Look Before You Leap: Unveiling the Power of GPT-4V in Robotic Vision-Language Planning
(2311.17842)Abstract
In this study, we are interested in imbuing robots with the capability of physically-grounded task planning. Recent advancements have shown that LLMs possess extensive knowledge useful in robotic tasks, especially in reasoning and planning. However, LLMs are constrained by their lack of world grounding and dependence on external affordance models to perceive environmental information, which cannot jointly reason with LLMs. We argue that a task planner should be an inherently grounded, unified multimodal system. To this end, we introduce Robotic Vision-Language Planning (ViLa), a novel approach for long-horizon robotic planning that leverages vision-language models (VLMs) to generate a sequence of actionable steps. ViLa directly integrates perceptual data into its reasoning and planning process, enabling a profound understanding of commonsense knowledge in the visual world, including spatial layouts and object attributes. It also supports flexible multimodal goal specification and naturally incorporates visual feedback. Our extensive evaluation, conducted in both real-robot and simulated environments, demonstrates ViLa's superiority over existing LLM-based planners, highlighting its effectiveness in a wide array of open-world manipulation tasks.
Overview
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The paper introduces the Robotic Vision-Language Planning (VILA) method for robotic planning, enhancing task performance by integrating perceptual data into planning.
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VILA significantly outperforms existing models by enabling robots to understand spatial layouts and object characteristics pertinent to tasks.
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The method allows robots to reason and plan using visual observations and high-level language instructions for a variety of goal specifications.
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In real-world scenarios, VILA excels in 16 manipulation tasks, demonstrating its effectiveness and general-purpose applicability in task planning.
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Future improvements for VILA include reducing dependency on preset skills, increasing model steerability, and achieving consistent outputs.
Introduction
Robotic planning is an intricate task that involves understanding the environment and formulating a series of actions to achieve a specific objective. Traditional approaches lean on LLMs for this purpose, yet they struggle due to the absence of physical world grounding and the reliance on external models for understanding visual data. LLMs cannot inherently perceive or reason about the state of robots within their surroundings, limiting their capacity to deal with real-world constraints and nuances.
Vision-Language Integration in Planning
Addressing the limitations of LLMs, this paper introduces the Robotic Vision-Language Planning (VILA) method, a significant leap forward that integrates perceptual data directly into the reasoning and planning phases. This integration enables robots to reason with an enriched commonsense understanding of the world as it is visually perceived, leading to enhanced performance in tasks that demand spatial and object attribute comprehensions, such as figuring out spatial layouts and identifying object characteristics pertinent to the tasks at hand.
Advancements in Robotic Task Planning
VILA outperforms existing models by engaging a novel approach that prompts Vision-Language Models (VLMs) to produce actionable steps based on visual observations and high-level language instructions. The key strengths of VILA are threefold:
- Spatial Layout and Object Attribute Recognition: It can interpret complex geometric configurations and relationships and reason about attributes of objects in a context-sensitive manner.
- Versatile Goal Specification: VILA accepts not just verbal instructions but also uses images to define goals, thereby allowing for a diverse set of goal descriptions encompassing text, pictures, or a mixture of both.
- Natural Use of Visual Feedback: The model can robustly plan in a dynamic environment thanks to its capability to process visual feedback in a meaningful and intuitive manner.
Real-World Applicability and Evaluation
Extensive tests display VILA's proficiency across 16 real-world manipulation tasks that demand a deep understanding of common knowledge grounded in the visual world. In simulated environments, too, VILA remains consistent, demonstrating a universal appeal and underscoring its potential as a general-purpose task planner.
Challenges and Future Directions
Despite the remarkable success of VILA, there are areas for future improvement like avoiding reliance on preset primitive skills, improving the steerability of black-box VLMs, and refining the consistency of model outputs without the need for in-context examples. These aspects present fertile ground for advancing robotic planning even further.
Conclusion
By melding vision with language, VILA ensures that embodied agents such as robots can interpret instructions within the context of their visible environment. This leap has the potential to drastically improve robot autonomy and efficiency in performing a wide range of complex, real-world tasks.
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