Overview
Imagine2Real is a proposed zero-shot framework specifically designed for humanoid-object interaction (HOI). The framework aims to facilitate flexible, geometry-free interactions without relying on high-fidelity 3D data, which is identified as a bottleneck in whole-body HOI research. It addresses two primary challenges: representation misalignment and retargeting complexity.
Research Context
Whole-body Humanoid-Object Interaction (HOI) is constrained by the limited availability of high-fidelity 3D data. While video generative priors offer a potential alternative, existing methodologies encounter difficulties. These issues include Representation Misalignment, often stemming from dependence on geometric priors like explicit CAD models, and Retargeting Complexity, a consequence of intensive morphing and morphological inconsistencies between different representations.
Approach
Imagine2Real employs several mechanisms to mitigate the identified challenges:
- Representation Misalignment Resolution: To overcome misalignment, the framework formulates both robot and object motions as unified 4D point trajectories. This approach aims to provide a consistent representation across different interactive components.
- Retargeting Complexity Mitigation: The system utilizes a Keypoints Tracker, which specifically monitors sparse critical points. These critical points include the base, hands, and the interacting object. This focused tracking strategy is intended to bypass the error-amplifying process typically associated with full retargeting.
- Natural Gait Maintenance: Despite relying on sparse signals from the Keypoints Tracker, Imagine2Real integrates the latent space of a Behavior Foundation Model (BFM). This latent space serves as the search domain for the tracker, which is designed to help maintain natural gaits during interactions.
- Progressive Training Strategy: The framework incorporates a progressive training strategy. This strategy is leveraged to enable the learning of robust behaviors using simple tracking rewards. This facilitates the zero-shot physical deployment of the learned behaviors within a motion capture (mocap) system.
Findings
The Imagine2Real framework is presented as a zero-shot HOI framework capable of achieving flexible, geometry-free interaction. By formulating robot and object motions as unified 4D point trajectories, it addresses representation misalignment. The Keypoints Tracker, focusing on sparse critical points, bypasses issues related to retargeting complexity. The use of a Behavior Foundation Model's latent space within the tracker's search domain is intended to maintain natural gaits despite sparse input. The progressive training strategy enables robust behavior learning through simple tracking rewards, allowing for zero-shot physical deployment in a mocap system.
Potential Applications
The framework supports zero-shot physical deployment within a motion capture (mocap) system, suggesting its applicability in environments where real-time interaction data can be utilized without prior specific training for each interaction scenario.