Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userSimulation leagues: Enabling replicable and robust investigation of complex robotic systems
287
0
0.0
(
0
)
Added by
David Budden
Publication date
2014
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Physically-realistic simulated environments are powerful platforms for enabling measurable, replicable and statistically-robust investigation of complex robotic systems. Such environments are epitomised by the RoboCup simulation leagues, which have been successfully utilised to conduct massively-parallel experiments in topics including: optimisation of bipedal locomotion, self-localisation from noisy perception data and planning complex multi-agent strategies without direct agent-to-agent communication. Many of these systems are later transferred to physical robots, making the simulation leagues invaluable well-beyond the scope of simulated soccer matches. In this study, we provide an overview of the RoboCup simulation leagues and describe their properties as they pertain to replicable and robust robotics research. To demonstrate their utility directly, we leverage the ability to run parallelised experiments to evaluate different competition formats (e.g. round robin) for the RoboCup 2D simulation league. Our results demonstrate that a previously-proposed hybrid format minimises fluctuations from true (statistically-significant) team performance rankings within the time constraints of the RoboCup world finals. Our experimental analysis would be impossible with physical robots alone, and we encourage other researchers to explore the potential for enriching their experimental pipelines with simulated components, both to minimise experimental costsand enable others to replicate and expand upon their results in a hardware-independent manner.
rate research
Read More
In contrast to manned missions, the application of autonomous robots for space exploration missions decreases the safety concerns of the exploration missions while extending the exploration distance since returning transportation is not necessary for robotics missions. In addition, the employment of robots in these missions also decreases mission complexities and costs because there is no need for onboard life support systems: robots can withstand and operate in harsh conditions, for instance, extreme temperature, pressure, and radiation, where humans cannot survive. In this article, we introduce environments on Mars, review the existing autonomous driving techniques deployed on Earth, as well as explore technologies required to enable future commercial autonomous space robotic explorers. Last but not least, we also present that one of the urgent technical challenges for autonomous space explorers, namely, computing power onboard.
We present ConFusion, an open-source package for online sensor fusion for robotic applications. ConFusion is a modular framework for fusing measurements from many heterogeneous sensors within a moving horizon estimator. ConFusion offers greater flexibility in sensor fusion problem design than filtering-based systems and the ability to scale the online estimate quality with the available computing power. We demonstrate its performance in comparison to an iterated extended Kalman filter in visual-inertial tracking, and show its versatility through whole-body sensor fusion on a mobile manipulator.
Robust and accurate estimation of liquid height lies as an essential part of pouring tasks for service robots. However, vision-based methods often fail in occluded conditions while audio-based methods cannot work well in a noisy environment. We instead propose a multimodal pouring network (MP-Net) that is able to robustly predict liquid height by conditioning on both audition and haptics input. MP-Net is trained on a self-collected multimodal pouring dataset. This dataset contains 300 robot pouring recordings with audio and force/torque measurements for three types of target containers. We also augment the audio data by inserting robot noise. We evaluated MP-Net on our collected dataset and a wide variety of robot experiments. Both network training results and robot experiments demonstrate that MP-Net is robust against noise and changes to the task and environment. Moreover, we further combine the predicted height and force data to estimate the shape of the target container.
The selection of an appropriate competition format is critical for both the success and credibility of any competition, both real and simulated. In this paper, the automated parallelism offered by the RoboCupSoccer 2D simulation league is leveraged to conduct a 28,000 game round-robin between the top 8 teams from RoboCup 2012 and 2013. A proposed new competition format is found to reduce variation from the resultant statistically significant team performance rankings by 75% and 67%, when compared to the actual competition results from RoboCup 2012 and 2013 respectively. These results are statistically validated by generating 10,000 random tournaments for each of the three considered formats and comparing the respective distributions of ranking discrepancy.
Despite the success of reinforcement learning methods, they have yet to have their breakthrough moment when applied to a broad range of robotic manipulation tasks. This is partly due to the fact that reinforcement learning algorithms are notoriously difficult and time consuming to train, which is exacerbated when training from images rather than full-state inputs. As humans perform manipulation tasks, our eyes closely monitor every step of the process with our gaze focusing sequentially on the objects being manipulated. With this in mind, we present our Attention-driven Robotic Manipulation (ARM) algorithm, which is a general manipulation algorithm that can be applied to a range of sparse-rewarded tasks, given only a small number of demonstrations. ARM splits the complex task of manipulation into a 3 stage pipeline: (1) a Q-attention agent extracts interesting pixel locations from RGB and point cloud inputs, (2) a next-best pose agent that accepts crops from the Q-attention agent and outputs poses, and (3) a control agent that takes the goal pose and outputs joint actions. We show that current learning algorithms fail on a range of RLBench tasks, whilst ARM is successful.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
