TACO: Benchmarking Generalizable Bimanual Tool-ACtion-Object Understanding (2401.08399v2)

Abstract: Humans commonly work with multiple objects in daily life and can intuitively transfer manipulation skills to novel objects by understanding object functional regularities. However, existing technical approaches for analyzing and synthesizing hand-object manipulation are mostly limited to handling a single hand and object due to the lack of data support. To address this, we construct TACO, an extensive bimanual hand-object-interaction dataset spanning a large variety of tool-action-object compositions for daily human activities. TACO contains 2.5K motion sequences paired with third-person and egocentric views, precise hand-object 3D meshes, and action labels. To rapidly expand the data scale, we present a fully automatic data acquisition pipeline combining multi-view sensing with an optical motion capture system. With the vast research fields provided by TACO, we benchmark three generalizable hand-object-interaction tasks: compositional action recognition, generalizable hand-object motion forecasting, and cooperative grasp synthesis. Extensive experiments reveal new insights, challenges, and opportunities for advancing the studies of generalizable hand-object motion analysis and synthesis. Our data and code are available at https://taco2024.github.io.

• The paper presents TACO, a large-scale dataset featuring 2.5K motion sequences that span 15 actions and 131 tool-action-object combinations.
• It employs a fully-automatic data acquisition pipeline with multi-view sensing and optical motion capture for high-quality 3D hand-object reconstructions.
• Benchmarking tasks on compositional action recognition, motion forecasting, and grasp synthesis expose model limitations in generalizing to unseen geometric variations.

Overview of the TACO Dataset for Bimanual Tool-action-object Understanding

The paper introduces the TACO (Tool-Action-Object) dataset, an extensive collection designed to enhance the understanding of generalizable bimanual hand-object interactions in complex tool-based activities. This dataset addresses limitations in the current landscape of hand-object interaction (HOI) studies by providing a comprehensive suite of real-world scenarios that feature diverse and intricate interactions among tools, objects, and bimanual manipulation tasks.

Contributions and Methodology

The TACO dataset is a significant resource in the domain of computer vision and robotics due to its scale and the richness of its data. Key contributions include:

1. Scale and Diversity: TACO is composed of 2.5K motion sequences that cover 15 different actions across 131 tool-action-object combinations, featuring 196 unique 3D object models. This vast array of possibilities supports the investigation of generalization in action recognition and interaction synthesis across new tool types and behaviors.
2. Automatic Data Acquisition Pipeline: The dataset was created using a fully-automatic data acquisition pipeline integrating multi-view sensing and optical motion capture. This setup provides high-quality 3D hand-object mesh reconstructions and detailed segmentation annotations, enhancing the fidelity of the data and enabling robust benchmarking.
3. Benchmarking and Insights: The paper benchmarks three core tasks: compositional action recognition, generalizable hand-object motion forecasting, and cooperative grasp synthesis. These benchmarks expose current algorithms to test-time generalization scenarios involving unseen object geometries and new interaction combinations, providing insights into the challenges and limitations of existing models.

Practical and Theoretical Implications

Practically, TACO offers a valuable dataset for training models in various application areas, including VR/AR, human-robot interaction, and dexterous manipulation, where understanding nuanced bimanual coordination is critical. The benchmarking results highlight opportunities to improve model architectures to better handle the complexities of real-world tool-use scenarios, particularly in terms of generalization abilities.

Theoretically, the dataset prompts further exploration into the principles of hand-object interaction mechanics, encouraging the development of more sophisticated models capable of understanding and predicting human actions in diverse contexts. It also stimulates discussions around the synthesis of realistic bimanual motions, posing unmet challenges in collision avoidance, contact realism, and dynamic adaptability.

Future Directions

Looking forward, TACO sets a foundation for several exciting avenues in AI and robotics research:

• Enhanced Generalization Techniques: Further studies could explore few-shot or zero-shot learning techniques to improve model adaptability to new objects and actions.
• Integration with Physics-based Models: Incorporating physical simulation data could augment the reality and applicability of synthesized interactions, promoting models capable of safe and efficient tool-use in robotics.
• Augmented Interaction Contexts: Extending datasets to include more complex environments and articulated objects could significantly enhance the practical utility of learned models.

In conclusion, TACO represents a pivotal advancement in hand-object interaction research, offering a comprehensive toolset for exploring generalizable interaction models and inspiring future developments in AI-driven understanding of human dexterity.