HiFi-CS: Towards Open Vocabulary Visual Grounding For Robotic Grasping Using Vision-Language Models (2409.10419v1)

Abstract: Robots interacting with humans through natural language can unlock numerous applications such as Referring Grasp Synthesis (RGS). Given a text query, RGS determines a stable grasp pose to manipulate the referred object in the robot's workspace. RGS comprises two steps: visual grounding and grasp pose estimation. Recent studies leverage powerful Vision-LLMs (VLMs) for visually grounding free-flowing natural language in real-world robotic execution. However, comparisons in complex, cluttered environments with multiple instances of the same object are lacking. This paper introduces HiFi-CS, featuring hierarchical application of Featurewise Linear Modulation (FiLM) to fuse image and text embeddings, enhancing visual grounding for complex attribute rich text queries encountered in robotic grasping. Visual grounding associates an object in 2D/3D space with natural language input and is studied in two scenarios: Closed and Open Vocabulary. HiFi-CS features a lightweight decoder combined with a frozen VLM and outperforms competitive baselines in closed vocabulary settings while being 100x smaller in size. Our model can effectively guide open-set object detectors like GroundedSAM to enhance open-vocabulary performance. We validate our approach through real-world RGS experiments using a 7-DOF robotic arm, achieving 90.33\% visual grounding accuracy in 15 tabletop scenes. We include our codebase in the supplementary material.

HiFi-CS: Open Vocabulary Visual Grounding for Robotic Grasping Using Vision-LLMs

The paper "HiFi-CS: Towards Open Vocabulary Visual Grounding for Robotic Grasping Using Vision-LLMs" presents a comprehensive approach for improving the capabilities of Referring Grasp Synthesis (RGS) in robotic systems. RGS is a critical task in robotics, empowering robots to grasp objects based on textual descriptions. This task is accomplished through two key processes: visual grounding and grasp pose estimation. This paper introduces HiFi-CS, which enhances the visual grounding stage by leveraging Vision-LLMs (VLMs) in conjunction with Featurewise Linear Modulation (FiLM) to fuse image and text embeddings hierarchically. This approach improves the model's efficacy in interpreting complex and rich textual queries for robotic grasping.

Methodology

The core contribution of the paper is the HiFi-ClipSeg (HiFi-CS) model, which employs a lightweight decoder in combination with a frozen VLM. The decoder utilizes hierarchical FiLM to effectively merge vision-text embeddings, which is crucial for accurate visual grounding of complex text queries. The use of FiLM allows the model to retain semantic information by harmonizing image and text features in progressive layers, tailored for predicting pixel-wise segmentation in images.

HiFi-CS is tested under two scenarios: Closed Vocabulary and Open Vocabulary visual grounding. In the Closed Vocabulary setup, all scenarios contain pre-known object categories, while the Open Vocabulary evaluation assesses performance on previously unseen categories. This open vocabulary capability ensures that HiFi-CS can generalize its visual grounding across environments with novel object sets.

Numerical Results

HiFi-CS achieves significant advancements in scenario-specific visual grounding metrics, most notably outperforming existing methods in terms of Intersection Over Union (IoU) accuracy. In closed vocabulary contexts, HiFi-CS exhibits strong superiority with an IoU accuracy of 90.33% across various testing environments, demonstrating its robustness when managing complex object-related text queries. Importantly, HiFi-CS achieves these results while maintaining a model size that is 100x smaller than competitive models, emphasizing its computational efficiency.

The model demonstrates enhanced precision in recognizing and segmenting objects in cluttered environments, a frequent obstacle in robotic contexts. It achieves high precision across varying thresholds of IoU, indicating its reliability and precision in diverse indoor scenes.

Practical Implications

This research directly impacts the development of language-guided robotic manipulation, enhancing the interaction between humans and robots. By creating a more efficient and capable system for visual grounding, HiFi-CS can be integrated into practical robotic systems for task automation in dynamic environments. The 7-DOF robotic arm experiments specifically showcase HiFi-CS's applicability in real-world scenarios, where it successfully guided grasping tasks with high accuracy.

Theoretical Implications

From a theoretical perspective, the paper introduces a methodology to blend visual and linguistic inputs in a manner that retains semantic richness across modalities. This fusion at various stages within a neural network is a novel step that could be extrapolated to other multimodal machine learning tasks beyond visual grounding. The hierarchical strategy for feature fusion could be explored further in additional areas such as human-computer interaction and intelligent autonomous systems.

Future Directions

Looking forward, the potential enhancements in grasp synthesis await exploration. Integrating visual grounding with advanced grasp-estimation algorithms could push the boundary in end-to-end RGS tasks. Furthermore, adopting closed-loop feedback systems as discussed in the paper could significantly increase the accuracy of robotic grasping in real-time, dynamic environments. Additional exploration into the scalability of HiFi-CS across varying robotic platforms and configurations will further validate and expand its applicability.

The insights and architectures proposed by HiFi-CS open new avenues for both theoretical research and practical implementations, emphasizing the importance of sophisticated visual and linguistic perception for next-generation autonomous systems.