764 research outputs found
Online Visual Robot Tracking and Identification using Deep LSTM Networks
Collaborative robots working on a common task are necessary for many
applications. One of the challenges for achieving collaboration in a team of
robots is mutual tracking and identification. We present a novel pipeline for
online visionbased detection, tracking and identification of robots with a
known and identical appearance. Our method runs in realtime on the limited
hardware of the observer robot. Unlike previous works addressing robot tracking
and identification, we use a data-driven approach based on recurrent neural
networks to learn relations between sequential inputs and outputs. We formulate
the data association problem as multiple classification problems. A deep LSTM
network was trained on a simulated dataset and fine-tuned on small set of real
data. Experiments on two challenging datasets, one synthetic and one real,
which include long-term occlusions, show promising results.Comment: IEEE/RSJ International Conference on Intelligent Robots and Systems
(IROS), Vancouver, Canada, 2017. IROS RoboCup Best Paper Awar
Using Monte Carlo Search With Data Aggregation to Improve Robot Soccer Policies
RoboCup soccer competitions are considered among the most challenging
multi-robot adversarial environments, due to their high dynamism and the
partial observability of the environment. In this paper we introduce a method
based on a combination of Monte Carlo search and data aggregation (MCSDA) to
adapt discrete-action soccer policies for a defender robot to the strategy of
the opponent team. By exploiting a simple representation of the domain, a
supervised learning algorithm is trained over an initial collection of data
consisting of several simulations of human expert policies. Monte Carlo policy
rollouts are then generated and aggregated to previous data to improve the
learned policy over multiple epochs and games. The proposed approach has been
extensively tested both on a soccer-dedicated simulator and on real robots.
Using this method, our learning robot soccer team achieves an improvement in
ball interceptions, as well as a reduction in the number of opponents' goals.
Together with a better performance, an overall more efficient positioning of
the whole team within the field is achieved
FC Portugal 3D Simulation Team: Team Description Paper 2020
The FC Portugal 3D team is developed upon the structure of our previous
Simulation league 2D/3D teams and our standard platform league team. Our
research concerning the robot low-level skills is focused on developing
behaviors that may be applied on real robots with minimal adaptation using
model-based approaches. Our research on high-level soccer coordination
methodologies and team playing is mainly focused on the adaptation of
previously developed methodologies from our 2D soccer teams to the 3D humanoid
environment and on creating new coordination methodologies based on the
previously developed ones. The research-oriented development of our team has
been pushing it to be one of the most competitive over the years (World
champion in 2000 and Coach Champion in 2002, European champion in 2000 and
2001, Coach 2nd place in 2003 and 2004, European champion in Rescue Simulation
and Simulation 3D in 2006, World Champion in Simulation 3D in Bremen 2006 and
European champion in 2007, 2012, 2013, 2014 and 2015). This paper describes
some of the main innovations of our 3D simulation league team during the last
years. A new generic framework for reinforcement learning tasks has also been
developed. The current research is focused on improving the above-mentioned
framework by developing new learning algorithms to optimize low-level skills,
such as running and sprinting. We are also trying to increase student contact
by providing reinforcement learning assignments to be completed using our new
framework, which exposes a simple interface without sharing low-level
implementation details
Multi-Agent Coordination for a Partially Observable and Dynamic Robot Soccer Environment with Limited Communication
RoboCup represents an International testbed for advancing research in AI and
robotics, focusing on a definite goal: developing a robot team that can win
against the human world soccer champion team by the year 2050. To achieve this
goal, autonomous humanoid robots' coordination is crucial. This paper explores
novel solutions within the RoboCup Standard Platform League (SPL), where a
reduction in WiFi communication is imperative, leading to the development of
new coordination paradigms. The SPL has experienced a substantial decrease in
network packet rate, compelling the need for advanced coordination
architectures to maintain optimal team functionality in dynamic environments.
Inspired by market-based task assignment, we introduce a novel distributed
coordination system to orchestrate autonomous robots' actions efficiently in
low communication scenarios. This approach has been tested with NAO robots
during official RoboCup competitions and in the SimRobot simulator,
demonstrating a notable reduction in task overlaps in limited communication
settings.Comment: International Conference of the Italian Association for Artificial
Intelligence (AIxIA 2023) - Italian Workshop on Artificial Intelligence and
Robotics (AIRO) Rome, 6 - 9 November, 202
Toward Real-Time Decentralized Reinforcement Learning using Finite Support Basis Functions
This paper addresses the design and implementation of complex Reinforcement
Learning (RL) behaviors where multi-dimensional action spaces are involved, as
well as the need to execute the behaviors in real-time using robotic platforms
with limited computational resources and training times. For this purpose, we
propose the use of decentralized RL, in combination with finite support basis
functions as alternatives to Gaussian RBF, in order to alleviate the effects of
the curse of dimensionality on the action and state spaces respectively, and to
reduce the computation time. As testbed, a RL based controller for the in-walk
kick in NAO robots, a challenging and critical problem for soccer robotics, is
used. The reported experiments show empirically that our solution saves up to
99.94% of execution time and 98.82% of memory consumption during execution,
without diminishing performance compared to classical approaches.Comment: Accepted in the RoboCup Symposium 2017. Final version will be
published at Springe
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