DRATS derives a minimax objective from a feasibility formulation of MTRL to adaptively sample tasks with the largest return gaps, leading to better worst-task performance on MetaWorld benchmarks.
Data augmentation for manipulation
3 Pith papers cite this work. Polarity classification is still indexing.
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A framework augments single fisheye demonstrations into multiple novel-view trajectories with obstacles via fisheye-adapted Gaussian Splatting and trajectory optimization, raising policy success rates in original and modified scenes.
MimicGen creates over 50K robot demonstrations from roughly 200 human ones, allowing imitation learning to achieve strong performance on complex long-horizon tasks like assembly and coffee preparation.
citing papers explorer
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Distributionally Robust Multi-Task Reinforcement Learning via Adaptive Task Sampling
DRATS derives a minimax objective from a feasibility formulation of MTRL to adaptively sample tasks with the largest return gaps, leading to better worst-task performance on MetaWorld benchmarks.
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One Demo is Worth a Thousand Trajectories: Action-View Augmentation for Visuomotor Policies
A framework augments single fisheye demonstrations into multiple novel-view trajectories with obstacles via fisheye-adapted Gaussian Splatting and trajectory optimization, raising policy success rates in original and modified scenes.
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MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations
MimicGen creates over 50K robot demonstrations from roughly 200 human ones, allowing imitation learning to achieve strong performance on complex long-horizon tasks like assembly and coffee preparation.