# Enhancing Dexterity in Confined Spaces: Real-Time Motion Planning for Multi-Fingered In-Hand Manipulation

arXiv: https://arxiv.org/abs/2309.06955 · HTML: https://mlsanity.com/paper/2309.06955/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2023.

## Built on by (most cited first, 2 of 2)

- [UniBYD: A Unified Framework for Learning Robotic Manipulation Across Embodiments Beyond Imitation of Human Demonstrations](https://mlsanity.com/paper/2512.11609/) (2025)
- [Scalable Dexterous Robot Learning with AR-based Remote Human-Robot Interactions](https://mlsanity.com/paper/2602.07341/) (2026)

## Abstract (arXiv)

Dexterous in-hand manipulation in robotics, particularly with multi-fingered robotic hands, poses significant challenges due to the intricate avoidance of collisions among fingers and the object being manipulated. Collision-free paths for all fingers must be generated in real-time, as the rapid changes in hand and finger positions necessitate instantaneous recalculations to prevent collisions and ensure undisturbed movement. This study introduces a real-time approach to motion planning in high-dimensional spaces. We first explicitly model the collision-free space using neural networks that are retrievable in real time. Then, we combined the C-space representation with closed-loop control via dynamical system and sampling-based planning approaches. This integration enhances the efficiency and feasibility of path-finding, enabling dynamic obstacle avoidance, thereby advancing the capabilities of multi-fingered robotic hands for in-hand manipulation tasks.