# D-Grasp: Physically Plausible Dynamic Grasp Synthesis for Hand-Object Interactions

arXiv: https://arxiv.org/abs/2112.03028 · HTML: https://mlsanity.com/paper/2112.03028/

## Key facts

- Cited by 113 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 48 references that are in the map.
- First posted on arXiv 1 Dec 2021.

## Builds on (most cited first, 30 of 34)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations](https://mlsanity.com/paper/1709.10087/) (2017)
- [Learning Dexterous In-Hand Manipulation](https://mlsanity.com/paper/1808.00177/) (2018)
- [GRAB: A Dataset of Whole-Body Human Grasping of Objects](https://mlsanity.com/paper/2008.11200/) (2020)
- [Learning joint reconstruction of hands and manipulated objects](https://mlsanity.com/paper/1904.05767/) (2019)
- [DexYCB: A Benchmark for Capturing Hand Grasping of Objects](https://mlsanity.com/paper/2104.04631/) (2021)
- [H2O: Two Hands Manipulating Objects for First Person Interaction Recognition](https://mlsanity.com/paper/2104.11181/) (2021)
- [DexMV: Imitation Learning for Dexterous Manipulation from Human Videos](https://mlsanity.com/paper/2108.05877/) (2021)
- [ContactPose: A Dataset of Grasps with Object Contact and Hand Pose](https://mlsanity.com/paper/2007.09545/) (2020)
- [Grasping Field: Learning Implicit Representations for Human Grasps](https://mlsanity.com/paper/2008.04451/) (2020)
- [Hand-Object Contact Consistency Reasoning for Human Grasps Generation](https://mlsanity.com/paper/2104.03304/) (2021)
- [ContactDB: Analyzing and Predicting Grasp Contact via Thermal Imaging](https://mlsanity.com/paper/1904.06830/) (2019)
- [Reconstructing Hand-Object Interactions in the Wild](https://mlsanity.com/paper/2012.09856/) (2020)
- [H+O: Unified Egocentric Recognition of 3D Hand-Object Poses and Interactions](https://mlsanity.com/paper/1904.05349/) (2019)
- [Semi-Supervised 3D Hand-Object Poses Estimation with Interactions in Time](https://mlsanity.com/paper/2106.05266/) (2021)
- [Leveraging Photometric Consistency over Time for Sparsely Supervised Hand-Object Reconstruction](https://mlsanity.com/paper/2004.13449/) (2020)
- [ContactOpt: Optimizing Contact to Improve Grasps](https://mlsanity.com/paper/2104.07267/) (2021)
- [CPF: Learning a Contact Potential Field to Model the Hand-Object Interaction](https://mlsanity.com/paper/2012.00924/) (2020)
- [SimPoE: Simulated Character Control for 3D Human Pose Estimation](https://mlsanity.com/paper/2104.00683/) (2021)
- [HOPE-Net: A Graph-based Model for Hand-Object Pose Estimation](https://mlsanity.com/paper/2004.00060/) (2020)
- [Dynamics-Regulated Kinematic Policy for Egocentric Pose Estimation](https://mlsanity.com/paper/2106.05969/) (2021)
- [Weakly Supervised 3D Hand Pose Estimation via Biomechanical Constraints](https://mlsanity.com/paper/2003.09282/) (2020)
- [Contact and Human Dynamics from Monocular Video](https://mlsanity.com/paper/2007.11678/) (2020)
- [Learning Dexterous Grasping with Object-Centric Visual Affordances](https://mlsanity.com/paper/2009.01439/) (2020)
- [ArtiBoost: Boosting Articulated 3D Hand-Object Pose Estimation via Online Exploration and Synthesis](https://mlsanity.com/paper/2109.05488/) (2021)
- [Neural Monocular 3D Human Motion Capture with Physical Awareness](https://mlsanity.com/paper/2105.01057/) (2021)
- [PhysCap: Physically Plausible Monocular 3D Motion Capture in Real Time](https://mlsanity.com/paper/2008.08880/) (2020)
- [A Skeleton-Driven Neural Occupancy Representation for Articulated Hands](https://mlsanity.com/paper/2109.11399/) (2021)
- [Generalization in Dexterous Manipulation via Geometry-Aware Multi-Task Learning](https://mlsanity.com/paper/2111.03062/) (2021)

## Built on by (most cited first, 30 of 113)

- [ARCTIC: A Dataset for Dexterous Bimanual Hand-Object Manipulation](https://mlsanity.com/paper/2204.13662/) (2022)
- [GOAL: Generating 4D Whole-Body Motion for Hand-Object Grasping](https://mlsanity.com/paper/2112.11454/) (2021)
- [IMos: Intent-Driven Full-Body Motion Synthesis for Human-Object Interactions](https://mlsanity.com/paper/2212.07555/) (2022)
- [UniDexGrasp: Universal Robotic Dexterous Grasping via Learning Diverse Proposal Generation and Goal-Conditioned Policy](https://mlsanity.com/paper/2303.00938/) (2023)
- [Controllable Human-Object Interaction Synthesis](https://mlsanity.com/paper/2312.03913/) (2023)
- [UniDexGrasp++: Improving Dexterous Grasping Policy Learning via Geometry-aware Curriculum and Iterative Generalist-Specialist Learning](https://mlsanity.com/paper/2304.00464/) (2023)
- [Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications](https://mlsanity.com/paper/2211.08064/) (2022)
- [TACO: Benchmarking Generalizable Bimanual Tool-ACtion-Object Understanding](https://mlsanity.com/paper/2401.08399/) (2024)
- [HOLD: Category-agnostic 3D Reconstruction of Interacting Hands and Objects from Video](https://mlsanity.com/paper/2311.18448/) (2023)
- [PhysHOI: Physics-Based Imitation of Dynamic Human-Object Interaction](https://mlsanity.com/paper/2312.04393/) (2023)
- [InterMimic: Towards Universal Whole-Body Control for Physics-Based Human-Object Interactions](https://mlsanity.com/paper/2502.20390/) (2025)
- [ManipTrans: Efficient Dexterous Bimanual Manipulation Transfer via Residual Learning](https://mlsanity.com/paper/2503.21860/) (2025)
- [DiffH2O: Diffusion-Based Synthesis of Hand-Object Interactions from Textual Descriptions](https://mlsanity.com/paper/2403.17827/) (2024)
- [GraspXL: Generating Grasping Motions for Diverse Objects at Scale](https://mlsanity.com/paper/2403.19649/) (2024)
- [ParaHome: Parameterizing Everyday Home Activities Towards 3D Generative Modeling of Human-Object Interactions](https://mlsanity.com/paper/2401.10232/) (2024)
- [CAMS: CAnonicalized Manipulation Spaces for Category-Level Functional Hand-Object Manipulation Synthesis](https://mlsanity.com/paper/2303.15469/) (2023)
- [ArtiGrasp: Physically Plausible Synthesis of Bi-Manual Dexterous Grasping and Articulation](https://mlsanity.com/paper/2309.03891/) (2023)
- [Physically Plausible Full-Body Hand-Object Interaction Synthesis](https://mlsanity.com/paper/2309.07907/) (2023)
- [FLEX: Full-Body Grasping Without Full-Body Grasps](https://mlsanity.com/paper/2211.11903/) (2022)
- [CORE4D: A 4D Human-Object-Human Interaction Dataset for Collaborative Object REarrangement](https://mlsanity.com/paper/2406.19353/) (2024)
- [DexGraspNet 2.0: Learning Generative Dexterous Grasping in Large-scale Synthetic Cluttered Scenes](https://mlsanity.com/paper/2410.23004/) (2024)
- [Task-Oriented Human-Object Interactions Generation with Implicit Neural Representations](https://mlsanity.com/paper/2303.13129/) (2023)
- [Object pop-up: Can we infer 3D objects and their poses from human interactions alone?](https://mlsanity.com/paper/2306.00777/) (2023)
- [HOIDiffusion: Generating Realistic 3D Hand-Object Interaction Data](https://mlsanity.com/paper/2403.12011/) (2024)
- [HIMO: A New Benchmark for Full-Body Human Interacting with Multiple Objects](https://mlsanity.com/paper/2407.12371/) (2024)
- [Benchmarks and Challenges in Pose Estimation for Egocentric Hand Interactions with Objects](https://mlsanity.com/paper/2403.16428/) (2024)
- [Learning Continuous Grasping Function with a Dexterous Hand from Human Demonstrations](https://mlsanity.com/paper/2207.05053/) (2022)
- [Learning to Imitate Object Interactions from Internet Videos](https://mlsanity.com/paper/2211.13225/) (2022)
- [RealDex: Towards Human-like Grasping for Robotic Dexterous Hand](https://mlsanity.com/paper/2402.13853/) (2024)
- [GigaHands: A Massive Annotated Dataset of Bimanual Hand Activities](https://mlsanity.com/paper/2412.04244/) (2024)

## Abstract (arXiv)

We introduce the dynamic grasp synthesis task: given an object with a known 6D pose and a grasp reference, our goal is to generate motions that move the object to a target 6D pose. This is challenging, because it requires reasoning about the complex articulation of the human hand and the intricate physical interaction with the object. We propose a novel method that frames this problem in the reinforcement learning framework and leverages a physics simulation, both to learn and to evaluate such dynamic interactions. A hierarchical approach decomposes the task into low-level grasping and high-level motion synthesis. It can be used to generate novel hand sequences that approach, grasp, and move an object to a desired location, while retaining human-likeness. We show that our approach leads to stable grasps and generates a wide range of motions. Furthermore, even imperfect labels can be corrected by our method to generate dynamic interaction sequences.