EVA-Client: 실제 로봇 조작 정책의 통합 배포·데이터 수집 프레임워크
EVA-Client: A Unified Data Collection, Inference, and Deployment Framework for Embodied Policies on Real Robots
- 매체
- Hugging Face
- 게시
- 중요도
- 3/5
- 화제 지수
- 11
한국어 요약
실제 로봇에서 조작 정책의 배포, 데이터 수집, 평가를 위한 오픈소스 프레임워크 EVA-Client가 공개됐다. 컴포넌트 분리 아키텍처로 로봇 백엔드와 추론 전략을 독립적으로 확장 가능하며, 평가 실행 시 자동으로 학습 데이터를 수집하는 방식으로 정책 반복 루프를 효율화한다.
원문 발췌 · English
We present EVA-Client, an open-source framework for deployment, data collection, and evaluation of trained manipulation policies on real robots. Sitting between a policy server and the physical hardware, EVA-Client unifies the real-robot stages of the policy iteration loop within a single codebase. It makes three contributions. First, a component-decoupled architecture in which robot backends, inference strategies, and transport middlewares form an orthogonal grid: adding a robot or a strategy touches only its own layer. Second, inspectable execution through Debug, Collect, and Eval workflows, with modes ranging from open-loop simulation to continuous real-time control. Third, every evaluation run doubles as a data collection, recording full rollouts in training-ready format alongside exhaustive logs and a side-by-side comparison viewer, so each evaluation feeds the next round of training rather than ending as an unrecorded impression. EVA-Client further consolidates major real-time inference strategies, synchronous and asynchronous execution, ACT-style temporal ensembling, Real-Time Chunking, and a naive-async ablation baseline, behind a single configuration surface.