Closing the Dynamics Gap through Self-Directed Robotic Interaction
Overview
This research presents CUE, an agentic system designed to bridge the 'dynamics gap' in Real2Sim2Real workflows. By interacting with the environment itself rather than relying on resets, CUE reconstructs scenes in simulation and identifies physical parameters such as friction and restitution that are unobservable from camera data alone. The system autonomously builds perception and targets interactions where its simulation is least accurate. In evaluations on billiards, CUE reduced held-out ball-position error by 72% relative to the strongest baseline across 25 hidden-physics conditions in simulation. The authors further demonstrate deployment of the system on a real pool table.
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Environment: simulation
“arXiv:2611.02691v1 Announce Type: new Abstract: Agentic Real2Sim2Real systems can reconstruct a scene in simulation from camera observations, but contact-rich dynamics such as friction and restitution cannot be observed without interaction. We call this the dynamics gap and present CUE, an agentic system that closes it by interacting with the environment itself. Given only a high-level instruction and no resets, the”
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Reported quantity · number of hidden-physics conditions: 25 other · reported trials
“agent builds its own perception, identifies physical parameters, and targets interactions where its simulation is least accurate. On billiards, CUE reduces held-out ball-position error by 72% relative to the strongest baseline across 25 hidden-physics conditions in simulation, and we further deploy it on a real pool table. More details can be found on the project website: https://cue-dynamics.github.io/”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalClosing the Dynamics Gap through Self-Directed Robotic Interaction
This research presents CUE, an agentic system designed to bridge the 'dynamics gap' in Real2Sim2Real workflows. By interacting with the environment itself rather than relying on resets, CUE reconstructs scenes in simulation and identifies physical parameters such as friction and restitution that are unobservable from camera data alone. The system autonomously builds perception and targets interactions where its simulation is least accurate. In evaluations on billiards, CUE reduced held-out ball-position error by 72% relative to the strongest baseline across 25 hidden-physics conditions in simulation. The authors further demonstrate deployment of the system on a real pool table.
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