Yao Mu
9 indexed papers
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BORA is an offline-to-online RL framework that enhances dexterous VLA models for real-world robotics by using an action-conditioned critic and a lightweight residual adaptation mechanism to correct execution errors.
BORA is an offline-to-online RL framework that enhances dexterous VLA models for real-world robotics by using an action-conditioned critic and a lightweight residual adaptation mechanism to correct execution errors.
The Implicit Drifting Policy (IDP) is a novel one-step action generation framework that implicitly enforces trajectory correction constraints by analyzing local expert action geometry, overcoming the difficulties of explicitly estimating a training-time drifting field.
The Implicit Drifting Policy (IDP) is a novel one-step action generation framework that implicitly enforces trajectory correction constraints by analyzing local expert action geometry, overcoming the difficulties of explicitly estimating a training-time drifting field.
The paper proposes ROVE, a reinforcement learning framework for humanoid VLA post-training with imperfect human interventions.
The paper presents EBench, a simulation benchmark for diagnosing generalist mobile manipulation policies.
This paper introduces a safety benchmark and dataset to evaluate the safety of Vision-Language-Action models.
The paper introduces dVLA-RL, a method for training Vision-Language-Action models using Reinforcement Learning.
This paper organizes embodied data sources for multimodal foundation models into a pyramid, focusing on real-robot, UMI-style, egocentric and exocentric, simulation, and general vision-language data.