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RLinf

RLinf/RPent

An open framework for building embodied agents that use LLM planners, VLA manipulation models and memory to run robot tasks in simulators and on real robots.

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마지막 커밋
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RPent (Recursive Physical Agent) is an agentic infrastructure for physical robots. Instead of tying the system to one foundation model, it combines perception, reasoning, memory, execution and self-evolution components into a single physical agent, built on service-oriented, standardized and composable design principles.

The framework lets a planner such as Claude Code or Codex decide how to break a task into steps, while VLA models like Pi0.5, RLDX-1 and LingBot-VLA execute the low-level manipulation primitives. It supports LIBERO-PRO, RoboCasa, RoboTwin and RoboDojo simulators, and Franka and dual-Franka real-robot setups are listed as supported, with SO-101 and YAM marked as planned.

RPent ships a CLI for running predefined tasks, an interactive mode for steering the agent from a terminal, and a live dashboard that streams agent reasoning, camera views and the action timeline. Documentation is available in English and Chinese.

핵심 기능

  • 01Pluggable planners, including Claude Code, Codex and custom planners.
  • 02Supports VLA models such as Pi0.5, RLDX-1 and LingBot-VLA as action primitives.
  • 03Runs on LIBERO-PRO, RoboCasa, RoboTwin and RoboDojo simulators.
  • 04Supports Franka and dual-Franka real-robot setups.
  • 05Interactive CLI mode for steering the agent during a run.
  • 06Live dashboard with agent reasoning, camera views and action timeline.

활용 사례

  • Benchmarking long-horizon manipulation methods on LIBERO-PRO or RoboCasa.
  • Researching self-evolving or memory-guided embodied agents.
  • Prototyping agentic robot control on Franka arms before production deployment.

토픽

#embodied-ai#robotics#agentic-ai#vla#robot-simulation#reinforcement-learning#llm-agents#embodied

자주 묻는 질문

RPent은(는) 무엇인가요?
An open framework for building embodied agents that use LLM planners, VLA manipulation models and memory to run robot tasks in simulators and on real robots. RPent (Recursive Physical Agent) is an agentic infrastructure for physical robots. Instead of tying the system to one foundation model, it combines perception, reasoning, memory, execution and self-evolution components into a single physical agent, built on service-oriented, standardized and composable design principles.
RPent은(는) 무료로 사용할 수 있나요? 라이선스는 무엇인가요?
RPent은(는) Apache-2.0 라이선스의 오픈소스입니다. 상업용 제품에 사용하기 전에 라이선스 조건을 확인하세요.
RPent은(는) 아직 유지보수되고 있나요?
활발히 유지보수 중. RPent의 마지막 커밋은 2026년 10월 19일입니다.
RPent은(는) 어디에 사용할 수 있나요?
Benchmarking long-horizon manipulation methods on LIBERO-PRO or RoboCasa. Researching self-evolving or memory-guided embodied agents. Prototyping agentic robot control on Franka arms before production deployment.
RPent은(는) 어디서 받을 수 있나요?
소스 코드와 설치 방법은 GitHub에 있습니다: https://github.com/RLinf/RPent

요약은 프로젝트 README를 바탕으로 AI가 작성했습니다. GitHub 통계는 매일 갱신됩니다.

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