The work gives the first algorithms for general robust Markov games with linear function approximation whose sample complexity breaks the curse of multiagency for large state spaces in both generative and online settings.
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Solving Rubik's Cube with a Robot Hand
Canonical reference. 78% of citing Pith papers cite this work as background.
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78% of classified citations
abstract
We demonstrate that models trained only in simulation can be used to solve a manipulation problem of unprecedented complexity on a real robot. This is made possible by two key components: a novel algorithm, which we call automatic domain randomization (ADR) and a robot platform built for machine learning. ADR automatically generates a distribution over randomized environments of ever-increasing difficulty. Control policies and vision state estimators trained with ADR exhibit vastly improved sim2real transfer. For control policies, memory-augmented models trained on an ADR-generated distribution of environments show clear signs of emergent meta-learning at test time. The combination of ADR with our custom robot platform allows us to solve a Rubik's cube with a humanoid robot hand, which involves both control and state estimation problems. Videos summarizing our results are available: https://openai.com/blog/solving-rubiks-cube/
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representative citing papers
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CoP tactile representation with differentiable calibration enables zero-shot sim-to-real transfer and outperforms binary and raw-taxel baselines on peg-in-hole insertion and ball balancing with a multi-fingered hand.
A transformer policy distilled from a privileged RL teacher enables 3.1x faster real-world cube rotation on the ORCA hand using solely joint sensor data by extracting implicit object state from temporal joint patterns.
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SeqRejectron constructs a stopping rule with a small set of validator policies to achieve horizon-free sample complexity for selective imitation learning under arbitrary dynamics shifts.
ReGuard discovers network scenarios where RL controllers perform 43-64% worse than achievable and reduces those gaps by 79-85% with lightweight rule-based protection that preserves normal performance.
HANDFUL learns resource-aware grasps using finger contact rewards and curriculum learning to improve success on sequential dexterous tasks in simulation and on a real LEAP hand.
Betting mechanisms can yield provably more accurate and efficient estimates of real-world robot behavior than Monte Carlo sampling under specified conditions, with practical approximations demonstrated on synthetic data and a robotic manipulator task.
Topology-preserving synthetic P&IDs generated by seeding from real drawings enable models trained solely on synthetics to achieve 63.8% edge mAP on real P&ID benchmarks, closing most of the gap to real-data training.
Single-timescale actor-critic with STORM momentum and a recent-sample buffer achieves optimal O(ε^{-2}) sample complexity for ε-optimal policies in finite discounted MDPs.
A Sim2Real2Sim learning pipeline enables a real-world dexterous robot to play piano pieces including Happy Birthday and Ode to Joy with an average F1-score of 0.881.
OpenAI Five achieved superhuman performance in Dota 2 by defeating the world champions using scaled self-play reinforcement learning.
A world-action model trained on ~800 synthetic demonstrations per task achieves 35% zero-shot success on real-robot manipulation tasks.
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Fishbone introduces a unified rib-spine representation computed via adaptive heat method, iso-contour ribs, and geometry-aware spine that enables real-time parametric deformation, reduced-space simulation, and animation on general meshes.
A VAE-based latent task representation enables automatic curriculum generation in CRL for non-Euclidean navigation tasks, outperforming interpolation and GAN-based methods in experiments.
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