sim-to-real

This paper theoretically identifies and mitigates context distribution shift in multi-turn dialogue RL, proposing Calibrated Interactive RL that couples interactive RL with simulator alignment to reduce the sim-to-real gap and achieve state-of-the-art performance.

This paper studies when and how a planner should supplement a pre-trained simulator with real experiments in sequential decision problems, proposing Fisher-SEP to minimize posterior variance of a target policy's value.

This paper introduces Domain-Randomized Instance Set (DRIS), a method that simultaneously represents multiple randomized instances to improve sim-to-real transfer for dexterous manipulation. It demonstrates zero-shot transfer on a challenging reactive catching task with a flat plate end-effector, requiring no real-world fine-tuning.

OpenAI developed a robot hand capable of solving a Rubik's Cube using a novel technique called Automatic Domain Randomization (ADR), which progressively increases simulation difficulty to enable effective transfer of learned behaviors from simulation to the real world.

OpenAI proposes an asymmetric actor-critic method for robot learning that leverages full state observability in simulators to train policies that operate on partial observations (RGBD images), enabling effective sim-to-real transfer without real-world training data.

OpenAI researchers demonstrate a method to bridge the reality gap in robotic control by training policies with randomized simulator dynamics, enabling robots trained purely in simulation to successfully transfer to real-world tasks like object manipulation without physical training.

OpenAI demonstrates that domain randomization—randomly varying colors, textures, lighting, and camera settings in simulated training data—enables deep learning models to effectively transfer from simulation to real-world robotic spam detection tasks without retraining from scratch.

This paper proposes a method to bridge the simulation-to-real-world gap in robotics by learning a deep inverse dynamics model that maps desired next states (from simulation) to appropriate real-world actions. The approach is evaluated against baselines like output error control and Gaussian dynamics adaptation.