4,906 research outputs found
DoorGym: A Scalable Door Opening Environment And Baseline Agent
In order to practically implement the door opening task, a policy ought to be
robust to a wide distribution of door types and environment settings.
Reinforcement Learning (RL) with Domain Randomization (DR) is a promising
technique to enforce policy generalization, however, there are only a few
accessible training environments that are inherently designed to train agents
in domain randomized environments. We introduce DoorGym, an open-source door
opening simulation framework designed to utilize domain randomization to train
a stable policy. We intend for our environment to lie at the intersection of
domain transfer, practical tasks, and realism. We also provide baseline
Proximal Policy Optimization and Soft Actor-Critic implementations, which
achieves success rates between 0% up to 95% for opening various types of doors
in this environment. Moreover, the real-world transfer experiment shows the
trained policy is able to work in the real world. Environment kit available
here: https://github.com/PSVL/DoorGym/Comment: Full version (Real world transfer experiments result
Collective Robot Reinforcement Learning with Distributed Asynchronous Guided Policy Search
In principle, reinforcement learning and policy search methods can enable
robots to learn highly complex and general skills that may allow them to
function amid the complexity and diversity of the real world. However, training
a policy that generalizes well across a wide range of real-world conditions
requires far greater quantity and diversity of experience than is practical to
collect with a single robot. Fortunately, it is possible for multiple robots to
share their experience with one another, and thereby, learn a policy
collectively. In this work, we explore distributed and asynchronous policy
learning as a means to achieve generalization and improved training times on
challenging, real-world manipulation tasks. We propose a distributed and
asynchronous version of Guided Policy Search and use it to demonstrate
collective policy learning on a vision-based door opening task using four
robots. We show that it achieves better generalization, utilization, and
training times than the single robot alternative.Comment: Submitted to the IEEE International Conference on Robotics and
Automation 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
Deep Visual Foresight for Planning Robot Motion
A key challenge in scaling up robot learning to many skills and environments
is removing the need for human supervision, so that robots can collect their
own data and improve their own performance without being limited by the cost of
requesting human feedback. Model-based reinforcement learning holds the promise
of enabling an agent to learn to predict the effects of its actions, which
could provide flexible predictive models for a wide range of tasks and
environments, without detailed human supervision. We develop a method for
combining deep action-conditioned video prediction models with model-predictive
control that uses entirely unlabeled training data. Our approach does not
require a calibrated camera, an instrumented training set-up, nor precise
sensing and actuation. Our results show that our method enables a real robot to
perform nonprehensile manipulation -- pushing objects -- and can handle novel
objects not seen during training.Comment: ICRA 2017. Supplementary video:
https://sites.google.com/site/robotforesight
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