15,481 research outputs found
Bayesian Optimization for Developmental Robotics with Meta-Learning by Parameters Bounds Reduction
In robotics, methods and softwares usually require optimizations of
hyperparameters in order to be efficient for specific tasks, for instance
industrial bin-picking from homogeneous heaps of different objects. We present
a developmental framework based on long-term memory and reasoning modules
(Bayesian Optimisation, visual similarity and parameters bounds reduction)
allowing a robot to use meta-learning mechanism increasing the efficiency of
such continuous and constrained parameters optimizations. The new optimization,
viewed as a learning for the robot, can take advantage of past experiences
(stored in the episodic and procedural memories) to shrink the search space by
using reduced parameters bounds computed from the best optimizations realized
by the robot with similar tasks of the new one (e.g. bin-picking from an
homogenous heap of a similar object, based on visual similarity of objects
stored in the semantic memory). As example, we have confronted the system to
the constrained optimizations of 9 continuous hyperparameters for a
professional software (Kamido) in industrial robotic arm bin-picking tasks, a
step that is needed each time to handle correctly new object. We used a
simulator to create bin-picking tasks for 8 different objects (7 in simulation
and one with real setup, without and with meta-learning with experiences coming
from other similar objects) achieving goods results despite a very small
optimization budget, with a better performance reached when meta-learning is
used (84.3% vs 78.9% of success overall, with a small budget of 30 iterations
for each optimization) for every object tested (p-value=0.036).Comment: Accepted at the IEEE International Conference on Development and
Learning and Epigenetic Robotics 2020 (ICDL-Epirob 2020
Analysis and Observations from the First Amazon Picking Challenge
This paper presents a overview of the inaugural Amazon Picking Challenge
along with a summary of a survey conducted among the 26 participating teams.
The challenge goal was to design an autonomous robot to pick items from a
warehouse shelf. This task is currently performed by human workers, and there
is hope that robots can someday help increase efficiency and throughput while
lowering cost. We report on a 28-question survey posed to the teams to learn
about each team's background, mechanism design, perception apparatus, planning
and control approach. We identify trends in this data, correlate it with each
team's success in the competition, and discuss observations and lessons learned
based on survey results and the authors' personal experiences during the
challenge
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