11,289 research outputs found
Real-Time Online Re-Planning for Grasping Under Clutter and Uncertainty
We consider the problem of grasping in clutter. While there have been motion
planners developed to address this problem in recent years, these planners are
mostly tailored for open-loop execution. Open-loop execution in this domain,
however, is likely to fail, since it is not possible to model the dynamics of
the multi-body multi-contact physical system with enough accuracy, neither is
it reasonable to expect robots to know the exact physical properties of
objects, such as frictional, inertial, and geometrical. Therefore, we propose
an online re-planning approach for grasping through clutter. The main challenge
is the long planning times this domain requires, which makes fast re-planning
and fluent execution difficult to realize. In order to address this, we propose
an easily parallelizable stochastic trajectory optimization based algorithm
that generates a sequence of optimal controls. We show that by running this
optimizer only for a small number of iterations, it is possible to perform real
time re-planning cycles to achieve reactive manipulation under clutter and
uncertainty.Comment: Published as a conference paper in IEEE Humanoids 201
From virtual demonstration to real-world manipulation using LSTM and MDN
Robots assisting the disabled or elderly must perform complex manipulation
tasks and must adapt to the home environment and preferences of their user.
Learning from demonstration is a promising choice, that would allow the
non-technical user to teach the robot different tasks. However, collecting
demonstrations in the home environment of a disabled user is time consuming,
disruptive to the comfort of the user, and presents safety challenges. It would
be desirable to perform the demonstrations in a virtual environment. In this
paper we describe a solution to the challenging problem of behavior transfer
from virtual demonstration to a physical robot. The virtual demonstrations are
used to train a deep neural network based controller, which is using a Long
Short Term Memory (LSTM) recurrent neural network to generate trajectories. The
training process uses a Mixture Density Network (MDN) to calculate an error
signal suitable for the multimodal nature of demonstrations. The controller
learned in the virtual environment is transferred to a physical robot (a
Rethink Robotics Baxter). An off-the-shelf vision component is used to
substitute for geometric knowledge available in the simulation and an inverse
kinematics module is used to allow the Baxter to enact the trajectory. Our
experimental studies validate the three contributions of the paper: (1) the
controller learned from virtual demonstrations can be used to successfully
perform the manipulation tasks on a physical robot, (2) the LSTM+MDN
architectural choice outperforms other choices, such as the use of feedforward
networks and mean-squared error based training signals and (3) allowing
imperfect demonstrations in the training set also allows the controller to
learn how to correct its manipulation mistakes
Body randomization reduces the sim-to-real gap for compliant quadruped locomotion
Designing controllers for compliant, underactuated robots is challenging and usually requires a learning procedure. Learning robotic control in simulated environments can speed up the process whilst lowering risk of physical damage. Since perfect simulations are unfeasible, several techniques are used to improve transfer to the real world. Here, we investigate the impact of randomizing body parameters during learning of CPG controllers in simulation. The controllers are evaluated on our physical quadruped robot. We find that body randomization in simulation increases chances of finding gaits that function well on the real robot
Gym-Ignition: Reproducible Robotic Simulations for Reinforcement Learning
This paper presents Gym-Ignition, a new framework to create reproducible
robotic environments for reinforcement learning research. It interfaces with
the new generation of Gazebo, part of the Ignition Robotics suite, which
provides three main improvements for reinforcement learning applications
compared to the alternatives: 1) the modular architecture enables using the
simulator as a C++ library, simplifying the interconnection with external
software; 2) multiple physics and rendering engines are supported as plugins,
simplifying their selection during the execution; 3) the new distributed
simulation capability allows simulating complex scenarios while sharing the
load on multiple workers and machines. The core of Gym-Ignition is a component
that contains the Ignition Gazebo simulator and exposes a simple interface for
its configuration and execution. We provide a Python package that allows
developers to create robotic environments simulated in Ignition Gazebo.
Environments expose the common OpenAI Gym interface, making them compatible
out-of-the-box with third-party frameworks containing reinforcement learning
algorithms. Simulations can be executed in both headless and GUI mode, the
physics engine can run in accelerated mode, and instances can be parallelized.
Furthermore, the Gym-Ignition software architecture provides abstraction of the
Robot and the Task, making environments agnostic on the specific runtime. This
abstraction allows their execution also in a real-time setting on actual
robotic platforms, even if driven by different middlewares.Comment: Accepted in SII202
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