VLA-RL applies online RL to pretrained VLAs, yielding a 4.5% gain over strong baselines on 40 LIBERO manipulation tasks and matching commercial models like π₀-FAST.
Bootstrap- ping reinforcement learning with imitation 10 for vision-based agile flight
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RL framework for agile drone racing combines task-aware switching and physically informed procedural track generation to achieve 7.4x better zero-shot generalization to unseen tracks while maintaining competitive speeds.
PerchRL applies two-stage RL with randomized trajectories, temporal augmentation, and visibility-aware rewards to achieve vision-based perching on irregularly moving inclined platforms.
Unsupervised behavioral mode discovery combined with mutual information rewards enables RL fine-tuning of multimodal generative policies that achieves higher success rates without losing action diversity.
SCAL aligns source and target latent features conditioned on system state, reducing target imitation loss to a source loss plus a conditional-KL term, and reports strong sample efficiency in BARC-CARLA.
citing papers explorer
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VLA-RL: Towards Masterful and General Robotic Manipulation with Scalable Reinforcement Learning
VLA-RL applies online RL to pretrained VLAs, yielding a 4.5% gain over strong baselines on 40 LIBERO manipulation tasks and matching commercial models like π₀-FAST.
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Bridging Performance and Generalization in Reinforcement Learning for Agile Flight
RL framework for agile drone racing combines task-aware switching and physically informed procedural track generation to achieve 7.4x better zero-shot generalization to unseen tracks while maintaining competitive speeds.
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PerchRL: Vision-Based Agile Perching on Inclined Platforms under Rapid and Irregular Motion
PerchRL applies two-stage RL with randomized trajectories, temporal augmentation, and visibility-aware rewards to achieve vision-based perching on irregularly moving inclined platforms.
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Behavioral Mode Discovery for Fine-tuning Multimodal Generative Policies
Unsupervised behavioral mode discovery combined with mutual information rewards enables RL fine-tuning of multimodal generative policies that achieves higher success rates without losing action diversity.
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State-Conditional Adversarial Learning: An Off-Policy Visual Domain Transfer Method for End-to-End Imitation Learning
SCAL aligns source and target latent features conditioned on system state, reducing target imitation loss to a source loss plus a conditional-KL term, and reports strong sample efficiency in BARC-CARLA.