arXiv subjects

Jie Chu

Publications and source records attributed to Jie Chu.

3 recordsLinked to original sources

arXiv2025

RetrDex: Efficient Object Retrieval in Cluttered Scenes with a Dexterous Hand

Retrieving objects buried beneath clutter is both challenging and time-consuming, as complex support relationships make manipulation particularly difficult. Existing methods either focus on support relations and rely on sequential grasping to remove occluding objects, or perform preparatory actions such as pushing to facilitate subsequent grasps. However, these approaches are often inefficient and treat physical interactions as isolated auxiliary steps. In this paper, we propose RetrDex, an efficient framework for dexterous arm-hand systems to learn object retrieval in cluttered scenes. Our approach leverages large-scale parallel reinforcement learning (RL) in diverse cluttered scenes and incorporates a spatially aware representation that encodes occlusion patterns and spatial relationships among the target, the dexterous hand, and surrounding clutter. This representation enables the policy to develop diverse manipulation skills (e.g., pushing, stirring, and poking) that actively clear occluders. We evaluate RetrDex on 16 household objects across varied clutter configurations, and obtain strong retrieval performance and efficiency on both seen and unseen targets. Furthermore, we demonstrate successful zero-shot transfer to a real-world dexterous multi-fingered robot system, validating the practical applicability of our method. Videos can be found on our project website: https://RetrDex.github.io.

cs.RO↗

arXiv2024

Learning Dual Transformers for All-In-One Image Restoration from a Frequency Perspective

This work aims to tackle the all-in-one image restoration task, which seeks to handle multiple types of degradation with a single model. The primary challenge is to extract degradation representations from the input degraded images and use them to guide the model's adaptation to specific degradation types. Building on the insight that various degradations affect image content differently across frequency bands, we propose a new dual-transformer approach comprising two components: a frequency-aware Degradation estimation transformer (Dformer) and a degradation-adaptive Restoration transformer (Rformer). The Dformer captures the essential characteristics of various degradations by decomposing the input into different frequency components. By understanding how degradations affect these frequency components, the Dformer learns robust priors that effectively guide the restoration process. The Rformer then employs a degradation-adaptive self-attention module to selectively focus on the most affected frequency components, guided by the learned degradation representations. Extensive experimental results demonstrate that our approach outperforms existing methods in five representative restoration tasks, including denoising, deraining, dehazing, deblurring, and low-light enhancement. Additionally, our method offers benefits for handling, real-world degradations, spatially variant degradations, and unseen degradation levels.

cs.CV↗

arXiv2022

An improved algorithm for Generalized Čech complex construction

In this paper, we present an algorithm that computes the generalized Čech complex for a finite set of disks where each may have a different radius in 2D space. An extension of this algorithm is also proposed for a set of balls in 3D space with different radius. To compute a $k$-simplex, we leverage the computation performed in the round of $(k-1)$-simplices such that we can reduce the number of potential candidates to verify to improve the efficiency. An efficient verification method is proposed to confirm if a $k$-simplex can be constructed on the basis of the $(k-1)$-simplices. We demonstrate the performance with a comparison to some closely related algorithms.

cs.CG↗