일반 로봇 제어를 위한 비전-언어-행동 흐름 모델, π0: 대규모 로봇 데이터와 플로우 매칭을 활용한 덱스트러스 조작 능력 학습
인터넷 규모의 시맨틱 지식을 가진 사전 훈련된 비전-언어 모델(VLM)을 기반으로 하고 플로우 매칭 아키텍처를 통해 연속적인 행동을 생성하는 새로운 로봇 제어 모델 π0를 제시하며, 대규모의 다양한 로봇 데이터를 통해 덱스트러스하고 복잡한 조작 작업을 수행하는 방법을 제시합니다.
π0: A Vision-Language-Action Model for General Robot Control Using Flow Matching and Large-Scale Pre-Training
This paper introduces π0, a novel vision-language-action (VLA) model for general robot control that leverages flow matching for action generation and benefits from large-scale pre-training on diverse robot manipulation data, achieving state-of-the-art performance on complex, dexterous tasks.
Sparse Diffusion Policy: Enhancing Robot Learning Efficiency Through Sparsity, Reusability, and Flexibility
This paper introduces Sparse Diffusion Policy (SDP), a novel approach that enhances robot learning efficiency in multitask, continual, and transfer learning scenarios by integrating Mixture of Experts (MoE) within a transformer-based diffusion policy.
GR-2: A Generative AI Model for Robot Manipulation Using Web-Scale Knowledge
GR-2, a novel AI model, demonstrates impressive capabilities in robot manipulation by leveraging large-scale pre-training on internet videos, enabling it to generalize across a wide range of tasks and environments.
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