KALIE: Fine-Tuning Vision-Language Models for Open-World Manipulation without Robot Data (2409.14066v1)

Abstract: Building generalist robotic systems involves effectively endowing robots with the capabilities to handle novel objects in an open-world setting. Inspired by the advances of large pre-trained models, we propose Keypoint Affordance Learning from Imagined Environments (KALIE), which adapts pre-trained Vision LLMs (VLMs) for robotic control in a scalable manner. Instead of directly producing motor commands, KALIE controls the robot by predicting point-based affordance representations based on natural language instructions and visual observations of the scene. The VLM is trained on 2D images with affordances labeled by humans, bypassing the need for training data collected on robotic systems. Through an affordance-aware data synthesis pipeline, KALIE automatically creates massive high-quality training data based on limited example data manually collected by humans. We demonstrate that KALIE can learn to robustly solve new manipulation tasks with unseen objects given only 50 example data points. Compared to baselines using pre-trained VLMs, our approach consistently achieves superior performance.

An Overview of KALIE: Enhancing Vision-LLMs for Open-World Robotic Manipulation

The paper presents a novel approach, Keypoint Affordance Learning from Imagined Environments (KALIE), which addresses the challenge of leveraging Vision-LLMs (VLMs) for robotic manipulation without the need for direct robotic data. The work is anchored in the aspiration to develop generalist robotic systems capable of handling an open set of tasks, diversifying beyond the constrained traditional datasets that limit generalization capabilities.

Key Contributions

KALIE posits that the primary mechanism for improving robotic control is through the fine-tuning of VLMs to predict point-based affordance representations. This method bypasses the intricacies and substantial effort required to collect large-scale robotic datasets, leveraging VLMs' pre-trained visual and language understanding.

A central innovation of KALIE is its affordance-aware data synthesis pipeline. This pipeline uses limited example data collected by humans to automatically generate extensive synthetic data. By doing so, it ensures that the training data encompasses diverse scenarios while preserving the task semantics and associated keypoint annotations. The context for object geometries, critical for maintaining task faithfulness during data synthesis, is derived using a pre-trained diffusion model with additional context from the task environment.

Methodology and Key Insights

The authors fine-tune a pre-trained VLM using both real and synthesized data. They compare two design options for affordance prediction: a regression-based approach and a natural language-based approach for keypoint prediction. The latter aligns more closely with the functioning of VLMs and showed comparable performance.

The adaptability of KALIE is evaluated through tasks including tool use, manipulation of articulated objects, and deformable objects across unseen environments. Remarkably, KALIE consistently outperforms baseline models that rely on zero-shot transfer learning from VLMs. The system’s effectiveness is rooted in its ability to generate high-precision data for fine-tuning, substantiating the integration of well-structured data synthesis into robot learning frameworks.

Numerical Results and Implications

KALIE achieved significantly superior performance in tasks such as table sweeping, drawer operation, and trowel pouring, with success rates consistently above those of baseline methods. This underscores the potential for VLM integration in robotic systems, expanding the boundary of task complexity manageable in open-set environments.

Theoretical and Practical Implications

Practically, KALIE illustrates the value of repurposing large-scale models with minimal additional manual data collection, potentially reducing labor and cost constraints in robotic learning. Theoretically, the paper contributes to the ongoing discourse on the scalability of pre-trained models in robotics, providing a framework that bridges vision-language understanding with robotic affordance learning.

Future Directions in AI

Future work could extend KALIE to dynamic and multi-agent environments, leveraging the adaptability of its data synthesis process. There is also scope to explore zero-shot task generalization, which would require integration with broader datasets during synthesis to account for an even more extensive variety of tasks. Moreover, improving the fidelity and complexity of the synthesized scenarios could further enhance the generalization capabilities of the fine-tuned models.

In conclusion, KALIE offers a scalable and practical framework for leveraging pre-trained VLMs in robotic manipulation, highlighting synergies between data synthesis and affordance prediction in achieving robust task execution across diverse environments. This work marks a significant step towards more autonomous and generalist robotic systems.