ReSYNC learns recovery skills via RL then discovers and refines relational predicates to enable abstract planning that generalizes failure avoidance to unseen long-horizon tasks, outperforming baselines by over 50% in simulation and transferring to real robots.
Towards bridging the gap: Systematic sim-to- real transfer for diverse legged robots
7 Pith papers cite this work. Polarity classification is still indexing.
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A per-joint 2-DoF feedforward–feedback controller with disturbance observer shapes real actuators to match idealized second-order sim dynamics, enabling zero-shot RL policy transfer.
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.
CTS-MoE combines a dense MoE actor with perception-based gating and a multi-critic architecture to enable adaptive perceptive locomotion on discontinuous terrain in a single-stage teacher-student training setup.
citing papers explorer
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Recover, Discover, Plan: Learning Skills and Concepts from Robot Failures
ReSYNC learns recovery skills via RL then discovers and refines relational predicates to enable abstract planning that generalizes failure avoidance to unseen long-horizon tasks, outperforming baselines by over 50% in simulation and transferring to real robots.
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Actuator Reality Shaping for Zero-Shot Sim-to-Real Robot Learning
A per-joint 2-DoF feedforward–feedback controller with disturbance observer shapes real actuators to match idealized second-order sim dynamics, enabling zero-shot RL policy transfer.
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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.
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Humanoid Whole-Body Badminton via Multi-Stage Reinforcement Learning
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.
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Isaac Lab: A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning
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.
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CTS-MoE: Implicit Terrain Adaptation via Mixture-of-Experts for Perceptive Locomotion
CTS-MoE combines a dense MoE actor with perception-based gating and a multi-critic architecture to enable adaptive perceptive locomotion on discontinuous terrain in a single-stage teacher-student training setup.