Related Work
Recent advancements in dexterous grasp planning have been largely propelled by data-driven approaches.
Implementation Details
Computational Efficiency
A significant advantage of our object-agnostic formulation is the speed of data generation. The entire dataset creation required approximately 1 GPU hour on a single Nvidia RTX 4090. In contrast, a previous work reported a much longer generation time of 1,400 GPU hours using Nvidia A100.
Grasp Pose Constraints and Generation
A key advantage of this method is the flexibility to impose the object’s pose. The object pose defines the object’s position and orientation relative to the gripper.
In a practical grasping scenario, the transformation between the gripper and the object is typically constrained by the task and the physical environment. Consequently, generating grasp poses that are not physically feasible is inefficient. Furthermore, objects with translational or rotational symmetries, such as a cylinder rotating around its axis of revolution, can be grasped effectively with a single pose inference, as it provides a broad range of valid grasp configurations for that object type.
Grasp Pose Generation Strategy
To address the need for comprehensive grasp coverage, particularly for objects lacking significant symmetry, we propose a strategy for generating grasp candidates. We sample gripper poses uniformly on the object’s convex hull, which is dilated by 110%. The gripper’s palm is oriented to point opposite to the hull’s normal vector. This approach ensures that the generated grasp poses are both diverse and well-distributed across the object’s surface.
Evaluation Metrics
Efficiency
We evaluate the efficiency of all models by calculating the average time required to generate a single grasp. This measurement is based on the total time to produce 100 grasps, including both the model inference and optimization phases. The resulting value is then normalized to a single grasp. The time for the Isaac Gym simulation is excluded from this metric.
Real-Robot Experiments
We conducted experiments using an Allegro Left Hand mounted on a 7-DoF robot arm. We successfully grasped 11 objects from the YCB Dataset, demonstrating the method’s ability to transfer to real-world objects. Successful grasps are illustrated in the paper, and the experimental setup is also shown. Videos of the experiments are provided in supplementary material.
Conclusion
This paper introduces GOAG, a novel learning paradigm for data-driven grasp planners. Our approach leverages the strengths of deep learning while eliminating the need for a large-scale, object-specific grasp database. By adopting a gripper-centric training phase that makes no assumptions about object shapes, our model learns a generalizable grasp strategy.
Extensive evaluations across multiple benchmarks demonstrate that our method achieves performance competitive with state-of-the-art approaches, even without training on their specific datasets. This highlights the strong generalization capabilities of our formulation, which we also validate with a successful real-robot deployment.
Frequently Asked Questions
What is GOAG? GOAG is a learning paradigm for data-driven grasp planners that uses an object-agnostic, gripper-centric training approach.
How efficiently was the GOAG dataset generated? The entire dataset was created in approximately 1 GPU hour on a single Nvidia RTX 4090.
How are grasp candidates generated? Gripper poses are sampled uniformly on a convex hull around the object, dilated by 110%, with the palm oriented opposite to the hull’s normal vector.
Was GOAG tested on a real robot? Yes. An Allegro Left Hand mounted on a 7-DoF robot arm successfully grasped 11 objects from the YCB Dataset.