24 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 Contact-Rich Manipulation Skills with Guided Policy Search
Autonomous learning of object manipulation skills can enable robots to
acquire rich behavioral repertoires that scale to the variety of objects found
in the real world. However, current motion skill learning methods typically
restrict the behavior to a compact, low-dimensional representation, limiting
its expressiveness and generality. In this paper, we extend a recently
developed policy search method \cite{la-lnnpg-14} and use it to learn a range
of dynamic manipulation behaviors with highly general policy representations,
without using known models or example demonstrations. Our approach learns a set
of trajectories for the desired motion skill by using iteratively refitted
time-varying linear models, and then unifies these trajectories into a single
control policy that can generalize to new situations. To enable this method to
run on a real robot, we introduce several improvements that reduce the sample
count and automate parameter selection. We show that our method can acquire
fast, fluent behaviors after only minutes of interaction time, and can learn
robust controllers for complex tasks, including putting together a toy
airplane, stacking tight-fitting lego blocks, placing wooden rings onto
tight-fitting pegs, inserting a shoe tree into a shoe, and screwing bottle caps
onto bottles
Online Trajectory Planning Through Combined Trajectory Optimization and Function Approximation: Application to the Exoskeleton Atalante
Autonomous robots require online trajectory planning capability to operate in
the real world. Efficient offline trajectory planning methods already exist,
but are computationally demanding, preventing their use online. In this paper,
we present a novel algorithm called Guided Trajectory Learning that learns a
function approximation of solutions computed through trajectory optimization
while ensuring accurate and reliable predictions. This function approximation
is then used online to generate trajectories. This algorithm is designed to be
easy to implement, and practical since it does not require massive computing
power. It is readily applicable to any robotics systems and effortless to set
up on real hardware since robust control strategies are usually already
available. We demonstrate the computational performance of our algorithm on
flat-foot walking with the self-balanced exoskeleton Atalante