GenDexGrasp: Generalizable Dexterous Grasping (2210.00722v2)

Abstract: Generating dexterous grasping has been a long-standing and challenging robotic task. Despite recent progress, existing methods primarily suffer from two issues. First, most prior arts focus on a specific type of robot hand, lacking the generalizable capability of handling unseen ones. Second, prior arts oftentimes fail to rapidly generate diverse grasps with a high success rate. To jointly tackle these challenges with a unified solution, we propose GenDexGrasp, a novel hand-agnostic grasping algorithm for generalizable grasping. GenDexGrasp is trained on our proposed large-scale multi-hand grasping dataset MultiDex synthesized with force closure optimization. By leveraging the contact map as a hand-agnostic intermediate representation, GenDexGrasp efficiently generates diverse and plausible grasping poses with a high success rate and can transfer among diverse multi-fingered robotic hands. Compared with previous methods, GenDexGrasp achieves a three-way trade-off among success rate, inference speed, and diversity. Code is available at https://github.com/tengyu-liu/GenDexGrasp.

• The paper introduces a hand-agnostic grasping framework using a CVAE and contact maps to generate diverse grasp poses across multiple robotic hands.
• It overcomes previous limitations by achieving a balanced trade-off between grasp success (77.19%), inference speed (16.4 sec), and pose diversity.
• The method’s efficient design has promising implications for adaptive robotic manipulation in dynamic industrial environments.

Insights into GenDexGrasp: Generalizable Dexterous Grasping

The paper "GenDexGrasp: Generalizable Dexterous Grasping" presents an innovative approach to robotic grasping, focusing on enhancing generalizability across various robotic hand structures. The authors address two notable limitations of prior methods: specialization toward specific hand types and inefficiency in generating diverse grasps swiftly. Their proposed methodology, GenDexGrasp, integrates a hand-agnostic grasping algorithm with the synthesis of a multi-hand dataset named MultiDex, aimed at improving the generality and efficiency of grasp generation.

Technical Overview

GenDexGrasp adopts a novel algorithm that leverages contact maps as hand-agnostic intermediate representations. This innovative approach allows the efficient generation of diverse and plausible grasp poses with high success rates across multiple robotic hands. By utilizing a conditional variational autoencoder (CVAE), the system can generate these contact maps from large-scale multi-hand data, thereby overcoming the structural dependency issues present in prior methodologies.

To further enhance the algorithm's efficiency, the researchers introduce the concept of "aligned distance" to compute the distance between surface points of an object and a robotic hand. This method corrects the errors typically encountered in contact mapping, particularly for objects with thin-shell geometry, by factoring in both surface distance and directional alignment.

Numerical Findings

Significant findings of the paper reveal that the GenDexGrasp algorithm achieves a well-balanced three-way trade-off among success rate, inference speed, and diversity of grasping poses. The method successfully generated diverse grasping poses with a success rate of 77.19% for unseen ShadowHands, demonstrating its efficacy in real-world scenarios. It also maintains a competitive inference speed of approximately 16.4 seconds, notably faster than many existing hand-agnostic methods such as dfc \cite{liu2021synthesizing}, which take over an hour for a single instance.

Implications and Future Work

The successful implementation of GenDexGrasp indicates promising potential applications in environments necessitating adaptive robotic manipulation. This advancement holds particular significance for industries where robotic systems must handle objects with minimal manual input or preconfiguration, such as e-commerce package handling or autonomous assembly lines.

From a theoretical perspective, GenDexGrasp contributes to the broader understanding and development of hand-agnostic systems within robotics, showcasing a viable path toward reducing reliance on predetermined hand shapes or configurations. Further exploration could involve refining the CVAE component to improve contact map accuracy and success rates even further, or extending the algorithm to accommodate more complex object shapes and textures.

Conclusion

Overall, GenDexGrasp represents a significant advance in the field of robotic grasping, offering a framework that integrates speed, adaptability, and accuracy. The insights from this research may pave the way for more generalized tools and algorithms capable of advancing the current capabilities of robotic dexterity. As the technology progresses, it will be interesting to observe its impact on the development and deployment of more autonomous and adaptable robotic systems.