1,867 research outputs found
Learning a Structured Neural Network Policy for a Hopping Task
In this work we present a method for learning a reactive policy for a simple
dynamic locomotion task involving hard impact and switching contacts where we
assume the contact location and contact timing to be unknown. To learn such a
policy, we use optimal control to optimize a local controller for a fixed
environment and contacts. We learn the contact-rich dynamics for our
underactuated systems along these trajectories in a sample efficient manner. We
use the optimized policies to learn the reactive policy in form of a neural
network. Using a new neural network architecture, we are able to preserve more
information from the local policy and make its output interpretable in the
sense that its output in terms of desired trajectories, feedforward commands
and gains can be interpreted. Extensive simulations demonstrate the robustness
of the approach to changing environments, outperforming a model-free gradient
policy based methods on the same tasks in simulation. Finally, we show that the
learned policy can be robustly transferred on a real robot.Comment: IEEE Robotics and Automation Letters 201
Learning Image-Conditioned Dynamics Models for Control of Under-actuated Legged Millirobots
Millirobots are a promising robotic platform for many applications due to
their small size and low manufacturing costs. Legged millirobots, in
particular, can provide increased mobility in complex environments and improved
scaling of obstacles. However, controlling these small, highly dynamic, and
underactuated legged systems is difficult. Hand-engineered controllers can
sometimes control these legged millirobots, but they have difficulties with
dynamic maneuvers and complex terrains. We present an approach for controlling
a real-world legged millirobot that is based on learned neural network models.
Using less than 17 minutes of data, our method can learn a predictive model of
the robot's dynamics that can enable effective gaits to be synthesized on the
fly for following user-specified waypoints on a given terrain. Furthermore, by
leveraging expressive, high-capacity neural network models, our approach allows
for these predictions to be directly conditioned on camera images, endowing the
robot with the ability to predict how different terrains might affect its
dynamics. This enables sample-efficient and effective learning for locomotion
of a dynamic legged millirobot on various terrains, including gravel, turf,
carpet, and styrofoam. Experiment videos can be found at
https://sites.google.com/view/imageconddy
On Neuromechanical Approaches for the Study of Biological Grasp and Manipulation
Biological and robotic grasp and manipulation are undeniably similar at the
level of mechanical task performance. However, their underlying fundamental
biological vs. engineering mechanisms are, by definition, dramatically
different and can even be antithetical. Even our approach to each is
diametrically opposite: inductive science for the study of biological systems
vs. engineering synthesis for the design and construction of robotic systems.
The past 20 years have seen several conceptual advances in both fields and the
quest to unify them. Chief among them is the reluctant recognition that their
underlying fundamental mechanisms may actually share limited common ground,
while exhibiting many fundamental differences. This recognition is particularly
liberating because it allows us to resolve and move beyond multiple paradoxes
and contradictions that arose from the initial reasonable assumption of a large
common ground. Here, we begin by introducing the perspective of neuromechanics,
which emphasizes that real-world behavior emerges from the intimate
interactions among the physical structure of the system, the mechanical
requirements of a task, the feasible neural control actions to produce it, and
the ability of the neuromuscular system to adapt through interactions with the
environment. This allows us to articulate a succinct overview of a few salient
conceptual paradoxes and contradictions regarding under-determined vs.
over-determined mechanics, under- vs. over-actuated control, prescribed vs.
emergent function, learning vs. implementation vs. adaptation, prescriptive vs.
descriptive synergies, and optimal vs. habitual performance. We conclude by
presenting open questions and suggesting directions for future research. We
hope this frank assessment of the state-of-the-art will encourage and guide
these communities to continue to interact and make progress in these important
areas
Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control
Trial-and-error based reinforcement learning (RL) has seen rapid advancements
in recent times, especially with the advent of deep neural networks. However,
the majority of autonomous RL algorithms require a large number of interactions
with the environment. A large number of interactions may be impractical in many
real-world applications, such as robotics, and many practical systems have to
obey limitations in the form of state space or control constraints. To reduce
the number of system interactions while simultaneously handling constraints, we
propose a model-based RL framework based on probabilistic Model Predictive
Control (MPC). In particular, we propose to learn a probabilistic transition
model using Gaussian Processes (GPs) to incorporate model uncertainty into
long-term predictions, thereby, reducing the impact of model errors. We then
use MPC to find a control sequence that minimises the expected long-term cost.
We provide theoretical guarantees for first-order optimality in the GP-based
transition models with deterministic approximate inference for long-term
planning. We demonstrate that our approach does not only achieve
state-of-the-art data efficiency, but also is a principled way for RL in
constrained environments.Comment: Accepted at AISTATS 2018
Constructing Parsimonious Analytic Models for Dynamic Systems via Symbolic Regression
Developing mathematical models of dynamic systems is central to many
disciplines of engineering and science. Models facilitate simulations, analysis
of the system's behavior, decision making and design of automatic control
algorithms. Even inherently model-free control techniques such as reinforcement
learning (RL) have been shown to benefit from the use of models, typically
learned online. Any model construction method must address the tradeoff between
the accuracy of the model and its complexity, which is difficult to strike. In
this paper, we propose to employ symbolic regression (SR) to construct
parsimonious process models described by analytic equations. We have equipped
our method with two different state-of-the-art SR algorithms which
automatically search for equations that fit the measured data: Single Node
Genetic Programming (SNGP) and Multi-Gene Genetic Programming (MGGP). In
addition to the standard problem formulation in the state-space domain, we show
how the method can also be applied to input-output models of the NARX
(nonlinear autoregressive with exogenous input) type. We present the approach
on three simulated examples with up to 14-dimensional state space: an inverted
pendulum, a mobile robot, and a bipedal walking robot. A comparison with deep
neural networks and local linear regression shows that SR in most cases
outperforms these commonly used alternative methods. We demonstrate on a real
pendulum system that the analytic model found enables a RL controller to
successfully perform the swing-up task, based on a model constructed from only
100 data samples
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