LoHoRavens: A Long-Horizon Language-Conditioned Benchmark for Robotic Tabletop Manipulation

The convergence of embodied agents and large language models (LLMs) has brought significant advancements to embodied instruction following.Particularly, the strong reasoning capabilities of LLMs make it possible for robots to perform long-horizon tasks without expensive annotated demonstrations.However, public benchmarks for testing the long-horizon reasoning capabilities of language-conditioned robots in various scenarios are still missing. To fill this gap, this work focuses on the tabletopmanipulation task and releases a simulation benchmark,\textit{LoHoRavens}, which covers various long-horizonreasoning aspects spanning color, size, space, arithmeticsand reference.Furthermore, there is a key modality bridging problem forlong-horizon manipulation tasks with LLMs: how toincorporate the observation feedback during robot executionfor the LLM's closed-loop planning, which is however less studied by prior work. We investigate two methods of bridging the modality gap: caption generation and learnable interface for incorporating explicit and implicit observation feedback to the LLM, respectively.These methods serve as the two baselines for our proposed benchmark. Experiments show that both methods struggle to solve most tasks, indicating long-horizon manipulation tasks are still challenging for current popular models.We expect the proposed public benchmark and baselines can help the community develop better models for long-horizon tabletop manipulation tasks.