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RoboSimStudio addresses the critical challenge of simulating complex robot-object interactions by providing an editable toolkit for contact mechanics, deformations, and friction parameters within the Newton physics engine. By enabling iterative refinement through human guidance and coding agents, this tool advances the fidelity required for training robust robotic manipulation policies in simulation before real-world deployment.
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RoboSimStudio: An Interactive Toolkit for Rapid Real2Sim of Robot Manipulation Tasks

arXiv2 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2610.18835v1 Announce Type: cross Abstract: It is difficult to accurately model the physics of the interaction between a robot and the objects it manipulates, including contact mechanics, coupled contact dynamics, deformations, stiffness and friction. We present RoboSimStudio (RSS), an interactive toolkit that makes choices of solver, contact model, and physical parameters explicit and editable, allowing both humans and coding agents to iteratively refine simulations based on observed physical behavior. We implement RSS with the open Newton physics simulation system and describe experiments with human-guided real2sim using (1) visual evaluation, (2) force-feedback and (3) agentic Real2Sim2Real. Code, video and additional information can be found on the project website.

Abstract:It is difficult to accurately model the physics of the interaction between a robot and the objects it manipulates, including contact mechanics, coupled contact dynamics, deformations, stiffness and friction. We present RoboSimStudio (RSS), an interactive toolkit that makes choices of solver, contact model, and physical parameters explicit and editable, allowing both humans and coding agents to iteratively refine simulations based on observed physical behavior. We implement RSS with the open Newton physics simulation system and describe experiments with human-guided real2sim using (1) visual evaluation, (2) force-feedback and (3) agentic Real2Sim2Real. Code, video and additional information can be found on the project website.

13 pages, 7 figures, 1 table. Project website: this https URL

Robotics (cs.RO); Artificial Intelligence (cs.AI); Graphics (cs.GR); Machine Learning (cs.LG)

arXiv:2610.18835 [cs.RO]

(or arXiv:2610.18835v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.18835 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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