Actuator reality shaping uses a 2DOF controller to align real actuator closed-loop behavior with idealized simulation reference dynamics, enabling zero-shot sim-to-real policy deployment across multiple robot platforms.
Towards bridging the gap: Systematic sim-to- real transfer for diverse legged robots
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AquaMILR robot demonstrates that programmable compliance and depth regulation enable robust 3D locomotion in hydro-cluttered aquatic environments by exploiting rather than avoiding body-obstacle contacts.
A framework using 3D Gaussian Splatting for visual domain randomization enables robust monocular RGB-based dexterous in-hand reorientation on real hardware for multiple objects under varied lighting.
A multi-stage RL curriculum produces a unified whole-body controller enabling humanoid robots to sustain badminton rallies in simulation and return shuttles at up to 19.1 m/s in real hardware, with both EKF-based and prediction-free variants.
Isaac Lab is a unified GPU-native platform combining high-fidelity physics, photorealistic rendering, multi-frequency sensors, domain randomization, and learning pipelines for scalable multi-modal robot policy training.
citing papers explorer
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Actuator Reality Shaping for Zero-Shot Sim-to-Real Robot Learning
Actuator reality shaping uses a 2DOF controller to align real actuator closed-loop behavior with idealized simulation reference dynamics, enabling zero-shot sim-to-real policy deployment across multiple robot platforms.
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Three-dimensional hydro-cluttered locomotion by an undulatory robot
AquaMILR robot demonstrates that programmable compliance and depth regulation enable robust 3D locomotion in hydro-cluttered aquatic environments by exploiting rather than avoiding body-obstacle contacts.
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ViserDex: Visual Sim-to-Real for Robust Dexterous In-hand Reorientation
A framework using 3D Gaussian Splatting for visual domain randomization enables robust monocular RGB-based dexterous in-hand reorientation on real hardware for multiple objects under varied lighting.