(Georgia Institute of Technology, 2013-06)
Dantam, Neil; Hereid, Ayonga; Ames, Aaron; Stilman, Mike
We present a software synthesis method for speed-
controlled robot walking based on supervisory control of a
context-free Motion Grammar. First, we use Human-Inspired
control to identify parameters for fixed speed walking and for
transitions between fixed speeds, guaranteeing dynamic stability.
Next, we build a Motion Grammar representing the discrete-
time control for this set of speeds. Then, we synthesize C
code from this grammar and generate supervisors¹ online to
achieve desired walking speeds, guaranteeing correctness of
discrete computation. Finally, we demonstrate this approach on
the Aldebaran NAO, showing stable walking transitions with
dynamically selected speeds.
(Georgia Institute of Technology, 2013-05)
Ok, Kyel; Ansari, Sameer; Gallagher, Billy; Sica, William; Dellaert, Frank; Stilman, Mike
In this paper, a two-level path planning algorithm
that deals with map uncertainty is proposed. The higher
level planner uses modified generalized Voronoi diagrams to
guarantee finding a connected path from the start to the goal
if a collision-free path exists. The lower level planner considers
uncertainty of the observed obstacles in the environment and
assigns repulsive forces based on their distance to the robot
and their positional uncertainty. The attractive forces from the
Voronoi nodes and the repulsive forces from the uncertainty-
biased potential fields form a hybrid planner we call Voronoi
Uncertainty Fields (VUF). The proposed planner has two
strong properties: (1) bias against uncertain obstacles, and (2)
completeness. We analytically prove the properties and run
simulations to validate our method in a forest-like environment.
(Georgia Institute of Technology, 2012-10)
Dantam, Neil; Essa, Irfan; Stilman, Mike
We demonstrate the automatic transfer of an
assembly task from human to robot. This work extends efforts
showing the utility of linguistic models in verifiable robot
control policies by now performing real visual analysis of
human demonstrations to automatically extract a policy for the
task. This method tokenizes each human demonstration into a
sequence of object connection symbols, then transforms the set
of sequences from all demonstrations into an automaton, which
represents the task-language for assembling a desired object.
Finally, we combine this assembly automaton with a kinematic
model of a robot arm to reproduce the demonstrated task.
(Georgia Institute of Technology, 2010-10)
Wu, Hai-Ning; Levihn, Martin; Stilman, Mike
This paper explores the Navigation Among Movable
Obstacles (NAMO) problem in an unknown environment.
We consider the realistic scenario in which the robot has
to navigate to a goal position in an unknown environment
consisting of static and movable objects. The robot may move
objects if the goal can not be reached otherwise or if moving
the object may significantly shorten the path to the goal.
We consider real situations in which the robot only has
limited sensing information and where the action selection
can therefore only be based on partial knowledge learned
from the environment at that point. This paper introduces an
algorithm that significantly reduces the necessary calculations
to accomplish this task compared to a direct approach. We
present an efficient implementation for the case of planar,
axis-aligned environments and report experimental results on
challenging scenarios with more than 50 objects.