Solving Robotics Problems in Zero-Shot with Vision-Language Models

We introduce Wonderful Team, a multi-agent visual LLM (VLLM) framework for solving robotics problems in the zero-shot regime. By zero-shot we mean that, for a novel environment, we feed a VLLM an image of the robot's environment and a description of the task, and have the VLLM output the sequence of actions necessary for the robot to complete the task. Prior work on VLLMs in robotics has largely focused on settings where some part of the pipeline is fine-tuned, such as tuning an LLM on robot data or training a separate vision encoder for perception and action generation. Surprisingly, due to recent advances in the capabilities of VLLMs, this type of fine-tuning may no longer be necessary for many tasks. In this work, we show that with careful engineering, we can prompt a single off-the-shelf VLLM to handle all aspects of a robotics task, from high-level planning to low-level location-extraction and action-execution. Wonderful Team builds on recent advances in multi-agent LLMs to partition tasks across an agent hierarchy, making it self-corrective and able to effectively partition and solve even long-horizon tasks. Extensive experiments on VIMABench and real-world robotic environments demonstrate the system's capability to handle a variety of robotic tasks, including manipulation, visual 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 strongly be considered as a backbone for robotics problems going forward.

Cold Diffusion on the Replay Buffer: Learning to Plan from Known Good States

Learning from demonstrations (LfD) has successfully trained robots to exhibit remarkable generalization capabilities. However, many powerful imitation techniques do not prioritize the feasibility of the robot behaviors they generate. In this work, we explore the feasibility of plans produced by LfD. As in prior work, we employ a temporal diffusion model with fixed start and goal states to facilitate imitation through in-painting. Unlike previous studies, we apply cold diffusion to ensure the optimization process is directed through the agent's replay buffer of previously visited states. This routing approach increases the likelihood that the final trajectories will predominantly occupy the feasible region of the robot's state space. We test this method in simulated robotic environments with obstacles and observe a significant improvement in the agent's ability to avoid these obstacles during planning.