ASPIRE is a research framework where coding agents iteratively write and refine sensorimotor skills for robot manipulation tasks in simulation and on real robots.
RLinf/RPent
An open framework for building embodied agents that use LLM planners, VLA models and memory to run manipulation tasks in simulators and on real robots.
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Funciones clave
- 01Pluggable planners, including Claude Code, Codex and custom planners.
- 02Supports multiple VLA models such as Pi0.5, RLDX-1 and LingBot-VLA.
- 03Runs tasks in LIBERO-PRO, RoboCasa, RoboTwin and RoboDojo simulators.
- 04Provides Franka and dual-Franka real-robot extensions.
- 05Offers interactive CLI control and a live web dashboard.
Casos de uso
- Researching long-horizon robotic manipulation with LLM-guided agents.
- Prototyping embodied agents before deploying them on Franka robot arms.
- Studying continual or reinforcement learning for self-evolving physical agents.
- Benchmarking VLA models across LIBERO, RoboCasa and RoboTwin tasks.
Temas
Preguntas frecuentes
- ¿Qué es RPent?
- An open framework for building embodied agents that use LLM planners, VLA models and memory to run manipulation tasks in simulators and on real robots.
- ¿RPent es gratuito? ¿Qué licencia tiene?
- RPent es open source bajo la licencia Apache-2.0. Revisa los términos de la licencia antes de usarlo en productos comerciales.
- ¿RPent sigue mantenido?
- Mantenimiento activo. El último commit de RPent fue el 16 oct 2026.
- ¿Para qué puedo usar RPent?
- Researching long-horizon robotic manipulation with LLM-guided agents. Prototyping embodied agents before deploying them on Franka robot arms. Studying continual or reinforcement learning for self-evolving physical agents. Benchmarking VLA models across LIBERO, RoboCasa and RoboTwin tasks.
- ¿Dónde consigo RPent?
- El código fuente y las instrucciones de instalación están en GitHub: https://github.com/RLinf/RPent
Resumen generado por IA a partir del README del proyecto. Las estadísticas de GitHub se actualizan a diario.
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