Evaluating a Physics Engine as an Ingredient for Physical Reasoning

Weitnauer, Erik; Haschke, Robert; Ritter, Helge

doi:10.1007/978-3-642-17319-6_16

Cited by 14 publications

(10 citation statements)

References 10 publications

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“…We decided to follow [18] and we have chosen the Bullet RT physics engine, because (1) game-oriented physics engine are optimized towards large scale simulations (hundreds of bodies), and (2) contrary to usual simulation in robotics, speed and stability are preferable to accuracy in our context. Besides, Bullet is already integrated into ROS (the TF library uses Bullet datatypes, for instance), which facilitates future reuse of this work.…”

Section: B Physics Enginementioning

confidence: 99%

Simulation-based physics reasoning for consistent scene estimation in an HRI context

Sallami

Lemaignan

Clodic

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Reasoning about spatial and geometric relations between objects in a tabletop human-robot interaction is a challenge due to the perception not being always consistent: objects placed on a table seem to be slightly in the air; they overlap; they disappear due to occlusions. Yet, interpreting and anchoring perceptual data in a physically consistent estimation of the scene is a crucial ability for humans, and thus robots in HRI context. In this paper we present a simulationbased physics reasoner integrated in a lightweight situationassessment framework called Underworlds, that allows the robot to stabilize objects and build at run-time a consistent estimation of the scene, even for entirely hidden objects, while inferring the actions performed by its human partner.

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Section: B Physics Enginementioning

confidence: 99%

Simulation-based physics reasoning for consistent scene estimation in an HRI context

Sallami

Lemaignan

Clodic

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…They find the quantitative evaluations of human reasoning capabilities to be surprisingly similar to the results obtained from physics simulations. Other well-known terminology is temporal projection [9], physics-based reasoning [10] and physical reasoning [11] which deal with predicting real-world behavior using knowledge inferred from physics simulation. In terms of repetitively performing actions which improve environment manipulation strategies, robotic playing [12] is another related bio-inspired technique which compares trial-and-error behavior while accumulating environment knowledge with a children's way of exploring the world.…”

Section: Related Workmentioning

confidence: 99%

Self-supervised damage-avoiding manipulation strategy optimization via mental simulation

Doernbach

2019

Intel Serv Robotics

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Everyday robotics are challenged to deal with autonomous product handling in applications like logistics or retail, possibly causing damage on the items during manipulation. Traditionally, most approaches try to minimize physical interaction with goods. However, this paper proposes to take into account any unintended object motion and to learn damage-minimizing manipulation strategies in a self-supervised way. The presented approach consists of a simulation-based planning method for an optimal manipulation sequence with respect to possible damage. The planned manipulation sequences are generalized to new, unseen scenes in the same application scenario using machine learning. This learned manipulation strategy is continuously refined in a self-supervised, simulationin-the-loop optimization cycle during load-free times of the system, commonly known as mental simulation. In parallel, the generated manipulation strategies can be deployed in near-real time in an anytime fashion. The approach is validated on an industrial container-unloading scenario and on a retail shelf-replenishment scenario.

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“…Exploiting physical simulators for effectively solving subproblems in the context of robotics has become more attractive as shown by a number of recent investigations, where simulations are employed for planning in robocup soccer [9], for navigating in environments with deformable objects [10], and for reasoning about the consequences of everyday manipulation tasks [1]. A detailed evaluation for using physics engines for improving the physical reasoning capabilities of robots is given in [11]. But other fields also recognize simulators as valuable tools and utilize them, e.g., for character animation [12] and motion tracking [13].…”

Section: Related Workmentioning

confidence: 99%

“…, force,[1,3,5,7,9,11]), (P2, position, [-0.02, 0.00, 0.02])], Timeline), not(occurs(break,_,Timeline)), not(occurs(slip,_,Timeline)).…”

mentioning

confidence: 99%

Logic programming with simulation-based temporal projection for everyday robot object manipulation

Kunze

Dolha

Beetz

2011

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

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In everyday object manipulation tasks, like making a pancake, autonomous robots are required to decide on the appropriate action parametrizations in order to achieve desired (and to avoid undesired) outcomes. For determining the right parameters for actions like pouring a pancake mix onto a pancake maker, robots need capabilities to predict the physical consequences of their own manipulation actions. In this work, we integrate a simulation-based approach for making temporal projections for robot manipulation actions into the logic programming language PROLOG. The realized system enables robots to determine action parameters that bring about certain effects by utilizing simulation-based temporal projections within PROLOG's chronological backtracking mechanism. For a set of formal parameters and their respective ranges of values, the developed system translates the manipulation problems into physical simulations, monitors and logs the relevant data structures of the simulations, translates the logged data back into first-order time-interval-based representations, called timelines, and eventually evaluates the individual timelines with respect to specified performance criteria. Integrating the proposed approach into robot control programs allow robots to mentally simulate the consequences of different action parametrizations before committing to them and thereby to reduce the number of undesired outcomes.

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Evaluating a Physics Engine as an Ingredient for Physical Reasoning

Cited by 14 publications

References 10 publications

Simulation-based physics reasoning for consistent scene estimation in an HRI context

Simulation-based physics reasoning for consistent scene estimation in an HRI context

Self-supervised damage-avoiding manipulation strategy optimization via mental simulation

Logic programming with simulation-based temporal projection for everyday robot object manipulation

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