever, turning a wheel, or shifting gears. CIPs have two primary design goals: to minimize what must be learned by exploiting structure present in the world and the robot, and to support sequential composition by construction, so that learned skills can be used by a task-level planner. Using an ablation experiment in four simulat…
Robot Task Planning Under Local Observability
2024-05-13 · 2 citations
articleSenior authorReal-world robot task planning is intractable in part due to partial observability. A common approach to reducing complexity is introducing additional structure into the decision process, such as mixed-observability, factored states, or temporally-extended actions. We propose the locally observable Markov decision process, a novel formulation that models task-level planning where uncertainty pertains to object-level attributes and where a robot has subroutines for seeking and accurately observin…
Recent grants
RI: Medium: Learning Task-Specific Representations for Broadly Capable Reinforcement Learning Agents
NSF · $1.2M · 2020–2024
CAREER: Learning Symbolic Representations for Robot Manipulation
NSF · $566k · 2019–2024
NSF · $208k · 2017–2021
Frequent coauthors
- 70 shared
Stefanie Tellex
John Brown University
- 33 shared
Eric Rosen
John Brown University
- 29 shared
Michael L. Littman
- 24 shared
Andrew G. Barto
Amherst College
- 20 shared
Ben Abbatematteo
- 20 shared
Leslie Pack Kaelbling
- 19 shared
Benjamin Burchfiel
- 15 shared
Cameron Allen
Monash University
Education
- 2009
Ph.D., Computer Science
Brown University
- 2005
M.S., Computer Science
Brown University
- 2003
B.S., Computer Science
Brown University
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