74 research outputs found
Ensemble control of Hamiltonian systems
This thesis aims to demonstrate the conditions under which a particular kind of ensemble control system is controllable. Previous studies on this topic have tended to make simplifying assumptions on the systems in view, and this paper attempts to remove some of those assumptions. Even in a non-linear ensemble system, it can be demonstrated that controllability is achievable under some circumstances. This controllability is limited, however, by the Hamiltonian preservation of area in the position-velocity plane. At the end of this thesis, some example systems will be simulated, demonstrating the concepts of the paper and showing that the controllability result is sound
Combining Physical Simulators and Object-Based Networks for Control
Physics engines play an important role in robot planning and control;
however, many real-world control problems involve complex contact dynamics that
cannot be characterized analytically. Most physics engines therefore employ .
approximations that lead to a loss in precision. In this paper, we propose a
hybrid dynamics model, simulator-augmented interaction networks (SAIN),
combining a physics engine with an object-based neural network for dynamics
modeling. Compared with existing models that are purely analytical or purely
data-driven, our hybrid model captures the dynamics of interacting objects in a
more accurate and data-efficient manner.Experiments both in simulation and on a
real robot suggest that it also leads to better performance when used in
complex control tasks. Finally, we show that our model generalizes to novel
environments with varying object shapes and materials.Comment: ICRA 2019; Project page: http://sain.csail.mit.ed
Crowdsourcing Swarm Manipulation Experiments: A Massive Online User Study with Large Swarms of Simple Robots
Micro- and nanorobotics have the potential to revolutionize many applications
including targeted material delivery, assembly, and surgery. The same
properties that promise breakthrough solutions---small size and large
populations---present unique challenges to generating controlled motion. We
want to use large swarms of robots to perform manipulation tasks;
unfortunately, human-swarm interaction studies as conducted today are limited
in sample size, are difficult to reproduce, and are prone to hardware failures.
We present an alternative.
This paper examines the perils, pitfalls, and possibilities we discovered by
launching SwarmControl.net, an online game where players steer swarms of up to
500 robots to complete manipulation challenges. We record statistics from
thousands of players, and use the game to explore aspects of large-population
robot control. We present the game framework as a new, open-source tool for
large-scale user experiments. Our results have potential applications in human
control of micro- and nanorobots, supply insight for automatic controllers, and
provide a template for large online robotic research experiments.Comment: 8 pages, 13 figures, to appear at 2014 IEEE International Conference
on Robotics and Automation (ICRA 2014
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