191 research outputs found
Adaptive Asynchronous Control Using Meta-learned Neural Ordinary Differential Equations
Model-based Reinforcement Learning and Control have demonstrated great
potential in various sequential decision making problem domains, including in
robotics settings. However, real-world robotics systems often present
challenges that limit the applicability of those methods. In particular, we
note two problems that jointly happen in many industrial systems: 1)
Irregular/asynchronous observations and actions and 2) Dramatic changes in
environment dynamics from an episode to another (e.g. varying payload inertial
properties). We propose a general framework that overcomes those difficulties
by meta-learning adaptive dynamics models for continuous-time prediction and
control. The proposed approach is task-agnostic and can be adapted to new tasks
in a straight-forward manner. We present evaluations in two different robot
simulations and on a real industrial robot.Comment: 16 double column pages, 14 figures, 3 table
Exploring New Horizons in Evolutionary Design of Robots
International audienceThis introduction paper to the 2009 IROS workshop “Exploring new horizons in Evolutionary Design of Robots” considers the field of Evolutionary Robotics (ER) from the perspective of its potential users: roboticists. The core hypothesis motivating this field of research will be discussed, as well as the potential use of ER in a robot design process. Three main aspects of ER will be presented: (a) ER as an automatic parameter tuning procedure, which is the most mature application and is used to solve real robotics problem, (b) evolutionary-aided design, which may benefit the designer as an efficient tool to build robotic systems and (c) automatic synthesis, which corresponds to the automatic design of a mechatronic device. Critical issues will also be presented as well as current trends and pespectives in ER
Behavioral Repertoire via Generative Adversarial Policy Networks
Learning algorithms are enabling robots to solve increasingly challenging
real-world tasks. These approaches often rely on demonstrations and reproduce
the behavior shown. Unexpected changes in the environment may require using
different behaviors to achieve the same effect, for instance to reach and grasp
an object in changing clutter. An emerging paradigm addressing this robustness
issue is to learn a diverse set of successful behaviors for a given task, from
which a robot can select the most suitable policy when faced with a new
environment. In this paper, we explore a novel realization of this vision by
learning a generative model over policies. Rather than learning a single
policy, or a small fixed repertoire, our generative model for policies
compactly encodes an unbounded number of policies and allows novel controller
variants to be sampled. Leveraging our generative policy network, a robot can
sample novel behaviors until it finds one that works for a new environment. We
demonstrate this idea with an application of robust ball-throwing in the
presence of obstacles. We show that this approach achieves a greater diversity
of behaviors than an existing evolutionary approach, while maintaining good
efficacy of sampled behaviors, allowing a Baxter robot to hit targets more
often when ball throwing in the presence of obstacles.Comment: In Proceedings of 2019 Joint IEEE 9th International Conference on
Development and Learning and Epigenetic Robotics (ICDL-EpiRob), pages 320 -
32
Learning to Grasp: from Somewhere to Anywhere
Robotic grasping is still a partially solved, multidisciplinary problem where
data-driven techniques play an increasing role. The sparse nature of rewards
make the automatic generation of grasping datasets challenging, especially for
unconventional morphologies or highly actuated end-effectors. Most approaches
for obtaining large-scale datasets rely on numerous human-provided
demonstrations or heavily engineered solutions that do not scale well. Recent
advances in Quality-Diversity (QD) methods have investigated how to learn
object grasping at a specific pose with different robot morphologies. The
present work introduces a pipeline for adapting QD-generated trajectories to
new object poses. Using an RGB-D data stream, the vision pipeline first detects
the targeted object, predicts its 6-DOF pose, and finally tracks it. An
automatically generated reach-and-grasp trajectory can then be adapted by
projecting it relatively to the object frame. Hundreds of trajectories have
been deployed into the real world on several objects and with different robotic
setups: a Franka Research 3 with a parallel gripper and a UR5 with a dexterous
SIH Schunk hand. The transfer ratio obtained when applying transformation to
the object pose matches the one obtained when the object pose matches the
simulation, demonstrating the efficiency of the proposed approach
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