ClickDiff: Click to Induce Semantic Contact Map for Controllable Grasp Generation with Diffusion Models (2407.19370v1)

Abstract: Grasp generation aims to create complex hand-object interactions with a specified object. While traditional approaches for hand generation have primarily focused on visibility and diversity under scene constraints, they tend to overlook the fine-grained hand-object interactions such as contacts, resulting in inaccurate and undesired grasps. To address these challenges, we propose a controllable grasp generation task and introduce ClickDiff, a controllable conditional generation model that leverages a fine-grained Semantic Contact Map (SCM). Particularly when synthesizing interactive grasps, the method enables the precise control of grasp synthesis through either user-specified or algorithmically predicted Semantic Contact Map. Specifically, to optimally utilize contact supervision constraints and to accurately model the complex physical structure of hands, we propose a Dual Generation Framework. Within this framework, the Semantic Conditional Module generates reasonable contact maps based on fine-grained contact information, while the Contact Conditional Module utilizes contact maps alongside object point clouds to generate realistic grasps. We evaluate the evaluation criteria applicable to controllable grasp generation. Both unimanual and bimanual generation experiments on GRAB and ARCTIC datasets verify the validity of our proposed method, demonstrating the efficacy and robustness of ClickDiff, even with previously unseen objects. Our code is available at https://github.com/adventurer-w/ClickDiff.

• The paper introduces a novel Semantic Contact Map (SCM) framework to overcome contact ambiguity in grasp generation.
• It employs a dual generation approach that integrates Semantic and Contact Conditional Modules for improved accuracy and user control.
• Experimental results on GRAB and ARCTIC datasets demonstrate reduced contact errors and enhanced realism for AR, VR, and robotic applications.

Analysis of ClickDiff for Controllable Grasp Generation with Diffusion Models

The research paper titled "ClickDiff: Click to Induce Semantic Contact Map for Controllable Grasp Generation with Diffusion Models" delineates a novel approach to the task of grasp generation using diffusion models, specifically designed to transcend the limitations observed in traditional methods—namely, contact ambiguity and imprecision in hand-object interactions. This paper introduces a new framework that leverages the Semantic Contact Map (SCM), demonstrating robust capabilities in generating precise, controllable grasps on objects even when presented with previously unseen objects.

The core innovation presented is the Semantic Contact Map, a fine-grained contact representation method that captures the essential details of hand-object interactions in a manner conducive to user control and customization. Through SCM, users can specify which object points and finger sections are in contact, mitigating the contact ambiguity typically inherent in models that rely solely on conventional contact maps. Such fine control facilitates more accurate modeling of physical interactions with objects, addressing the unreliability of traditional generation techniques.

The authors propose a Dual Generation Framework, integrating the Semantic Conditional Module and the Contact Conditional Module. The Semantic Conditional Module generates contact maps using either user-specified or predicted SCMs. Meanwhile, the Contact Conditional Module synthesizes realistic hand grasps by leveraging both the SCM and the generated contact maps. This two-stage framework enhances control and accuracy, handling the intricacies of physical structure interactions better than past efforts, evidenced by their testing on GRAB and ARCTIC datasets.

In evaluating the model's performance on various metrics like Mean Per-Joint Position Error (MPJPE), Contact Deviation (CDev), and Success Rate, ClickDiff outperformed state-of-the-art models such as GrabNet, GOAL, and ContactGen. Not only does it demonstrate superior precision and realism in generated grasps, but it also illustrates effectiveness in the bimanual hand-object interactions domain, validating its capabilities across broader and more complex datasets like ARCTIC.

Moreover, the introduction of the Tactile-Guided Constraint (TGC) postulates an approach to address contact point alignment during generation, utilizing the SCM to reduce dimensional ambiguities in 3D space. This innovation results in a significant reduction of 'off-target' grasp placements, a notable problem seen in other methodologies.

This framework has important practical implications, namely in the realms of augmented reality (AR) and virtual reality (VR), where precise hand-object interactions are imperative for realism. It also holds significant potential in advancing robotic manipulation systems, offering robots the capability to navigate complex interaction spaces with the nuanced understanding of human-like grasping.

Going forward, exploration into further refining the technique to reduce computational costs could expand ClickDiff's applicability. Moreover, integrating broader sensory inputs (e.g., tactile feedback) could propel the fidelity of hand-object interactions further, providing a more immersive experience.

In summary, ClickDiff represents a commendable advancement in the domain of controllable grasp generation, offering a frictionless way to integrate human interactivity with machine learning models. By focusing on the controllability and preciseness of contact representation, this paper demonstrates insightful methodologies that pave the way for future explorations in AI-driven interaction modeling.