M
Mike Stilman
Researcher at Georgia Institute of Technology
Publications - 91
Citations - 3479
Mike Stilman is an academic researcher from Georgia Institute of Technology. The author has contributed to research in topics: Robot & Humanoid robot. The author has an hindex of 30, co-authored 91 publications receiving 3030 citations. Previous affiliations of Mike Stilman include Massachusetts Institute of Technology & Carnegie Mellon University.
Papers
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Proceedings ArticleDOI
Manipulation Planning Among Movable Obstacles
TL;DR: This paper presents the resolve spatial constraints (RSC) algorithm for manipulation planning in a domain with movable obstacles and identifies methods for sampling object surfaces and generating connecting paths between grasps and placements to optimize the efficiency.
Journal ArticleDOI
DART: Dynamic Animation and Robotics Toolkit
Jeongseok Lee,Michael X. Grey,Sehoon Ha,Tobias Kunz,Sumit Jain,Yuting Ye,Siddhartha S. Srinivasa,Mike Stilman,C. Karen Liu +8 more
TL;DR: DART (Dynamic Animation and Robotics Toolkit) is a collaborative, cross-platform, open source library that features a multibody dynamic simulator and various kinematic tools for control and motion planning.
Proceedings ArticleDOI
Navigation among movable obstacles: real-time reasoning in complex environments
Mike Stilman,James J. Kuffner +1 more
TL;DR: This paper presents a resolution complete planner for a subclass of NAMO problems, which takes advantage of the navigational structure through state-space decomposition and heuristic search and presents a practical framework for single-agent search that can be used in algorithmic reasoning about this domain.
Journal ArticleDOI
Global Manipulation Planning in Robot Joint Space With Task Constraints
TL;DR: This paper describes a representation of constrained motion for joint-space planners and develops two simple and efficient methods for constrained sampling of joint configurations: tangent-space sampling (TS) and first-order retraction (FR).
Proceedings ArticleDOI
Task constrained motion planning in robot joint space
TL;DR: This paper describes a representation of constrained motion for joint space planners and develops two simple and efficient methods for constrained sampling of joint configurations: Tangent Space Sampling (TS) and First-Order Retraction (FR).