2,041 research outputs found
Reset-free Trial-and-Error Learning for Robot Damage Recovery
The high probability of hardware failures prevents many advanced robots
(e.g., legged robots) from being confidently deployed in real-world situations
(e.g., post-disaster rescue). Instead of attempting to diagnose the failures,
robots could adapt by trial-and-error in order to be able to complete their
tasks. In this situation, damage recovery can be seen as a Reinforcement
Learning (RL) problem. However, the best RL algorithms for robotics require the
robot and the environment to be reset to an initial state after each episode,
that is, the robot is not learning autonomously. In addition, most of the RL
methods for robotics do not scale well with complex robots (e.g., walking
robots) and either cannot be used at all or take too long to converge to a
solution (e.g., hours of learning). In this paper, we introduce a novel
learning algorithm called "Reset-free Trial-and-Error" (RTE) that (1) breaks
the complexity by pre-generating hundreds of possible behaviors with a dynamics
simulator of the intact robot, and (2) allows complex robots to quickly recover
from damage while completing their tasks and taking the environment into
account. We evaluate our algorithm on a simulated wheeled robot, a simulated
six-legged robot, and a real six-legged walking robot that are damaged in
several ways (e.g., a missing leg, a shortened leg, faulty motor, etc.) and
whose objective is to reach a sequence of targets in an arena. Our experiments
show that the robots can recover most of their locomotion abilities in an
environment with obstacles, and without any human intervention.Comment: 18 pages, 16 figures, 3 tables, 6 pseudocodes/algorithms, video at
https://youtu.be/IqtyHFrb3BU, code at
https://github.com/resibots/chatzilygeroudis_2018_rt
More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch
For humans, the process of grasping an object relies heavily on rich tactile
feedback. Most recent robotic grasping work, however, has been based only on
visual input, and thus cannot easily benefit from feedback after initiating
contact. In this paper, we investigate how a robot can learn to use tactile
information to iteratively and efficiently adjust its grasp. To this end, we
propose an end-to-end action-conditional model that learns regrasping policies
from raw visuo-tactile data. This model -- a deep, multimodal convolutional
network -- predicts the outcome of a candidate grasp adjustment, and then
executes a grasp by iteratively selecting the most promising actions. Our
approach requires neither calibration of the tactile sensors, nor any
analytical modeling of contact forces, thus reducing the engineering effort
required to obtain efficient grasping policies. We train our model with data
from about 6,450 grasping trials on a two-finger gripper equipped with GelSight
high-resolution tactile sensors on each finger. Across extensive experiments,
our approach outperforms a variety of baselines at (i) estimating grasp
adjustment outcomes, (ii) selecting efficient grasp adjustments for quick
grasping, and (iii) reducing the amount of force applied at the fingers, while
maintaining competitive performance. Finally, we study the choices made by our
model and show that it has successfully acquired useful and interpretable
grasping behaviors.Comment: 8 pages. Published on IEEE Robotics and Automation Letters (RAL).
Website: https://sites.google.com/view/more-than-a-feelin
Comparative evaluation of approaches in T.4.1-4.3 and working definition of adaptive module
The goal of this deliverable is two-fold: (1) to present and compare different approaches towards learning and encoding movements us- ing dynamical systems that have been developed by the AMARSi partners (in the past during the first 6 months of the project), and (2) to analyze their suitability to be used as adaptive modules, i.e. as building blocks for the complete architecture that will be devel- oped in the project. The document presents a total of eight approaches, in two groups: modules for discrete movements (i.e. with a clear goal where the movement stops) and for rhythmic movements (i.e. which exhibit periodicity). The basic formulation of each approach is presented together with some illustrative simulation results. Key character- istics such as the type of dynamical behavior, learning algorithm, generalization properties, stability analysis are then discussed for each approach. We then make a comparative analysis of the different approaches by comparing these characteristics and discussing their suitability for the AMARSi project
Machine Learning Meets Advanced Robotic Manipulation
Automated industries lead to high quality production, lower manufacturing
cost and better utilization of human resources. Robotic manipulator arms have
major role in the automation process. However, for complex manipulation tasks,
hard coding efficient and safe trajectories is challenging and time consuming.
Machine learning methods have the potential to learn such controllers based on
expert demonstrations. Despite promising advances, better approaches must be
developed to improve safety, reliability, and efficiency of ML methods in both
training and deployment phases. This survey aims to review cutting edge
technologies and recent trends on ML methods applied to real-world manipulation
tasks. After reviewing the related background on ML, the rest of the paper is
devoted to ML applications in different domains such as industry, healthcare,
agriculture, space, military, and search and rescue. The paper is closed with
important research directions for future works
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