Reinforcement Learning with Foundation Priors: Let the Embodied Agent Efficiently Learn on Its Own

Understand

Reinforcement learning (RL) is a promising approach for solving robotic manipulation tasks.

• However, it is challenging to apply the RL algorithms directly in the real world.
• For one thing, RL is data-intensive and typically requires millions of interactions with environments, which are impractical in real scenarios.
• For another, it is necessary to make heavy engineering efforts to design reward functions manually.