As robotic systems evolve toward embodied agents capable of complex interaction, grasping can no longer be treated solely as a geometric stability optimization problem. The recent rise of humanoid robots, increasingly guided by high-level reasoning frameworks such as Large Language Models, demands grasps that directly answer functional, task-level intents. Fine-grained robot manipulation necessitates dexterous, multi-fingered hands with high Degrees of Freedom (DoF) because many downstream tasks—such as in-hand object reorientation, precision tool insertion, finger gaiting, and handle-based grasping—demand controllable, distributed multi-point contact topologies that fundamentally exceed the symmetric pinch and enveloping capabilities of simple parallel-jaw grippers.
Introduction
However, the transition to high-DoF systems exposes a critical limitation in existing grasp planners: while they can produce physically stable grasps, they lack the structural properties required to generate task-aligned contact topologies. Modern data-driven grasp planners predominantly focus on whether an object can be grasped rather than how it should be manipulated.
By optimizing purely for geometric stability and force closure, these methods introduce a severe generative bias toward energetically stable but functionally uniform grasps. Although dexterous hands offer rich articulated contact capabilities, current planners do not fully exploit their potential in terms of grasp pattern diversity, instead overwhelmingly defaulting to enveloping power grasps.
Consequently, synthesizing a specific precision grasp required for a downstream task becomes highly inefficient, as it necessitates the generation and rejection of an impractical volume of functionally unsuitable candidates.
The method is evaluated extensively in both simulation and real-world settings. The evaluation first demonstrates the severe functional bias of current taxonomy-unaware planners by measuring the entropy of their retrieved contact topology distributions. CoToGrasp is then compared against state-of-the-art taxonomy-guided methods, demonstrating superior topology compliance and stability.
The main contribution is CoToGrasp, an object-agnostic grasp planner that synthesizes grasps conditioned on structured contact topologies derived from human taxonomies. To properly assess these capabilities, a rigorous evaluation methodology is established to quantify the functional diversity and semantic bias inherent in dexterous grasp planners.
Method
To successfully decouple high-level functional semantics from arbitrary object geometries, an object-agnostic learning paradigm is adopted. Unlike standard methods, the architecture is trained exclusively on gripper point clouds and inferred on target object point clouds.
During the training phase, the model operates entirely within the canonical gripper frame, rendering it independent of the global pose.
The model uses a canonical gripper coordinate frame rather than depending on the gripper’s global position and orientation.
The network takes as input only the gripper’s local surface geometry and a semantic contact topology, learning to reconstruct the corresponding physical contact template mask.
Conclusion
This paper introduces CoToGrasp, a generative framework for contact-topology-conditioned dexterous grasp synthesis. By fundamentally decoupling functional intent from object identity, the approach effectively mitigates the severe mode collapse observed in contemporary grasp planners.
This allows CoToGrasp to synthesize highly constrained, topology-compliant precision and power grasps on unseen geometries without relying on costly object-annotated datasets. Extensive evaluations demonstrate that the validation pipeline robustly filters topologically invalid configurations, yielding state-of-the-art semantic diversity and physical stability.
Finally, successful real-world deployments on the Allegro Hand confirm that the structural properties of the generated contact topologies are physically executable on a real robot platform.
Acknowledgments
This publication was made possible by the use of the FactoryIA supercomputer, financially supported by the Ile-De-France Regional Council. Experiments presented in this paper were carried out thanks to a platform funded by DIM AI4IDF and PRAIRIE-PSAI.
The authors thank Timothée Carecchio for valuable assistance in achieving the experimental results presented in this paper.
This project received funding from the European Union’s Horizon Europe research and innovation program under grant agreement nº 101135708 through the JARVIS Project.
Liming Chen’s research was in part supported by the French Research Agency, l’Agence Nationale de Recherche, through the Aristotle, Astérix, DEMETER, and PROTEUS projects, and by the French national investment prioritary program through the PSPC FAIR WASTE project.
Frequently Asked Questions
What problem does CoToGrasp address? It addresses the inability of many dexterous grasp planners to generate task-aligned contact topologies rather than only physically stable grasps.
What does the planner condition grasps on? CoToGrasp conditions grasp synthesis on structured contact topologies derived from human taxonomies.
How is the method trained? The architecture is trained exclusively on gripper point clouds within a canonical gripper frame and is inferred on target object point clouds.
Was CoToGrasp tested on a real robot? Yes. Real-world deployments on the Allegro Hand demonstrated that the generated contact topologies were physically executable.