GROW$^2$: Grounding Which and Where for Robot Tool Use
Yuhong Deng, Yuyao Liu, David Hsu
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Key claim
GROW² enables zero-shot tool use in robots.
GROW² enables robots to creatively use everyday objects as tools by understanding their parts and functions. It achieves zero-shot generalization and outperforms existing methods in both simulated and real-world scenarios. This means builders can develop more versatile robotic systems that adapt to new tasks without extensive retraining.
In plain English
Imagine you're building a robot that needs to perform tasks in unpredictable environments, like cutting a cake without a knife. Traditionally, robots are limited to using tools as they were designed, which means they struggle when faced with novel situations or objects. For instance, if a robot only knows how to use a knife for cutting, it won't think to use a plate creatively, leading to failures in task execution. This limitation is known as open-world affordance grounding, where the robot must identify and localize the right tool and its parts for a given task.
What this paper introduces is a method called GROW², which helps robots overcome these challenges by breaking down the process of tool use into two levels: semantic and geometric. Semantically, it uses the reasoning capabilities of Vision-Language Models to interpret task instructions and select the right object as a tool. Geometrically, it then pinpoints the specific parts of the tool and the target object that are relevant for the task, all from a single RGB-D image. This hierarchical approach allows the robot to use objects in ways that weren't explicitly trained, making it more adaptable.
Compared to previous methods, GROW² shows significant improvements in predicting how objects can be used as tools, achieving better performance on established benchmarks and demonstrating zero-shot generalization. For builders, this means you can create robots that are not only more capable but also require less retraining when faced with new tasks or objects, ultimately leading to more efficient and versatile robotic systems.
The approach introduces a novel hierarchical method for open-world affordance grounding, leveraging commonsense reasoning and geometric grounding.
The experiments demonstrate strong performance against state-of-the-art baselines across multiple benchmarks, supporting the claims made.
Deep reliability assessment
The methodology supports the claim that GROW2 can generalize to open-category objects and perform well in both simulated and real-world settings, but the reliance on single-view RGB-D images may limit its effectiveness in more complex environments.
Reproducibility
No open source code or dataset is mentioned in the paper.
Discussion questions
- 1.The authors claim GROW2 can generalize to open-category objects without additional training data. How realistic is this claim in environments with highly variable object appearances?
- 2.GROW2 uses vision-language models for semantic selection. How might the performance change if the language model's understanding of tool affordances is limited or biased?
- 3.The paper highlights the importance of 3D reconstruction for resolving occlusions. Has anyone faced similar challenges in their work, and how did you address them?
- 4.GROW2's approach bypasses the need for large-scale affordance annotations. Do you think this tradeoff is beneficial, or does it risk missing nuanced affordance details?
- 5.The real-world experiments show a significant performance drop without multi-view support. How critical is multi-view capability in practical applications, and what are the cost implications?
Key figure
Figure 1 illustrates the GROW2 framework, which decomposes open-world affordance grounding into semantic selection and 3D geometric grounding.