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No physics engine is uniformly faithful, with critical failures in simulating impulsive contact and rapid textile motion revealed by the GAUGE benchmark.
Achieving a 78.3% success rate in real-world mobile manipulation, this framework bridges the reality gap with zero-shot transferability to unseen tasks.
Forget painstakingly collecting real-world data for deformable object manipulation: SIM1 achieves equivalent policy performance with 15x less data by grounding simulations in the physical world.
Reconstructing 3D scenes from images obscured by smoke and extreme darkness is now significantly more achievable, thanks to insights gleaned from the NTIRE 2026 challenge.
Zero-shot visuotactile policies trained in a fast, parallelized simulator can directly control real robots in contact-rich tasks.
VLNVerse tackles the sim-to-real gap in vision-language navigation by providing a unified, large-scale benchmark with realistic physics simulation and full-kinematics embodied agents.
