ContactGrasp: Functional Multi-finger Grasp Synthesis from Contact
Abstract
Domain fit: AI-adjacent · Paper appears method- or tooling-adjacent to AI workflows with partial ecosystem coverage.
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 because of the large optimization space. 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.
Results and benchmarks
Grasping and manipulating objects is an important human skill.
Benchmark evidence is limited
Evidence graph: 2 refs, 1 links.
Utility signals: depth 40/100, grounding 58/100, status low.
Implementation
No direct implementation yet
Maintained implementation evidence is not confirmed for this paper yet.
Use the implementation status and reproduction sections for the current action plan.
No verified maintained repo yet
There is no verified maintained implementation yet. Use this baseline plan to decide whether to prototype now or defer.
- This is primarily a method paper. Reproduce it within a maintained framework baseline instead of chasing paper-specific repos.
- Start with framework-native implementations (e.g. PyTorch optimizer module, Optax, or Transformers training loops).
- Replicate the paper ablation settings first, then compare against modern baselines.
Time to first repro: a few hours
This is primarily a method paper. Reproduce it within a maintained framework baseline instead of chasing paper-specific repos.
- No maintained paper-verified implementation is currently available
Reproduction readiness
No repo
No verified implementation available
- No maintained repository has been identified for this paper. Check adjacent implementations or HF artifacts below.
Validation caveat
Hugging Face artifacts
No trustworthy direct or curated related Hugging Face artifacts were found yet. Use targeted searches to quickly locate candidate models, datasets, and demos.
Models
Datasets
Spaces
Tip: start with models, then check datasets and spaces if you need evaluation data or demos.
Research context
5
Citations
43
References
Tasks
GRASP, Object (grammar), Representation (politics), Computer science, Robotic hand, Human–computer interaction, Robot, Space (punctuation)
Methods
None detected
Domains
Artificial intelligence, Computer vision
Related papers
- Synergy-Based, Data-Driven Generation of Object-Specific Grasps for Anthropomorphic HandsSearch on Paper2Code
2018 · Semantic similarity
- Learning and Inference of Dexterous Grasps for Novel Objects with Underactuated HandsSearch on Paper2Code
2016 · Semantic similarity
- Towards cognitive grasping: modeling of unknown objects and its corresponding grasp typesSearch on Paper2Code
2011 · Semantic similarity
- Templates for pre-grasp sliding interactionsSearch on Paper2Code
2011 · Semantic similarity
- GraspIt!: A Versatile Simulator for Grasp AnalysisSearch on Paper2Code
2000 · Semantic similarity
Open this paper in HFEPX to review benchmark signals, evaluation modes, and human-feedback protocol context.
Open in HFEPXJump to Paper2Code search queries derived from this paper's research context.