Generating Human-Like Grasps Aligned with Semantic Intentions

Core Concepts

SemGrasp generates human-like grasp poses that are aligned with linguistic intentions by incorporating semantic information into the grasp representation and leveraging a multimodal large language model.

Abstract

The paper introduces SemGrasp, a novel semantic-based grasp generation method that generates human grasp poses by incorporating semantic information into the grasp representation. The key highlights are: SemGrasp uses a discrete grasp representation that aligns the grasp space with the semantic space, enabling the generation of grasp postures in accordance with language instructions. The grasp representation is divided into three interrelated components: orientation, manner, and refinement. A multimodal large language model (MLLM) is fine-tuned to integrate object, grasp, and language within a unified semantic space, allowing the generation of grasps based on linguistic inputs. The authors compile a large-scale dataset named CapGrasp, which features detailed captions and diverse grasps aligned with semantic information, to facilitate the training of SemGrasp. Experiments demonstrate that SemGrasp efficiently generates natural human grasps that are aligned with linguistic intentions, outperforming baseline methods in terms of physical plausibility and semantic consistency. The authors also showcase the potential value of SemGrasp in AR/VR and embodied robotics applications by integrating it with reinforcement learning-based policies for dynamic grasp synthesis.

Stats

The paper does not provide specific numerical data or statistics. The key figures and metrics used to evaluate the performance of SemGrasp include: Mean Per-Vertex Position Error (MPVPE) Penetration Depth (PD) Solid Intersection Volume (SIV) Simulation Displacement (SD) Perceptual Score (PS) Fréchet Inception Distance (P-FID) GPT-4 assisted evaluation

Quotes

The paper does not contain any direct quotes that are particularly striking or support the key logics.

Key Insights Distilled From

SemGrasp

by Kailin Li,Ji... at arxiv.org 04-05-2024

https://arxiv.org/pdf/2404.03590.pdf

Deeper Inquiries

How can the discrete grasp representation be extended to support more complex hand-object interactions, such as two-handed manipulation

To extend the discrete grasp representation to support more complex hand-object interactions like two-handed manipulation, several key considerations need to be addressed. Firstly, the representation should be expanded to incorporate the coordination and synchronization of multiple hands. This could involve introducing additional tokens or components to capture the relative positions, orientations, and actions of each hand in the interaction. By discretizing the grasp information for each hand separately and then integrating them into a unified representation, the system can effectively model and generate complex two-handed manipulation scenarios. Additionally, the representation should account for the dynamic nature of two-handed interactions, allowing for the generation of coordinated and synchronized motions between the hands. By incorporating temporal information and constraints into the discrete representation, the system can generate continuous and coherent two-handed manipulation sequences.

What are the potential challenges and limitations in developing an end-to-end semantic grasp motion synthesis system that can generate continuous, dynamic grasp sequences

Developing an end-to-end semantic grasp motion synthesis system that can generate continuous, dynamic grasp sequences poses several challenges and limitations. One major challenge is the complexity of modeling and predicting the intricate and nuanced motions involved in dynamic grasp sequences. This requires the system to understand not only the semantic intent behind the grasp but also the dynamic interactions between the hand and the object over time. Additionally, ensuring the generated motions are physically plausible and biomechanically sound adds another layer of complexity. Another challenge is the availability of high-quality training data that captures a wide range of dynamic grasp sequences in various contexts. An extensive and diverse dataset is essential for training the system to generalize well and generate realistic and contextually appropriate grasp motions. Furthermore, the computational complexity of modeling and synthesizing dynamic grasp sequences in real-time poses a limitation, as it requires efficient algorithms and computational resources to handle the complexity of the task.

What other modalities or sources of information, beyond language and object geometry, could be leveraged to further enhance the semantic understanding and generation of human-like grasps

In addition to language and object geometry, several other modalities and sources of information can be leveraged to enhance the semantic understanding and generation of human-like grasps. One potential modality is tactile feedback, which provides valuable information about the contact forces, pressure distribution, and surface textures during grasping. Integrating tactile sensors or tactile feedback data into the grasp generation system can improve the realism and effectiveness of the generated grasps. Another modality is visual information, such as object appearance, context, and affordances. By incorporating visual cues from cameras or depth sensors, the system can better understand the object's properties, spatial relationships, and contextual relevance, leading to more contextually relevant and visually coherent grasp generation. Additionally, incorporating proprioceptive feedback, which includes information about the hand's position, orientation, and joint angles, can further enhance the system's ability to generate natural and human-like grasps. By integrating multiple modalities and sources of information, the semantic grasp generation system can achieve a more comprehensive and holistic understanding of the grasping task.