(Georgia Institute of Technology, 2011-09)
Cosgun, Akansel; Hermans, Tucker; Emeli, Victor; Stilman, Mike
We present a novel planning algorithm for the
problem of placing objects on a cluttered surface such as a
table, counter or floor. The planner (1) selects a placement for
the target object and (2) constructs a sequence of manipulation
actions that create space for the object. When no continuous
space is large enough for direct placement, the planner leverages
means-end analysis and dynamic simulation to find a sequence
of linear pushes that clears the necessary space. Our heuristic
for determining candidate placement poses for the target object
is used to guide the manipulation search. We show successful
results for our algorithm in simulation.
(Georgia Institute of Technology, 2011-09)
Akgun, Baris; Stilman, Mike
We present a sampling-based motion planner that
improves the performance of the probabilistically optimal
RRT* planning algorithm. Experiments demonstrate that our
planner finds a fast initial path and decreases the cost of this
path iteratively. We identify and address the limitations of
RRT* in high-dimensional configuration spaces. We introduce a
sampling bias to facilitate and accelerate cost decrease in these
spaces and a simple node-rejection criteria to increase efficiency.
Finally, we incorporate an existing bi-directional approach to
search which decreases the time to find an initial path. We
analyze our planner on a simple 2D navigation problem in
detail to show its properties and test it on a difficult 7D
manipulation problem to show its effectiveness. Our results
consistently demonstrate improved performance over RRT*.
(Georgia Institute of Technology, 2011-06)
Dantam, Neil; Stilman, Mike
We present and analyze the Motion Grammar: a
novel unified representation for task decomposition, perception,
planning, and control that provides both fast online control of
robots in uncertain environments and the ability to guarantee
completeness and correctness. The grammar represents a policy
for the task which is parsed in real-time based on perceptual
input. Branches of the syntax tree form the levels of a hierarchical
decomposition, and the individual robot sensor readings are given
by tokens. We implement this approach in the interactive game
of Yamakuzushi on a physical robot resulting in a system that
repeatably competes with a human opponent in sustained gameplay
for the roughly six minute duration of each match.