USEEK: Unsupervised SE(3)-Equivariant 3D Keypoints for Generalizable Manipulation (2209.13864v2)

Abstract: Can a robot manipulate intra-category unseen objects in arbitrary poses with the help of a mere demonstration of grasping pose on a single object instance? In this paper, we try to address this intriguing challenge by using USEEK, an unsupervised SE(3)-equivariant keypoints method that enjoys alignment across instances in a category, to perform generalizable manipulation. USEEK follows a teacher-student structure to decouple the unsupervised keypoint discovery and SE(3)-equivariant keypoint detection. With USEEK in hand, the robot can infer the category-level task-relevant object frames in an efficient and explainable manner, enabling manipulation of any intra-category objects from and to any poses. Through extensive experiments, we demonstrate that the keypoints produced by USEEK possess rich semantics, thus successfully transferring the functional knowledge from the demonstration object to the novel ones. Compared with other object representations for manipulation, USEEK is more adaptive in the face of large intra-category shape variance, more robust with limited demonstrations, and more efficient at inference time.

• The paper presents an unsupervised framework that discovers SE(3)-equivariant 3D keypoints, enabling generalizable manipulation from a single demonstration.
• It employs a teacher-student architecture to generate pseudo ground-truth labels for training on raw 3D point clouds, achieving superior mIoU performance over baselines.
• USEEK achieves high inference speed and robust pick-and-place manipulation in both simulated and real-world environments, marking a significant advance in robotic systems.

An Analysis of USEEK: Unsupervised SE(3)-Equivariant 3D Keypoints for Generalizable Manipulation

The paper introduces USEEK, a novel method that leverages unsupervised SE(3)-equivariant 3D keypoints to enable generalizable robotic manipulation. This research tackles the formidable challenge of manipulating unseen objects in arbitrary poses using a single demonstration of a grasping pose on an object instance. USEEK proposes to employ a teacher-student framework to achieve the desired properties of keypoints, facilitating efficient, invariant, and category-level generalization in robotic tasks.

Key Contributions and Methodology

USEEK's foundational innovation lies in its unsupervised framework to discover keypoints, which are integral for object manipulation. The method emphasizes four critical properties for keypoints:

1. Anti-occlusion: Ensuring repeatability despite self-occlusion.
2. Unsupervised: Avoiding the biases and costs associated with human annotations.
3. Aligned across instances: Preserving semantic correspondence across objects within a category.
4. SE(3)-Equivariant: Achieving equivariance with respect to translations and rotations of objects in 3D space.

The paper details a teacher-student architecture where the teacher network provides pseudo ground-truth labels to train the SE(3)-equivariant student network. This design choice effectively decouples the task of unsupervised keypoint discovery from ensuring equivariance. Through this mechanism, the student network can operate on raw 3D point clouds, accentuating the method's robustness and scalability.

Experimental Evaluation

The paper's empirical validation is twofold: (1) Assessment of the semantic integrity and invariance of the detected keypoints, and (2) Application of USEEK in robotic manipulation tasks. Extensive experiments on the SE(3) KeypointNet dataset showcased USEEK's superior mIoU scores, indicating adeptness in detecting semantically meaningful keypoints. USEEK outperformed state-of-the-art baselines, including ISS and NDF, demonstrating its robustness to SE(3) transformations and large intra-category shape variance.

In practical manipulation tasks, USEEK enabled robots to effectively execute pick-and-place maneuvers in simulated and real-world environments, boasting significant improvements in success rates over comparators. Crucially, USEEK achieved these outcomes with remarkable inference speed, underscoring its potential for real-time applications.

Implications and Future Directions

USEEK presents profound implications for the field of robotic manipulation, particularly in its ability to generalize from minimal demonstrations. This capability is pivotal as it mitigates the reliance on exhaustive sample training, thus broadening the applicability of robots in dynamic and unstructured environments. From a theoretical perspective, the research advances SE(3)-equivariant neural network design, promoting broader adoption in fields demanding spatial invariance.

Looking ahead, the application of USEEK to more complex manipulation tasks and its integration with more advanced perception systems could extend its utility. Further exploration into optimizing keypoint detection under challenging environmental conditions, such as high occlusion levels and reflective surfaces, can enhance robustness, making USEEK an even more versatile tool in the toolkit of robotic systems.

In conclusion, USEEK marks a significant stride in the development of unsupervised methods for robotic manipulation, offering a promising path toward more adaptable and efficient robotic capabilities in varied operational contexts.