OakInk: A Large-scale Knowledge Repository for Understanding Hand-Object Interaction (2203.15709v1)

Abstract: Learning how humans manipulate objects requires machines to acquire knowledge from two perspectives: one for understanding object affordances and the other for learning human's interactions based on the affordances. Even though these two knowledge bases are crucial, we find that current databases lack a comprehensive awareness of them. In this work, we propose a multi-modal and rich-annotated knowledge repository, OakInk, for visual and cognitive understanding of hand-object interactions. We start to collect 1,800 common household objects and annotate their affordances to construct the first knowledge base: Oak. Given the affordance, we record rich human interactions with 100 selected objects in Oak. Finally, we transfer the interactions on the 100 recorded objects to their virtual counterparts through a novel method: Tink. The recorded and transferred hand-object interactions constitute the second knowledge base: Ink. As a result, OakInk contains 50,000 distinct affordance-aware and intent-oriented hand-object interactions. We benchmark OakInk on pose estimation and grasp generation tasks. Moreover, we propose two practical applications of OakInk: intent-based interaction generation and handover generation. Our datasets and source code are publicly available at https://github.com/lixiny/OakInk.

• The paper introduces a dual knowledge repository combining object affordance data and human interaction records to enhance understanding of hand-object dynamics.
• It presents the Tink framework, which accurately transfers real-world interactions to virtual settings using neural generative modeling and iterative contact refinement.
• Benchmarked for pose estimation and grasp generation, OakInk demonstrates significant potential for advancing robotic manipulation and realistic human-interaction modeling.

OakInk: A Comprehensive Knowledge Repository for Hand-Object Interaction

The paper "OakInk: A Large-scale Knowledge Repository for Understanding Hand-Object Interaction" presents an extensive dataset and accompanying frameworks aimed at advancing the understanding of human hand-object interactions in both computer vision and robotics. The authors have identified two key components necessary for machines to effectively mimic human object manipulation: understanding object affordances and capturing human interactions based on these affordances. Despite the existence of hand-object interaction datasets, the authors argue for a more comprehensive repository that better encapsulates these components.

Key Contributions

1. Dual Knowledge Bases:

The OakInk repository is built upon two fundamental knowledge bases:

• Object Affordance Knowledge Base (Oak): This base involves the collection of over 1,800 everyday objects, annotated with affordance information. A knowledge graph is constructed to categorize objects through taxonomy and descriptive attributes.
• Interaction Knowledge Base (Ink): Human interactions with a selected subset of objects from Oak are recorded and transferred to virtual environments via a method named Tink. This base emphasizes the diversity of human interaction in accordance with object affordance.

2. Richly Annotated Dataset:

OakInk incorporates over 50,000 distinct hand-object interactions, providing a valuable resource for tasks such as pose estimation and grasp generation. The dataset includes detailed annotations about hand and object poses, affordances, contact regions, and interaction intents.

3. Benchmarking and Applications:

The paper benchmarks OakInk in pose estimation and grasp generation, proposing new interaction paradigms such as intent-based interaction generation and handover scenarios. This benchmarking underscores the dataset's utility in both academic research and practical application domains.

Methodological Innovations

Tink Framework:

Tink is a novel methodology for transferring recorded interactions onto virtual object counterparts, maintaining plausibility concerning contact, pose, and intent. The method involves shape interpolation through a neural generative model, contact mapping through iterative consistency checkpoints, and pose refinement, leveraging contact and anatomical constraints.

Implications and Future Directions

The OakInk dataset significantly enhances the potential for developing sophisticated machine interaction models with object affordance and human interaction capabilities central to understanding. In practical terms, this dataset can facilitate advancements in robotic manipulation, enabling robots to perform intricate tasks that require nuanced interaction with varied objects.

Theoretically, OakInk provides a robust platform for exploring deeper questions in the cognitive and perceptual processes involved in human-object interactions. The Tink framework introduces a scalable approach for interaction transfer, which could be adapted to other domains requiring knowledge transfer between heterogeneous entities.

Looking ahead, applications of OakInk can be extended to include more dynamic and complex interactions, perhaps even incorporating multi-modal sensory data. Future research could explore integrating tactile feedback or emotional cues into hand-object interaction models, leading to more holistic systems capable of mimicking human dexterity and adaptability in real-world scenarios.

In conclusion, the OakInk repository is not only a considerable stride toward comprehensive hand-object interaction datasets but also sets a foundation for future research and innovation in machine learning, computer vision, and robotics centered around human-object interaction models.