AffordPose: A Large-scale Dataset of Hand-Object Interactions with Affordance-driven Hand Pose (2309.08942v1)

Abstract: How human interact with objects depends on the functional roles of the target objects, which introduces the problem of affordance-aware hand-object interaction. It requires a large number of human demonstrations for the learning and understanding of plausible and appropriate hand-object interactions. In this work, we present AffordPose, a large-scale dataset of hand-object interactions with affordance-driven hand pose. We first annotate the specific part-level affordance labels for each object, e.g. twist, pull, handle-grasp, etc, instead of the general intents such as use or handover, to indicate the purpose and guide the localization of the hand-object interactions. The fine-grained hand-object interactions reveal the influence of hand-centered affordances on the detailed arrangement of the hand poses, yet also exhibit a certain degree of diversity. We collect a total of 26.7K hand-object interactions, each including the 3D object shape, the part-level affordance label, and the manually adjusted hand poses. The comprehensive data analysis shows the common characteristics and diversity of hand-object interactions per affordance via the parameter statistics and contacting computation. We also conduct experiments on the tasks of hand-object affordance understanding and affordance-oriented hand-object interaction generation, to validate the effectiveness of our dataset in learning the fine-grained hand-object interactions. Project page: https://github.com/GentlesJan/AffordPose.

• The paper introduces AffordPose, a large-scale dataset with 26.7K hand-object interactions and 13 object categories, uniquely featuring part-level affordance labels.
• Statistical analysis revealed that hand poses exhibit both distinctive characteristics tied to specific affordances and universal patterns across object categories.
• Experiments show AffordPose improves affordance prediction accuracy and enables generating functionality-driven hand poses, benefiting robotics and HCI applications.

Analyzing the AffordPose Dataset: Implications and Applications for Hand-Object Interactions

This essay reviews the paper focusing on AffordPose, a dataset engineered to explore the intricate dynamics of hand-object interactions driven by affordances. The research introduces a comprehensive collection of data highlighting the functional implications of objects and the corresponding hand poses required to manifest these interactions. AffordPose contextualizes hand-object interactions not merely as geometric executions but rooted in the affordance, significantly contributing to an enriched understanding of robotic manipulation and human-computer interaction.

Main Contributions and Dataset Insights

The paper presents AffordPose as a pioneering dataset, compiling 26.7K interactions involving 641 objects across 13 categories with specified affordances. This large-scale dataset diverges from traditional ones by not only focusing on the mechanical aspect of interactions but providing part-level affordance labels that guide the localization and purpose of hand-object interactions. The eight affordance types—handle-grasp, press, lift, pull, twist, wrap-grasp, support, and lever—are meticulously annotated to reflect how diverse object functionalities affect and correspond to the detailed arrangement of hand poses.

The statistical analysis offers enlightening empirical insights into the affordance-driven characteristics of hand poses, including:

1. Distinctive Characteristics Across Affordances: Representative hand poses, highlighting commonalities and unique traits per affordance, demonstrate significant variations, notably in pinching for pull or curling for lift.
2. Universal Patterns and Diversity: While unique, affordances exhibit certain universal patterns across object categories, with differences in intrinsic hand joint configurations and interaction diversities reflecting individual habits or ergonomic practices.
3. Quantitative Metrics Analysis: Contact frequency and standard deviation analyses provide a fundamental understanding of how hand-object interaction specifics, like joint movements, correspond to varied affordances, supporting the dataset's applicability in prediction models.

Experimental Evaluations and Applications

AffordPose serves as a robust foundation for testing hand-object affordance understanding and affordance-oriented interaction generation. The experiments yielded noteworthy results:

• Affordance Prediction and Localization: High accuracy and IoU metrics affirm the dataset’s utility in guiding interactions effectively through labeled affordances. Importantly, leveraging all hand pose parameters, rather than just intrinsic ones, enhances prediction accuracy.
• Affordance-oriented Interaction Generation: The paper demonstrates the superiority of AffordPoseNet over conventional models like GrabNet. By conditionally generating hand poses from object and affordance inputs, it achieves highly specific, functionality-driven interaction arrangements that can inform future manipulative tasks in AI and robotics.
• RGB-Based Applications: AffordPose further supports image-based interaction analysis and mesh recovery, showcasing real-world applicability in augmented reality and human-computer interfaces.

Implications for Robotics and Future Directions

The findings suggest crucial implications for the fields of AI and robotics:

• Enhanced Human-Robot Interaction: The dataset's focus on the semantic meaning and functionality of hand-object interactions, facilitated by affordance-driven data, refines how robots could learn from human affordances for task-oriented actions.
• Development in Dexterous Manipulation: With its emphasis on fine-grained and pragmatic interactions, AffordPose is poised to facilitate improvements in dexterous manipulation and intelligent prosthetics, expanding the field of robotic capabilities beyond basic grasping tasks.
• Future Research Avenues: Prospective avenues include the integration of dynamic interaction datasets reflecting sequential and cooperative hand-object tasks, further enhancing the representation and simulation of complex, multi-phase procedures.

AffordPose epitomizes a refined perspective on hand-object interaction by associating mechanical movement with purposeful actions, promoting a deeper understanding of functionality in AI systems. The research paves the way for purchasing substantial groundwork on affordance-driven environments, potentially revolutionizing the trajectory of robotic design and human-computer interaction methodologies.