Mike Stilman Ph.D.

Assistant Professor
Robotics and Intelligent Machines
School of Interactive Computing
Georgia Tech
801 Atlantic Drive
Atlanta, GA 30332


650.283.4284



Humanoid Robotics: Robots that look, think and act in a way that we perceive as human or intelligent. Dexterous, mobile robots that resemble human beings are a reality. In my research, I strive to develop algorithms for planning and control that would enable these robots to autonomously perform human tasks.

Human tasks, from everyday housekeeping to complex search and rescue, almost always require significant interaction with the environment. Pushing objects out of the way, using tools and supports are all examples of the immense possibilities for environment contact. Humans not only excercise such contact routinely, but do so intuitively - often subconsciously. I believe that choosing meaningful contacts and performing useful manipulation that is unspecified in a robot's task would not only expand the skillset of current robots, but also demonstrate progress towards Artificial Intelligence in the physical world.

In contrast to humans, robots have no intuition. At least not yet. Real-world settings could contain hundreds of objects, not to mention the unlimited possibilities for support. How would the robot decide which environment interactions are useful? How would it plan to perform the interactions with guarantees of safety and stability? What would it do if something went wrong? These are the questions I address in my research.


Research Projects and Selected Publications
Navigation Among Movable Obstacles [Project Webpage]

Abstract: We introduce a practical extension to navigation for humanoids and other dexterous mobile robots. The robot is permitted to reconfigure the environment by moving obstacles and clearing free space for a path. This work presents a resolution complete planner for a subclass of NAMO problems. Our planner takes advantage of the navigational structure through state-space decomposition and heuristic search. The planning complexity is reduced to the difficulty of the specific navigation task, rather than the dimensionality of the multiobject domain. We demonstrate real-time results for spaces that contain large numbers of movable obstacles. We also present a practical framework for single-agent search that can be used in algorithmic reasoning about this domain. [an error occurred while processing this directive]

M. Stilman, K. Nishiwaki, S. Kagami and J. Kuffner. Planning and Executing Navigation Among Movable Obstacles , Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Oct., 2006. [PDF][BibTex]

M. Stilman and J.J. Kuffner. Navigation Among Movable Obstacles: Real-Time Reasoning in Complex Environments, International Journal of Humanoid Robotics, Vol. 2, No. 4, December, 2005, pp. 479-504.[PDF][BibTex]


Humanoid Tele-Manipulation

Abstract: We present results of successful telemanipulation of large, heavy objects by a humanoid robot. Using a single joystick the operator controls walking and whole body manipulation along arbitrary paths for up to ten minutes of continuous execution. The robot grasps, walks, pushes, pulls, turns and re-grasps a 55kg range of loads on casters. Our telemanipulation framework changes reference frames online to let the operator steer the robot in free walking, its hands in grasping and the object during mobile manipulation. In the case of manipulation, our system computes a robot motion that satisfies the commanded object path as well as the kinematic and dynamic constraints of the robot. Furthermore, we achieve increased robot stability by learning dynamic friction models of manipulated objects.

M. Stilman, K. Nishiwaki and S. Kagami Humanoid Teleoperation for Whole Body Manipulation, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'08), May, 2008. [PDF][BibTex]

M. Stilman, K. Nishiwaki and S. Kagami Learning Object Models for Whole Body Manipulation, Proceedings of the 2007 IEEE Int. Conf. on Humanoid Robotics (Humanoids'07), November, 2007.[PDF][BibTex]



Compliant Motion Planning

Abstract: A study of randomized joint space path planning for articulated robots that are subject to task space constraints.This paper presents 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). Constrained joint space planning is important for many real world problems involving redundant manipulators. On the one hand, tasks are designated in work space coordinates: rotating doors about fixed axes, sliding drawers along fixed trajectories or holding objects level during transport. On the other, joint space planning gives alternative paths that use redundant degrees of freedom to avoid obstacles or satisfy additional goals while performing a task. In simulation, we demonstrate that our methods are faster and significantly more invariant to problem/algorithm parameters than existing techniques.

Mike Stilman. Task Constrained Motion Planning in Robot Joint Space In Proceedings IEEE/RSJ Int. Conf. on Intelligent Robots and Systems IROS 07, Oct. 2007.[PDF] [BibTex]



Dynamic Biped Locomotion

Abstract: We explore the use of computational optimal control techniques for automated construction of policies in complex dynamic environments. Our implementation of dynamic programming is performed in a reduced dimensional subspace of a simulated four-DOF biped robot with point feet. We show that a computed solution to this problem can be generated and yield empirically stable walking that can handle various types of disturbances.

Mike Stilman, C.G. Atkeson, J. Kuffner, and G. Zeglin. Dynamic Programming in Reduced Dimensional Spaces: Dynamic Planning For Robust Biped Locomotion Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'05), April, 2005. [PDF][BibTex]