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Found 4 papers, 3 papers with code
Solving Rubik's Cube with a Robot Hand
We demonstrate that models trained only in simulation can be used to solve a manipulation problem of unprecedented complexity on a real robot.
Emergent Tool Use From Multi-Agent Autocurricula
Through multi-agent competition, the simple objective of hide-and-seek, and standard reinforcement learning algorithms at scale, we find that agents create a self-supervised autocurriculum inducing multiple distinct rounds of emergent strategy, many of which require sophisticated tool use and coordination.
Learning Dexterous In-Hand Manipulation
We use reinforcement learning (RL) to learn dexterous in-hand manipulation policies which can perform vision-based object reorientation on a physical Shadow Dexterous Hand.
Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
The purpose of this technical report is two-fold.
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