136 research outputs found
Learning Actions and Control of Focus of Attention with a Log-Polar-like Sensor
With the long-term goal of reducing the image processing time on an
autonomous mobile robot in mind we explore in this paper the use of log-polar
like image data with gaze control. The gaze control is not done on the
Cartesian image but on the log-polar like image data. For this we start out
from the classic deep reinforcement learning approach for Atari games. We
extend an A3C deep RL approach with an LSTM network, and we learn the policy
for playing three Atari games and a policy for gaze control. While the Atari
games already use low-resolution images of 80 by 80 pixels, we are able to
further reduce the amount of image pixels by a factor of 5 without losing any
gaming performance
Learning to Adapt the Parameters of Behavior Trees and Motion Generators (BTMGs) to Task Variations
The ability to learn new tasks and quickly adapt to different variations or
dimensions is an important attribute in agile robotics. In our previous work,
we have explored Behavior Trees and Motion Generators (BTMGs) as a robot arm
policy representation to facilitate the learning and execution of assembly
tasks. The current implementation of the BTMGs for a specific task may not be
robust to the changes in the environment and may not generalize well to
different variations of tasks. We propose to extend the BTMG policy
representation with a module that predicts BTMG parameters for a new task
variation. To achieve this, we propose a model that combines a Gaussian process
and a weighted support vector machine classifier. This model predicts the
performance measure and the feasibility of the predicted policy with BTMG
parameters and task variations as inputs. Using the outputs of the model, we
then construct a surrogate reward function that is utilized within an optimizer
to maximize the performance of a task over BTMG parameters for a fixed task
variation. To demonstrate the effectiveness of our proposed approach, we
conducted experimental evaluations on push and obstacle avoidance tasks in
simulation and with a real KUKA iiwa robot. Furthermore, we compared the
performance of our approach with four baseline methods
Continuous close-range 3D object pose estimation
In the context of future manufacturing lines, removing fixtures will be a
fundamental step to increase the flexibility of autonomous systems in assembly
and logistic operations. Vision-based 3D pose estimation is a necessity to
accurately handle objects that might not be placed at fixed positions during
the robot task execution. Industrial tasks bring multiple challenges for the
robust pose estimation of objects such as difficult object properties, tight
cycle times and constraints on camera views. In particular, when interacting
with objects, we have to work with close-range partial views of objects that
pose a new challenge for typical view-based pose estimation methods. In this
paper, we present a 3D pose estimation method based on a gradient-ascend
particle filter that integrates new observations on-the-fly to improve the pose
estimate. Thereby, we can apply this method online during task execution to
save valuable cycle time. In contrast to other view-based pose estimation
methods, we model potential views in full 6- dimensional space that allows us
to cope with close-range partial objects views. We demonstrate the approach on
a real assembly task, in which the algorithm usually converges to the correct
pose within 10-15 iterations with an average accuracy of less than 8mm
SkiROS2: A skill-based Robot Control Platform for ROS
The need for autonomous robot systems in both the service and the industrial
domain is larger than ever. In the latter, the transition to small batches or
even "batch size 1" in production created a need for robot control system
architectures that can provide the required flexibility. Such architectures
must not only have a sufficient knowledge integration framework. It must also
support autonomous mission execution and allow for interchangeability and
interoperability between different tasks and robot systems. We introduce
SkiROS2, a skill-based robot control platform on top of ROS. SkiROS2 proposes a
layered, hybrid control structure for automated task planning, and reactive
execution, supported by a knowledge base for reasoning about the world state
and entities. The scheduling formulation builds on the extended behavior tree
model that merges task-level planning and execution. This allows for a high
degree of modularity and a fast reaction to changes in the environment. The
skill formulation based on pre-, hold- and post-conditions allows to organize
robot programs and to compose diverse skills reaching from perception to
low-level control and the incorporation of external tools. We relate SkiROS2 to
the field and outline three example use cases that cover task planning,
reasoning, multisensory input, integration in a manufacturing execution system
and reinforcement learning.Comment: 8 pages, 3 figures. Accepted at 2023 IEEE/RSJ International
Conference on Intelligent Robots and Systems (IROS
Introduction to the Minitrack on Transforming Traditional Production Systems into Smart Production Systems
Realeasy: Real-Time capable Simulation to Reality Domain Adaptation
We address the problem of insufficient quality of robot simulators to produce precise sensor readings for joint positions, velocities and torques. Realistic simulations of sensor readings are particularly important for real time robot control laws and for data intensive Reinforcement Learning of robot movements in simulation. We systematically construct two architectures based on Long Short-Term Memory to model the difference between simulated and real sensor readings for online and offline application. Our solution is easy to integrate into existing Robot Operating System frameworks and its formulation is neither robot nor task specific. The collected data set, the plug-and-play Realeasy model for the Panda robot and a reproducible real-time docker setup are shared alongside the code. We demonstrate robust behavior and transferability of the learned model between individual Franka Emika Panda robots. Our experiments show a reduction in torque mean squared error of at least one order of magnitude
Learning of Parameters in Behavior Trees for Movement Skills
Reinforcement Learning (RL) is a powerful mathematical framework that allows
robots to learn complex skills by trial-and-error. Despite numerous successes
in many applications, RL algorithms still require thousands of trials to
converge to high-performing policies, can produce dangerous behaviors while
learning, and the optimized policies (usually modeled as neural networks) give
almost zero explanation when they fail to perform the task. For these reasons,
the adoption of RL in industrial settings is not common. Behavior Trees (BTs),
on the other hand, can provide a policy representation that a) supports modular
and composable skills, b) allows for easy interpretation of the robot actions,
and c) provides an advantageous low-dimensional parameter space. In this paper,
we present a novel algorithm that can learn the parameters of a BT policy in
simulation and then generalize to the physical robot without any additional
training. We leverage a physical simulator with a digital twin of our
workstation, and optimize the relevant parameters with a black-box optimizer.
We showcase the efficacy of our method with a 7-DOF KUKA-iiwa manipulator in a
task that includes obstacle avoidance and a contact-rich insertion
(peg-in-hole), in which our method outperforms the baselines.Comment: 8 pages, 5 figures, accepted at 2021 IEEE/RSJ International
Conference on Intelligent Robots and Systems (IROS
Learning Skill-based Industrial Robot Tasks with User Priors
Robot skills systems are meant to reduce robot setup time for new
manufacturing tasks. Yet, for dexterous, contact-rich tasks, it is often
difficult to find the right skill parameters. One strategy is to learn these
parameters by allowing the robot system to learn directly on the task. For a
learning problem, a robot operator can typically specify the type and range of
values of the parameters. Nevertheless, given their prior experience, robot
operators should be able to help the learning process further by providing
educated guesses about where in the parameter space potential optimal solutions
could be found. Interestingly, such prior knowledge is not exploited in current
robot learning frameworks. We introduce an approach that combines user priors
and Bayesian optimization to allow fast optimization of robot industrial tasks
at robot deployment time. We evaluate our method on three tasks that are
learned in simulation as well as on two tasks that are learned directly on a
real robot system. Additionally, we transfer knowledge from the corresponding
simulation tasks by automatically constructing priors from well-performing
configurations for learning on the real system. To handle potentially
contradicting task objectives, the tasks are modeled as multi-objective
problems. Our results show that operator priors, both user-specified and
transferred, vastly accelerate the discovery of rich Pareto fronts, and
typically produce final performance far superior to proposed baselines.Comment: 8 pages, 6 figures, accepted at 2022 IEEE International Conference on
Automation Science and Engineering (CASE
A Shared Pose Regression Network for Pose Estimation of Objects from RGB Images
In this paper we propose a shared regression network to jointly estimate the pose of multiple objects, replacing multiple object-specific solutions. We demonstrate that this shared network can outperform other similar approaches that rely on multiple object-specific models by evaluating it on the TLESS dataset using the VSD (Visible Surface Discrepancy). Our approach offers a less complex solution, with fewer parameters, lower memory consumption and less training required. Furthermore, it inherently handles symmetric objects by using a depth-based loss during training and can predict in real-time. Finally, we show how our proposed pipeline can be used for fine-tuning a feature extractor jointly on all objects while training the shared pose regression network. This fine-tuning process improves the pose estimation performance
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