ReplanVLM: Replanning Robotic Tasks with Visual Language Models

Large language models (LLMs) have gained increasing popularity in robotic task planning due to their exceptional abilities in text analytics and generation, as well as their broad knowledge of the world. However, they fall short in decoding visual cues. LLMs have limited direct perception of the world, which leads to a deficient grasp of the current state of the world. By contrast, the emergence of visual language models (VLMs) fills this gap by integrating visual perception modules, which can enhance the autonomy of robotic task planning. Despite these advancements, VLMs still face challenges, such as the potential for task execution errors, even when provided with accurate instructions. To address such issues, this paper proposes a ReplanVLM framework for robotic task planning. In this study, we focus on error correction interventions. An internal error correction mechanism and an external error correction mechanism are presented to correct errors under corresponding phases. A replan strategy is developed to replan tasks or correct error codes when task execution fails. Experimental results on real robots and in simulation environments have demonstrated the superiority of the proposed framework, with higher success rates and robust error correction capabilities in open-world tasks. Videos of our experiments are available at https://youtu.be/NPk2pWKazJc.

InsightSee: Advancing Multi-agent Vision-Language Models for Enhanced Visual Understanding

Accurate visual understanding is imperative for advancing autonomous systems and intelligent robots. Despite the powerful capabilities of vision-language models (VLMs) in processing complex visual scenes, precisely recognizing obscured or ambiguously presented visual elements remains challenging. To tackle such issues, this paper proposes InsightSee, a multi-agent framework to enhance VLMs' interpretative capabilities in handling complex visual understanding scenarios. The framework comprises a description agent, two reasoning agents, and a decision agent, which are integrated to refine the process of visual information interpretation. The design of these agents and the mechanisms by which they can be enhanced in visual information processing are presented. Experimental results demonstrate that the InsightSee framework not only boosts performance on specific visual tasks but also retains the original models' strength. The proposed framework outperforms state-of-the-art algorithms in 6 out of 9 benchmark tests, with a substantial advancement in multimodal understanding.

GameVLM: A Decision-making Framework for Robotic Task Planning Based on Visual Language Models and Zero-sum Games

With their prominent scene understanding and reasoning capabilities, pre-trained visual-language models (VLMs) such as GPT-4V have attracted increasing attention in robotic task planning. Compared with traditional task planning strategies, VLMs are strong in multimodal information parsing and code generation and show remarkable efficiency. Although VLMs demonstrate great potential in robotic task planning, they suffer from challenges like hallucination, semantic complexity, and limited context. To handle such issues, this paper proposes a multi-agent framework, i.e., GameVLM, to enhance the decision-making process in robotic task planning. In this study, VLM-based decision and expert agents are presented to conduct the task planning. Specifically, decision agents are used to plan the task, and the expert agent is employed to evaluate these task plans. Zero-sum game theory is introduced to resolve inconsistencies among different agents and determine the optimal solution. Experimental results on real robots demonstrate the efficacy of the proposed framework, with an average success rate of 83.3%.