kPAM: KeyPoint Affordances for Category-Level Robotic Manipulation (1903.06684v2)

Abstract: We would like robots to achieve purposeful manipulation by placing any instance from a category of objects into a desired set of goal states. Existing manipulation pipelines typically specify the desired configuration as a target 6-DOF pose and rely on explicitly estimating the pose of the manipulated objects. However, representing an object with a parameterized transformation defined on a fixed template cannot capture large intra-category shape variation, and specifying a target pose at a category level can be physically infeasible or fail to accomplish the task -- e.g. knowing the pose and size of a coffee mug relative to some canonical mug is not sufficient to successfully hang it on a rack by its handle. Hence we propose a novel formulation of category-level manipulation that uses semantic 3D keypoints as the object representation. This keypoint representation enables a simple and interpretable specification of the manipulation target as geometric costs and constraints on the keypoints, which flexibly generalizes existing pose-based manipulation methods. Using this formulation, we factor the manipulation policy into instance segmentation, 3D keypoint detection, optimization-based robot action planning and local dense-geometry-based action execution. This factorization allows us to leverage advances in these sub-problems and combine them into a general and effective perception-to-action manipulation pipeline. Our pipeline is robust to large intra-category shape variation and topology changes as the keypoint representation ignores task-irrelevant geometric details. Extensive hardware experiments demonstrate our method can reliably accomplish tasks with never-before seen objects in a category, such as placing shoes and mugs with significant shape variation into category level target configurations.

• The paper introduces a novel keypoint-based strategy that overcomes pose estimation limits in handling intra-category object variations.
• The methodology integrates instance segmentation, 3D keypoint detection, optimization planning, and precise grasp execution to compute robotic actions.
• Empirical validation demonstrates centimeter-level accuracy and robust performance on varied objects, highlighting practical adaptability in real-world tasks.

KeyPoint Affordances for Category-Level Robotic Manipulation (kPAM)

The paper "kPAM: KeyPoint Affordances for Category-Level Robotic Manipulation" introduces a novel approach to robotic manipulation at the category level, using semantic 3D keypoints to represent objects. This implementation allows for flexible configurations, capable of adapting to the manipulation's target geometric constraints. This work is a contribution by researchers from CSAIL, Massachusetts Institute of Technology, presenting a general and effective perception-to-action manipulation pipeline.

Conceptual Framework

Traditional manipulation pipelines rely on estimating a target 6-DOF pose. This approach, although widely used, struggles with objects exhibiting significant intra-category variation and topology change. kPAM addresses this limitation by adopting a framework built on key semantic 3D keypoints, diverging from the canonical template-based pose estimation. This keypoint-based representation enables task specification through geometric costs and constraints, thus allowing for more interpretable and flexible problem formulations.

Methodology

The kPAM pipeline is structured to factor the manipulation process into four discrete stages:

1. Instance Segmentation: Automated object segmentation in an RGB-D scene using Mask Region-based Convolutional Neural Networks (Mask R-CNN).
2. 3D Keypoint Detection: Utilizing integral networks for precise keypoint detection from RGB-D inputs.
3. Optimization-based Planning: Solving an inverse kinematics problem to determine the robot action as a rigid transformation ($T_{\text{action}}$), specified through cost and constraint functions on the detected keypoints.
4. Robotic Grasping and Execution: Applying grasping algorithms to initiate physical interaction, transferring the computed $T_{\text{action}}$ effectively to real-world tasks.

Empirical Validation

The authors present extensive hardware experiments to demonstrate the robustness of the kPAM approach for real-world scenarios involving significant object shape variation. Tasks such as placing varied shoes on racks and mugs on shelves or racks are used to highlight the method's adaptability. Intriguingly, the system was able to generalize to unseen instances within a category, achieving high reliability and precision through centimeter-level target configurations.

Comparative Analysis

Compared to traditional pose-based methods, the kPAM approach offers several advantages. In the context of objects with substantial intra-class variation, the traditional methods fall short due to their reliance on parameterized transformations anchored to a static template. The keypoint representation circumvents this by focusing only on task-critical segments of the object, thus eliminating ambiguity in configuration changes. Both in principle and practice, this keypoint approach appears to generalize more effectively to new object instances within a given category.

Implications and Future Directions

The implications of kPAM's findings are significant for the development of robots capable of adaptive, category-level interactions with diverse objects. Its robust handling of shape variation and precise, adaptable targeting highlights potential applications in industrial automation and service robotics. While the current work focuses on rigid body dynamics, future iterations could integrate deformable object manipulation, further expanding the practical utility of this framework.

In conclusion, kPAM introduces a nuanced alternative to pose-based manipulation strategies, broadening the practical capabilities of robotic systems. Future advancements may explore integration with learning-based paradigms to further refine this approach, facilitating more nuanced interaction tasks across a broader spectrum of scenarios.