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Sim-to-Real Reinforcement Learning for Vision-Based Dexterous Manipulation on Humanoids
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Learning generalizable robot manipulation policies, especially for complex multi-fingered humanoids, remains a significant challenge. Existing approaches primarily rely on extensive data collection and imitation learning, which are expensive, labor-intensive, and difficult to scale. Sim-to-real reinforcement learning (RL) offers a promising alternative, but has mostly succeeded in simpler state-based or single-hand setups. How to effectively extend this to vision-based, contact-rich bimanual manipulation tasks remains an open question. In this paper, we introduce a practical sim-to-real RL recipe that trains a humanoid robot to perform three challenging dexterous manipulation tasks: grasp-and-reach, box lift and bimanual handover. Our method features an automated real-to-sim tuning module, a generalized reward formulation based on contact and object goals, a divide-and-conquer policy distillation framework, and a hybrid object representation strategy with modality-specific augmentation. We demonstrate high success rates on unseen objects and robust, adaptive policy behaviors -- highlighting that vision-based dexterous manipulation via sim-to-real RL is not only viable, but also scalable and broadly applicable to real-world humanoid manipulation tasks.
Forward citations
Cited by 8 Pith papers
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SILO: Simulation-in-the-Loop Sim-to-Real Transfer for Multi-Stage Cable Routing
SILO enables the first reported zero-shot sim-to-real RL transfer for multi-stage cable routing by approximating cables as articulated rigid links and executing policy actions inside a synchronized digital twin.
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Closing the Reality Gap: Zero-Shot Sim-to-Real Deployment for Dexterous Force-Based Grasping and Manipulation
Zero-shot sim-to-real RL policies on a five-finger hand achieve commandable grasp-force tracking and in-hand reorientation using dense tactile simulation, current-to-torque calibration, and actuator randomization.
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ClutterDexGrasp: A Sim-to-Real System for General Dexterous Grasping in Cluttered Scenes
A simulation-trained teacher-student policy achieves zero-shot sim-to-real closed-loop target-oriented dexterous grasping in cluttered scenes, with 83.9 percent real-world success.
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CLEAR: Closed-Loop Reinforcement Learning at Scale for End-to-End Autonomous Driving
Residual waypoint RL around a frozen VLA prior, scaled via heterogeneous CARLA/H100 infrastructure, raises closed-loop driving score and success rate on longest6 v2 and Bench2Drive.
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Where to Touch, How to Contact: A Hierarchical RL-MPC Framework for Geometry-Aware Sim-to-Real Manipulation
A hierarchical RL-MPC framework with a 'contact intention' interface achieves data-efficient, robust non-prehensile manipulation that transfers zero-shot to a real robot.
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HERMES: Human-to-Robot Embodied Learning from Multi-Source Motion Data for Mobile Dexterous Manipulation
HERMES converts a single human motion demonstration into a deployable mobile bimanual dexterous manipulation policy, using RL, depth-image distillation, and closed-loop PnP pose refinement.
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EmbodieDreamer: Advancing Real2Sim2Real Transfer for Policy Training via Embodied World Modeling
A Real2Sim2Real framework that aligns simulator dynamics via differentiable parameter fitting and renders photorealistic policy-training videos with a diffusion model, improving real-world manipulation success.
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Learning Dexterous Object Handover
A dual quaternion-based reward function yields more robust learned object handover with a four-finger hand than Euler or matrix rotation rewards in simulation.
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