# Learning to Use Chopsticks in Diverse Gripping Styles

arXiv: https://arxiv.org/abs/2205.14313 · HTML: https://mlsanity.com/paper/2205.14313/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2022.

## Built on by (most cited first, 29 of 29)

- [InterDiff: Generating 3D Human-Object Interactions with Physics-Informed Diffusion](https://mlsanity.com/paper/2308.16905/) (2023)
- [InterMimic: Towards Universal Whole-Body Control for Physics-Based Human-Object Interactions](https://mlsanity.com/paper/2502.20390/) (2025)
- [InterDreamer: Zero-Shot Text to 3D Dynamic Human-Object Interaction](https://mlsanity.com/paper/2403.19652/) (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)
- [Locomotion-Action-Manipulation: Synthesizing Human-Scene Interactions in Complex 3D Environments](https://mlsanity.com/paper/2301.02667/) (2023)
- [TokenHSI: Unified Synthesis of Physical Human-Scene Interactions through Task Tokenization](https://mlsanity.com/paper/2503.19901/) (2025)
- [PMP: Learning to Physically Interact with Environments using Part-wise Motion Priors](https://mlsanity.com/paper/2305.03249/) (2023)
- [InterAct: Advancing Large-Scale Versatile 3D Human-Object Interaction Generation](https://mlsanity.com/paper/2509.09555/) (2025)
- [Physical Interaction: Reconstructing Hand-object Interactions with Physics](https://mlsanity.com/paper/2209.10833/) (2022)
- [F"urElise: Capturing and Physically Synthesizing Hand Motions of Piano Performance](https://mlsanity.com/paper/2410.05791/) (2024)
- [Physics-aware Hand-object Interaction Denoising](https://mlsanity.com/paper/2405.11481/) (2024)
- [Zero-Shot Human-Object Interaction Synthesis with Multimodal Priors](https://mlsanity.com/paper/2503.20118/) (2025)
- [Hand-Object Interaction Controller (HOIC): Deep Reinforcement Learning for Reconstructing Interactions with Physics](https://mlsanity.com/paper/2405.02676/) (2024)
- [Learning to Ball: Composing Policies for Long-Horizon Basketball Moves](https://mlsanity.com/paper/2509.22442/) (2025)
- [Learning to Transfer In-Hand Manipulations Using a Greedy Shape Curriculum](https://mlsanity.com/paper/2303.12726/) (2023)
- [RP1M: A Large-Scale Motion Dataset for Piano Playing with Bi-Manual Dexterous Robot Hands](https://mlsanity.com/paper/2408.11048/) (2024)
- [Separate to Collaborate: Dual-Stream Diffusion Model for Coordinated Piano Hand Motion Synthesis](https://mlsanity.com/paper/2504.09885/) (2025)
- [HuMoCon: Concept Discovery for Human Motion Understanding](https://mlsanity.com/paper/2505.20920/) (2025)
- [PAD-Hand: Physics-Aware Diffusion for Hand Motion Recovery](https://mlsanity.com/paper/2603.26068/) (2026)
- [Bidirectional GaitNet: A Bidirectional Prediction Model of Human Gait and Anatomical Conditions](https://mlsanity.com/paper/2306.04161/) (2023)
- [COLLAGE: Collaborative Human-Agent Interaction Generation using Hierarchical Latent Diffusion and Language Models](https://mlsanity.com/paper/2409.20502/) (2024)
- [UniCross: Unified Cross-Skill Dexterous Manipulation Synthesis](https://mlsanity.com/paper/2607.28198/) (2026)
- [CalliRewrite: Recovering Handwriting Behaviors from Calligraphy Images without Supervision](https://mlsanity.com/paper/2405.15776/) (2024)
- [ForceGrip: Reference-Free Curriculum Learning for Realistic Grip Force Control in VR Hand Manipulation](https://mlsanity.com/paper/2503.08061/) (2025)
- [MUSIC: Learning Muscle-Driven Dexterous Hand Control](https://mlsanity.com/paper/2604.23886/) (2026)
- [LHM-Humanoid: Long-Horizon Human Motion Control for Continuous Object Transport in Cluttered Scenes](https://mlsanity.com/paper/2508.16943/) (2025)
- [SWIM: Compact Environment Representation for Reinforcement Learning Human Swimming](https://mlsanity.com/paper/2605.31120/) (2026)
- [Mana: Dexterous Manipulation of Articulated Tools](https://mlsanity.com/paper/2606.13677/) (2026)

## Abstract (arXiv)

Learning dexterous manipulation skills is a long-standing challenge in computer graphics and robotics, especially when the task involves complex and delicate interactions between the hands, tools and objects. In this paper, we focus on chopsticks-based object relocation tasks, which are common yet demanding. The key to successful chopsticks skills is steady gripping of the sticks that also supports delicate maneuvers. We automatically discover physically valid chopsticks holding poses by Bayesian Optimization (BO) and Deep Reinforcement Learning (DRL), which works for multiple gripping styles and hand morphologies without the need of example data. Given as input the discovered gripping poses and desired objects to be moved, we build physics-based hand controllers to accomplish relocation tasks in two stages. First, kinematic trajectories are synthesized for the chopsticks and hand in a motion planning stage. The key components of our motion planner include a grasping model to select suitable chopsticks configurations for grasping the object, and a trajectory optimization module to generate collision-free chopsticks trajectories. Then we train physics-based hand controllers through DRL again to track the desired kinematic trajectories produced by the motion planner. We demonstrate the capabilities of our framework by relocating objects of various shapes and sizes, in diverse gripping styles and holding positions for multiple hand morphologies. Our system achieves faster learning speed and better control robustness, when compared to vanilla systems that attempt to learn chopstick-based skills without a gripping pose optimization module and/or without a kinematic motion planner.