Receiving fine-grained instruction from these specialized teachers can often be non-uniform, costly, and limited by their availability.

Offline safe reinforcement learning (RL) aims to find an optimal policy using a pre-collected dataset when data collection is impractical or risky.

Duetothe long-horizon nature ofthe task, we often heavily rely onvisual manuals that provide step-by-step guidance during the assembly process.