Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation (2112.05124v1)

Abstract: We present Neural Descriptor Fields (NDFs), an object representation that encodes both points and relative poses between an object and a target (such as a robot gripper or a rack used for hanging) via category-level descriptors. We employ this representation for object manipulation, where given a task demonstration, we want to repeat the same task on a new object instance from the same category. We propose to achieve this objective by searching (via optimization) for the pose whose descriptor matches that observed in the demonstration. NDFs are conveniently trained in a self-supervised fashion via a 3D auto-encoding task that does not rely on expert-labeled keypoints. Further, NDFs are SE(3)-equivariant, guaranteeing performance that generalizes across all possible 3D object translations and rotations. We demonstrate learning of manipulation tasks from few (5-10) demonstrations both in simulation and on a real robot. Our performance generalizes across both object instances and 6-DoF object poses, and significantly outperforms a recent baseline that relies on 2D descriptors. Project website: https://yilundu.github.io/ndf/.

• The paper presents a novel SE(3)-equivariant framework that generalizes object poses for efficient robotic manipulation.
• It employs neural implicit functions and occupancy-based networks to capture multi-scale, task-agnostic geometric features.
• Extensive experiments demonstrate improved handling of diverse object orientations while reducing manual keypoint annotation.

Neural Descriptor Fields: SE-Equivariant Object Representations for Manipulation

The paper presents "Neural Descriptor Fields" (NDFs), a novel object representation framework specifically designed for addressing the challenges in robotic manipulation tasks. The framework encodes category-level descriptors which amalgamate the spatial relationship between an object and target manipulators, such as robot grippers. Targeted at experienced researchers in robotic manipulation and geometric deep learning, the work leverages the structural regularities inherent in object categories to efficiently transfer manipulation tasks from a limited set of demonstrations to novel object instances. The critical innovation of NDFs lies in their SE-equivariant nature, enabling the frameworks to generalize object poses characterized by arbitrary transformations in 3D space.

Overview

NDFs stand out through their reliance on SE-equivariant object descriptors that stabilize across all translations and rotations within 3D configurations. The representation is constructed using neural networks trained to perform 3D reconstruction tasks, which ensures descriptors capture task-agnostic geometric structures without the need for extensive keypoint annotations. By parametrizing a neural implicit function, the authors translate this learning into a manipulation context, allowing it to solve for manipulation task applications by directly optimizing pose descriptors.

Methodology

NDFs leverage a novel SE-equivariant neural network architecture which generalizes across 3D spatial transformations. This design is achieved through the representation of 3D spatial coordinates as neural implicit functions that encode descriptors, facilitating precise spatial relationships across differing object instances and configurations. Specifically, spatial features are encoded using occupancy-based neural networks that generate hierarchical activations—each corresponding to different layers of the network, encapsulating multi-scale object information critical for manipulation.

Additionally, NDFs effectively employ query points configured to capture relevant object features that are task-specific, allowing for the mapping of equivalent poses across different object instances. This contrasts with methods relying purely on individually detected keypoints, thus improving the robustness and precision required for manipulating novel objects.

Practical Implications and Results

Through extensive experimentation, the effectiveness of NDFs is thoroughly validated. When tested in both simulated and real environments, and across various manipulation tasks such as mug handling and bowl placement, NDFs consistently showcased their strength in transitioning learned grasp and place tasks to novel instances. This was evidenced by the marked performance improvement over 2D-based descriptors, particularly in scenarios involving non-standard object orientations.

The results indicate that NDFs are well-equipped to handle robotic manipulation in less controlled settings, reducing the dependency on manual keypoint annotation and extensive training data, which are standard in current manipulation paradigms. This ensures quicker deployment times and increased adaptability in practical applications.

Theoretical Implications and Future Directions

From a theoretical perspective, the approach expands on the integration of geometric deep learning principles, emphasizing the benefits of equivariance in robotic perception tasks. Moreover, the adoption of neural implicit fields suggests a path for enhancing object recognition models by embedding spatial relationships that maintain consistency across a rich variety of observational scenarios.

Looking ahead, the development of NDFs opens avenues for incorporating dynamic object interactions and trajectory planning within this framework. Extending beyond rigid objects, further research may incorporate the handling of non-rigid objects using the insights provided by recent work on non-rigid neural representations. Additionally, synergizing NDFs with trajectory optimization could advance the capabilities of robotic systems in complex multi-step tasks, further establishing NDFs as a toolset for efficient robotic learning.

In conclusion, Neural Descriptor Fields position themselves as a compelling solution to generalizable robotic manipulation by ensuring that manipulation learned from sparse demonstrations can be effectively applied across uncharted object configurations, supporting both near-term robotic deployment and future research explorations in intelligent robotic systems.