Solving Robotics Problems in Zero-Shot with Vision-Language Models
Keywords: Embodied AI, Multi-modality, Large Language Models, Robotics, Agents, LLM Agents, Multi-agent, agentic AI, hierarchical learning
TL;DR: Using a novel multi-agent Visual LLM framework, we show that many robotics problems can be solved in zero-shot without any fine-tuning or adaptation.
Abstract: We introduce Wonderful Team, a multi-agent Vision Large Language Model (VLLM) framework designed to solve robotics problems in a zero-shot regime. In our context, zero-shot means that for a novel environment, we provide a VLLM with an image of the robot's surroundings and a task description, and the VLLM outputs the sequence of actions necessary for the robot to complete the task. Unlike prior work that requires fine-tuning parts of the pipeline -- such as adjusting an LLM on robot-specific data or training separate vision encoders -- our approach demonstrates that with careful engineering, a single off-the-shelf VLLM can autonomously handle all aspects of a robotics task, from high-level planning to low-level location extraction and action execution. Crucially, compared to using GPT-4o alone, Wonderful Team is self-corrective and capable of iteratively fixing its own mistakes, enabling it to solve challenging long-horizon tasks. We validate our framework through extensive experiments, both in simulated environments using VIMABench and in real-world settings. Our system showcases the ability to handle diverse tasks such as manipulation, goal-reaching, and visual reasoning---all in a zero-shot manner. These results underscore a key point: vision-language models have progressed rapidly in the past year and should be strongly considered as a backbone for many robotics problems moving forward.
Primary Area: applications to robotics, autonomy, planning
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Submission Number: 13393
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