ContactGrasp: Functional Multi-finger Grasp Synthesis from Contact

By Samarth Brahmbhatt, Ankur Handa, James Hays, Dieter Fox. 2019

164 citations recorded by the source corpus.

Grasping and manipulating objects is an important human skill. Since most objects are designed to be manipulated by human hands, anthropomorphic hands can enable richer human-robot interaction. Desirable grasps are not only stable, but also functional: they enable post-grasp actions with the object. However, functional grasp synthesis for high degree-of-freedom anthropomorphic hands from object shape alone is challenging. We present ContactGrasp, a framework for functional grasp synthesis from object shape and contact on the object surface. Contact can be manually specified or obtained through demonstrations. Our contact representation is object-centric and allows functional grasp synthesis even for hand models different than the one used for demonstration. Using a dataset of contact demonstrations from humans grasping diverse household objects, we synthesize functional grasps for three hand models and two functional intents. The project webpage is https://contactdb.cc.gatech.edu/contactgrasp.html.

Subfields: Biomedical Engineering, Control and Systems Engineering, Human-Computer Interaction, Robotics & Control

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Citations in the corpus

Fine-Grained Egocentric Hand-Object Segmentation: Dataset, Model, and Applications (2022, background): “A line of works [2,7,21,23,67] have also attempted to generate hand pose conditioned on objects.”
Grasp'D: Differentiable Contact-rich Grasp Synthesis for Multi-fingered Hands (2022, background): “Some works [8, 52] aim to transfer human grasps to robotic hands by matching contact patterns, but these suffer from important limitations, since the same contacts may not be achievable by human and robotic hands, given differences in their morphology and articulation constraints (e.”
Hand-Object Contact Consistency Reasoning for Human Grasps Generation (2021, methodology): “Generating human grasp is very challenging due to the higher degree-of-freedom of the human hand [24, 9, 46, 3, 21].”
BEHAVE: Dataset and Method for Tracking Human Object Interactions (2022, background): “Dynamic human object interactions Recently, there has been a strong push on modeling hand-object interactions based on 3D [42,72], 2.5D [13,14] and 2D [22,24,28,35,83] Dataset RGBD Hum.”
DexMV: Imitation Learning for Dexterous Manipulation from Human Videos (2021, background): “[12] propose to collect the contact maps on objects with a thermal camera, which can be used to learn functional grasps [32, 13, 44].”
Learning Dexterous Grasping with Object-Centric Visual Affordances (2020, methodology): “g.[7], as well as methods that directly regress 6-”
GraspNet-1Billion: A Large-Scale Benchmark for General Object Grasping (2020, background): “To avoid such problem, some recent works explore using simulated environment [26, 9, 48, 28, 4] to anotate grasp poses.”