5 papers · 1 filter
CoToGrasp: Contact-Topology-Conditioned Dexterous Grasp Synthesis via Canonical Workspace Learning
Julien Merand, Boris Meden, Liming Chen +1
Current dexterous grasp planners primarily optimize for physical stability, focusing on whether an object can be grasped rather than how it should be grasped to support downstream…
GOAG: Generative and Object-Agnostic Grasp Planner for Dexterous Robotic Manipulation
Julien Merand, Boris Meden, Mathieu Grossard +1
Multifingered grasping is a crucial robotic skill, but current deep-learning grasp planners often struggle to generalize to new objects because they are trained on limited, object-…
More Structure, Not More Capacity: Object-Centric Representations for Visuomotor Imitation Learning
Yi Li, Alexandre Chapin, Liming Chen +2
Robotic manipulation policies rely on pre-trained vision models that give either a global scene embedding or a dense patch grid. Both mix task-relevant and task-irrelevant features…
SaPaVe: Towards Active Perception and Manipulation in Vision-Language-Action Models for Robotics
Mengzhen Liu, Enshen Zhou, Cheng Chi +6
Active perception and manipulation are crucial for robots to interact with complex scenes. Existing methods struggle to unify semantic-driven active perception with robust, viewpoi…
Tactile MNIST: Benchmarking Active Tactile Perception
Tim Schneider, Guillaume Duret, Cristiana de Farias +3
Tactile perception has the potential to significantly enhance dexterous robotic manipulation by providing rich local information that can complement or substitute for other sensory…