# RoboDexVLM: Visual Language Model-Enabled Task Planning and Motion Control for Dexterous Robot Manipulation

arXiv: https://arxiv.org/abs/2503.01616 · HTML: https://mlsanity.com/paper/2503.01616/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 23 of its 30 references that are in the map.
- First posted on arXiv 3 Mar 2025.

## Placed in

- Multimodal LLMs / vision-language models / [Embodied and 3D vision-language models](https://mlsanity.com/mllm/embodied-and-3d-vision-language-models/)

## Builds on (most cited first, 23 of 23)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models](https://mlsanity.com/paper/2307.05973/) (2023)
- [CLIPort: What and Where Pathways for Robotic Manipulation](https://mlsanity.com/paper/2109.12098/) (2021)
- [ReKep: Spatio-Temporal Reasoning of Relational Keypoint Constraints for Robotic Manipulation](https://mlsanity.com/paper/2409.01652/) (2024)
- [A Survey on Vision-Language-Action Models for Embodied AI](https://mlsanity.com/paper/2405.14093/) (2024)
- [Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes](https://mlsanity.com/paper/2103.14127/) (2021)
- [Foundation Models in Robotics: Applications, Challenges, and the Future](https://mlsanity.com/paper/2312.07843/) (2023)
- [AnyGrasp: Robust and Efficient Grasp Perception in Spatial and Temporal Domains](https://mlsanity.com/paper/2212.08333/) (2022)
- [Physically Grounded Vision-Language Models for Robotic Manipulation](https://mlsanity.com/paper/2309.02561/) (2023)
- [DexCap: Scalable and Portable Mocap Data Collection System for Dexterous Manipulation](https://mlsanity.com/paper/2403.07788/) (2024)
- [Scaling Up and Distilling Down: Language-Guided Robot Skill Acquisition](https://mlsanity.com/paper/2307.14535/) (2023)
- [A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS](https://mlsanity.com/paper/2304.00501/) (2023)
- [RoboMamba: Efficient Vision-Language-Action Model for Robotic Reasoning and Manipulation](https://mlsanity.com/paper/2406.04339/) (2024)
- [OmniManip: Towards General Robotic Manipulation via Object-Centric Interaction Primitives as Spatial Constraints](https://mlsanity.com/paper/2501.03841/) (2025)
- [D(R,O) Grasp: A Unified Representation of Robot and Object Interaction for Cross-Embodiment Dexterous Grasping](https://mlsanity.com/paper/2410.01702/) (2024)
- [RobotGPT: Robot Manipulation Learning from ChatGPT](https://mlsanity.com/paper/2312.01421/) (2023)
- [Learning Robust Real-World Dexterous Grasping Policies via Implicit Shape Augmentation](https://mlsanity.com/paper/2210.13638/) (2022)
- [AIC MLLM: Autonomous Interactive Correction MLLM for Robust Robotic Manipulation](https://mlsanity.com/paper/2406.11548/) (2024)
- [Learning Prehensile Dexterity by Imitating and Emulating State-only Observations](https://mlsanity.com/paper/2404.05582/) (2024)
- [Learning Failure Prevention Skills for Safe Robot Manipulation](https://mlsanity.com/paper/2305.02807/) (2023)

## Built on by (most cited first, 15 of 15)

- [A Survey on Vision-Language-Action Models: An Action Tokenization Perspective](https://mlsanity.com/paper/2507.01925/) (2025)
- [IS-Bench: Evaluating Interactive Safety of VLM-Driven Embodied Agents in Daily Household Tasks](https://mlsanity.com/paper/2506.16402/) (2025)
- [Parallels Between VLA Model Post-Training and Human Motor Learning: Progress, Challenges, and Trends](https://mlsanity.com/paper/2506.20966/) (2025)
- [Scaffolding Dexterous Manipulation with Vision-Language Models](https://mlsanity.com/paper/2506.19212/) (2025)
- [Interactive Imitation Learning for Dexterous Robotic Manipulation: Challenges and Perspectives -- A Survey](https://mlsanity.com/paper/2506.00098/) (2025)
- [Unfettered Forceful Skill Acquisition with Physical Reasoning and Coordinate Frame Labeling](https://mlsanity.com/paper/2505.09731/) (2025)
- [UniManip: General-Purpose Zero-Shot Robotic Manipulation with Agentic Operational Graph](https://mlsanity.com/paper/2602.13086/) (2026)
- [Action with Visual Primitives](https://mlsanity.com/paper/2605.22183/) (2026)
- [Adaptive Vision-Language Grasping via Composable Foundation Priors and Generalizable Grasp Synthesis](https://mlsanity.com/paper/2609.04096/) (2026)
- [Fake or Real, Can Robots Tell? Evaluating VLM Robustness to Domain Shift in Single-View Robotic Scene Understanding](https://mlsanity.com/paper/2506.19579/) (2025)
- [RoCo Challenge at AAAI 2026: Benchmarking Robotic Collaborative Manipulation for Assembly Towards Industrial Automation](https://mlsanity.com/paper/2603.15469/) (2026)
- [Grasp-Then-Plan with Failure Attribution: A Closed Two-Stage Framework for Precise and Generalizable Robotic Manipulation](https://mlsanity.com/paper/2606.03385/) (2026)
- [Inductive Generalization for Robotic Manipulation](https://mlsanity.com/paper/2606.20999/) (2026)
- [Agentic RAG-VLM: Affordance-Aware Retrieval-Augmented Generation with Self-Reflective Planning for Robotic Grasping](https://mlsanity.com/paper/2606.31200/) (2026)
- [Safe Task Planning with Long-Term Graph Memory for Embodied Agents](https://mlsanity.com/paper/2609.08444/) (2026)

## Abstract (arXiv)

This paper introduces RoboDexVLM, an innovative framework for robot task planning and grasp detection tailored for a collaborative manipulator equipped with a dexterous hand. Previous methods focus on simplified and limited manipulation tasks, which often neglect the complexities associated with grasping a diverse array of objects in a long-horizon manner. In contrast, our proposed framework utilizes a dexterous hand capable of grasping objects of varying shapes and sizes while executing tasks based on natural language commands. The proposed approach has the following core components: First, a robust task planner with a task-level recovery mechanism that leverages vision-language models (VLMs) is designed, which enables the system to interpret and execute open-vocabulary commands for long sequence tasks. Second, a language-guided dexterous grasp perception algorithm is presented based on robot kinematics and formal methods, tailored for zero-shot dexterous manipulation with diverse objects and commands. Comprehensive experimental results validate the effectiveness, adaptability, and robustness of RoboDexVLM in handling long-horizon scenarios and performing dexterous grasping. These results highlight the framework's ability to operate in complex environments, showcasing its potential for open-vocabulary dexterous manipulation. Our open-source project page can be found at https://henryhcliu.github.io/robodexvlm.