3,087 research outputs found
Learning to Navigate Cloth using Haptics
We present a controller that allows an arm-like manipulator to navigate
deformable cloth garments in simulation through the use of haptic information.
The main challenge of such a controller is to avoid getting tangled in, tearing
or punching through the deforming cloth. Our controller aggregates force
information from a number of haptic-sensing spheres all along the manipulator
for guidance. Based on haptic forces, each individual sphere updates its target
location, and the conflicts that arise between this set of desired positions is
resolved by solving an inverse kinematic problem with constraints.
Reinforcement learning is used to train the controller for a single
haptic-sensing sphere, where a training run is terminated (and thus penalized)
when large forces are detected due to contact between the sphere and a
simplified model of the cloth. In simulation, we demonstrate successful
navigation of a robotic arm through a variety of garments, including an
isolated sleeve, a jacket, a shirt, and shorts. Our controller out-performs two
baseline controllers: one without haptics and another that was trained based on
large forces between the sphere and cloth, but without early termination.Comment: Supplementary video available at https://youtu.be/iHqwZPKVd4A.
Related publications http://www.cc.gatech.edu/~karenliu/Robotic_dressing.htm
The minimum energy expenditure shortest path method
This article discusses the addition of an energy parameter to the shortest path execution process; namely, the energy expenditure by a character during execution of the path. Given a simple environment in which a character has the ability to perform actions related to locomotion, such as walking and stair stepping, current techniques execute the shortest path based on the length of the extracted root trajectory. However, actual humans acting in constrained environments do not plan only according to shortest path criterion, they conceptually measure the path that minimizes the amount of energy expenditure. On this basis, it seems that virtual characters should also execute their paths according to the minimization of actual energy expenditure as well. In this article, a simple method that uses a formula for computing vanadium dioxide () levels, which is a proxy for the energy expenditure by humans during various activities, is presented. The presented solution could be beneficial in any situation requiring a sophisticated perspective of the path-execution process. Moreover, it can be implemented in almost every path-planning method that has the ability to measure stepping actions or other actions of a virtual character
A Novel Graph-based Motion Planner of Multi-Mobile Robot Systems with Formation and Obstacle Constraints
Multi-mobile robot systems show great advantages over one single robot in
many applications. However, the robots are required to form desired
task-specified formations, making feasible motions decrease significantly.
Thus, it is challenging to determine whether the robots can pass through an
obstructed environment under formation constraints, especially in an
obstacle-rich environment. Furthermore, is there an optimal path for the
robots? To deal with the two problems, a novel graphbased motion planner is
proposed in this paper. A mapping between workspace and configuration space of
multi-mobile robot systems is first built, where valid configurations can be
acquired to satisfy both formation constraints and collision avoidance. Then,
an undirected graph is generated by verifying connectivity between valid
configurations. The breadth-first search method is employed to answer the
question of whether there is a feasible path on the graph. Finally, an optimal
path will be planned on the updated graph, considering the cost of path length
and formation preference. Simulation results show that the planner can be
applied to get optimal motions of robots under formation constraints in
obstacle-rich environments. Additionally, different constraints are considered
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