ContactGrasp: Functional Multi-finger Grasp Synthesis from Contact
Open accessAbstract
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.
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The contact forces that separate stable grasps from infeasible poses are not recorded in kinematic data.
“the contact forces that distinguish stable grasps from infeasible poses (Brahmbhatt et al., 2019) are unrecorded”
From Dexterous Robot Manipulation from Human Demonstrations via Contact-Anchored Retargeting and Residual Policy Learning (2026) · cites this paper as Brahmbhatt et al., 2019
The Introduction (page unavailable) says recorded hand poses lack a clear objective for reproducing demonstrated contact, and the Results (page unavailable) distinguish stable grasps from functional ones. Neither passage says that kinematic data omit contact forces or that those forces distinguish stable grasps from infeasible poses. A search for force-related passages found no statement establishing that claim. (LLM, conf=0.72)
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HGC-Net: Deep Anthropomorphic Hand Grasping in Clutter
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Towards Generalizable Zero-Shot Manipulation via Translating Human Interaction Plans
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Human-like dexterous manipulation for anthropomorphic five-fingered hands: A review
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Learning a Contact Potential Field for Modeling the Hand-Object Interaction
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SemGrasp : Semantic Grasp Generation via Language Aligned Discretization
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ContactHandover: Contact-Guided Robot-to-Human Object Handover
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TravelNet: Self-supervised Physically Plausible Hand Motion Learning from Monocular Color Images
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Ag2Manip: Learning Novel Manipulation Skills with Agent-Agnostic Visual and Action Representations
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Wang, X., & Xu, Q. (2024) · IEEE Transactions on Robotics · doi:10.1109/tro.2024.3422054 · Cited by 14 · see citing works
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Single-View Scene Point Cloud Human Grasp Generation
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Dexterous Robotic Grasping with Object-Centric Visual Affordances.
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Robot Grasp Planning: A Learning from Demonstration-Based Approach
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A Two-stage Learning Architecture that Generates High-Quality Grasps for a Multi-Fingered Hand
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Grasping 3D Objects With Virtual Hand in VR Environment
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Lakshmipathy, A., Bauer, D., Bauer, C., & Pollard, N. S. (2022) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra46639.2022.9811739 · Cited by 11 · see citing works
GrainGrasp: Dexterous Grasp Generation with Fine-grained Contact Guidance
Zhao, F., Tsetserukou, D., & Liu, Q. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610035 · Cited by 10 · see citing works
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UniGraspTransformer: Simplified Policy Distillation for Scalable Dexterous Robotic Grasping
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Functional Grasp Transfer Across a Category of Objects From Only one Labeled Instance
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ViTa-Zero: Zero-shot Visuotactile Object 6D Pose Estimation
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Multi-Finger Grasping Like Humans
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DexHand: dexterous hand manipulation motion synthesis for virtual reality
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A Survey on Human Interaction Motion Generation
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CHORUS: Learning Canonicalized 3D Human-Object Spatial Relations from Unbounded Synthesized Images
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Kim, H., Han, S., Kwon, P., & Joo, H. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72983-6_23 · Cited by 5 · see citing works
A Model-Free Approach to Fingertip Slip and Disturbance Detection for Grasp Stability Inference
Kitouni, D., Khoramshahi, M., & Perdereau, V. (2023) · IEEE International Conference on Development and Learning (ICDL) · doi:10.1109/icdl55364.2023.10364337 · Cited by 5 · see citing works
Hao, Y., Zhang, J., Zhuo, T., Wen, F., & Fan, H. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i3.27979 · Cited by 5 · see citing works
CPF: Learning a Contact Potential Field to Model the Hand-Object InteractionPreprint
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Learning Cross-Hand Policies of High-DOF Reaching and Grasping
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CMG-Net: An End-to-End Contact-based Multi-Finger Dexterous Grasping Network
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AViTa: Adaptive Visual-Tactile Dexterous Grasping
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PEAR: phrase-based hand-object interaction anticipation
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Hand Grasp Pose Prediction Based on Motion Prior Field
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TPGP: Temporal-Parametric Optimization with Deep Grasp Prior for Dexterous Motion Planning
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FastGrasp: Efficient Grasp Synthesis with Diffusion
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One-shot learning for autonomous aerial manipulation
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Synthetic contact maps to predict grasp regions on objects
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Joint Hand and Object Pose Estimation from a Single RGB Image using High‐level 2D Constraints
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Parallel-Jaw Gripper and Grasp Co-Optimization for Sets of Planar Objects
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A Surprisingly Efficient Representation for Multi-Finger Grasping
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Target Pose Guided Whole-body Grasping Motion Generation for Digital Humans
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- Authors
- Brahmbhatt, S., Handa, A., Hays, J., & Fox, D.
- Affiliations
- Georgia Institute of Technology (US)
- Nvidia (United States) (US)
- Venue
- 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Publisher
- IEEE
- Published
- 2019-11
- Pages
- 2386-2393
- ISSN
- 2153-0858 (ISSN-L)
- OpenAlex
- W2934245910
- Cited by
- 125
- References
- 40
- Citations by year
- 2026: 6 · 2025: 19 · 2024: 34 · 2023: 26 · 2022: 22 · 2021: 11 · 2020: 6 · 2019: 1
- FWCI
- 15.46
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Engineering › Biomedical Engineering › Soft Robotics and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Keywords
- anthropomorphic hand, object shape, human-robot interaction, household products, grasp stability
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