Modular open robots simulation engine: MORSE

Echeverria, Gilberto; Lassabe, Nicolas; Degroote, Arnaud; Lemaignan, Séverin

doi:10.1109/icra.2011.5980252

Cited by 185 publications

(97 citation statements)

References 11 publications

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“…For overall simulation of a sophisticated platform we employ MORSE [8,9], a versatile simulator for complex robot interaction scenarios, to simulate a Willow Garage PR2 robot in a kitchen environment shown in Figure 1. We chose MORSE because it uses the Bullet physics engine and because it provided a realistic household environment and a fully modeled state-of-the art robot.…”

Section: Methodsmentioning

confidence: 99%

“…Second, we show how the general idea of exploiting abstract common sense rules to solve everyday tasks-which is a basic motivation of the qualitative reasoning community (see [3])-can be implemented in context learning manipulation tasks using QSL. Both characteristics are demonstrated by empirical evaluation in simulations, ranging from pure physical computations with the Bullet physics engine (http://bulletphysics.org) to a realistic simulation of a PR2 robot in a household environment (Figures 1 and 16) with the MORSE (https://www.openrobots.org) [8,9] simulator.…”

Section: Approach and Contributionmentioning

confidence: 99%

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Leveraging Qualitative Reasoning to Learning Manipulation Tasks

Wolter

Kirsch

2015

Robotics

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Learning and planning are powerful AI methods that exhibit complementary strengths. While planning allows goal-directed actions to be computed when a reliable forward model is known, learning allows such models to be obtained autonomously. In this paper we describe how both methods can be combined using an expressive qualitative knowledge representation. We argue that the crucial step in this integration is to employ a representation based on a well-defined semantics. This article proposes the qualitative spatial logic QSL, a representation that combines qualitative abstraction with linear temporal logic, allowing us to represent relevant information about the learning task, possible actions, and their consequences. Doing so, we empower reasoning processes to enhance learning performance beyond the positive effects of learning in abstract state spaces. Proof-of-concept experiments in two simulation environments show that this approach can help to improve learning-based robotics by quicker convergence and leads to more reliable action planning.Keywords: qualitative spatial reasoning; robot learning; AI robotics MotivationRobotic applications have evolved considerably, yet they still lack the efficiency, flexibility and adaptability that is needed to improve our everyday life as versatile service robots. Planning and learning Robotics 2015, 4 254 are the two major paradigms employed to make a robot act intelligently. Planning is a form of symbolic reasoning applied on various levels of abstraction. Planning is effective whenever reliable forward models of the robot and its environment are available. Learning is a powerful method to generate such models from data. However, it requires carefully handcrafted state space representations to converge to high-quality models in reasonable time. Neither planning nor learning on their own suffice to meet the demands of versatile service robots. By integrating both paradigms one can benefit from their respective strengths. So far, a true integration of learning and planning has not been achieved that allows a robot to acquire new skills efficiently in a truly autonomous way.Our work is motivated by this basic question of how to integrate learning and planning, leveraging their individual strengths to produce reliable and versatile robot behavior. We propose a qualitative reasoning framework as the glue between those two paradigms. Qualitative reasoning lifts information, e.g., obtained by perception, to a knowledge level, enabling us to integrate it with common sense knowledge and to use available reasoning techniques [1][2][3]. The qualitative representation serves two purposes: (1) to make a planning or action selection process with learned models more efficient; and (2) to control selection of learning samples in order to make a learning process more reliable and to obtain high-quality models with little manual design effort. We present two experimental studies in simulation for a specific robot task: to throw an object to a specific destination. The results ind...

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Section: Methodsmentioning

confidence: 99%

Section: Approach and Contributionmentioning

confidence: 99%

Leveraging Qualitative Reasoning to Learning Manipulation Tasks

Wolter

Kirsch

2015

Robotics

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“…Very first works are simulators which are dedicated to specific hardware like Molecubes [15] or CrossCube [16] and therefore lack of genericity. More recently, there have been more generic simulators proposed like Player/Stage [17], gazebo [18], USSR [19] or MORSE [20] but network access is not sufficiently detailed, as focus is made on the robots themselves. The most interesting initiative is ARGoS [21] which includes its own network simulator or that could be plugged to NS2 or NS3 [22].…”

Section: Wireless Network Simulation For Modular Robotsmentioning

confidence: 99%

Efficient Simulation Environment of Wireless Radio Communications in MEMS Modular Robots

Boillot

Dhoutaut

Bourgeois

2013

2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical A

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Abstract-Modular robots needs networking for coordination and it is particularly true for MEMS microrobots. A promising communication technology is nanowireless networking which could be integrated directly into MEMS microrobots, in our case, the catoms of the Claytronics project. A first step towards this objective is to design a wireless simulator able to deal with modular robots. This simulator called Vouivre is integrated in DPRSim a modular robot simulator developed by Intel Research. This paper describes Vouivre and its integration in DPRSim which is an interesting case of integrating different timelines in one simulator. Experiments validate our design and show the interest of using wireless communication in modular robots.

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“…Robotics systems are becoming highly complex and sophisticated, with an increasing number of hardware and software components there is also an increasing variety of tasks involved in performing robotics experiments, which induces much time and resources for validation the use of a simulator can ease the development, allowing to verify the component integration and to evaluate their behavior under different controlled circumstances [1,2]. The simulation today becomes one of the important issues which involved in the robotic fields due to many factors such as low cost , easily redesign, the safety especially the applications which involve the human applications and health such as assistive robot.…”

Section: Introductionmentioning

confidence: 99%

Simulated Robotic Arm Control using Sound Recognition System Commands

Majeed

Kuhnert

2017

IRATJ

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This manuscript deals with two specific issues for simulation a sound recognition system which has ability for controlling the movement of the manipulator robot arm (JACO). The robotic arm should be modulated then implemented using simulation program (MORSE), the simulated arm must have the same real design with all the details which involved the joints, links length, rotational joints angels. The sound system will build using robot operating system (ROS) then connect the system to the simulated arm for creating one block system that can control the arm with the specific words.

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Modular open robots simulation engine: MORSE

Cited by 185 publications

References 11 publications

Leveraging Qualitative Reasoning to Learning Manipulation Tasks

Leveraging Qualitative Reasoning to Learning Manipulation Tasks

Efficient Simulation Environment of Wireless Radio Communications in MEMS Modular Robots

Simulated Robotic Arm Control using Sound Recognition System Commands

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