Songlin Wei 魏松林

I earned my Bachelor of Software Engineering degree from Xiamen University. Over the years, my career has undergone various transformations. I developed large social media websites, built robots, and started companies.

I finally found my passion in doing research and went to Soochow University and obtained a master degree in Control Science and Technology. I had worked closely with Prof. Guodong Chen and Prof. WenZheng Chi.

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Publications

My research interests include 3D computer vision, robotic learning and Embodied AI. I'm currently working on Vision-Language-Action models for robotics.

D3RoMa: Disparity Diffusion-based Depth Sensing for Material-Agnostic Robotic Manipulation

We propose a diffusion model-based depth estimation framework on stereo image pairs for robotic manipulation.

Make a Donut🍩: Hierarchical EMD-Space Planning for Zero-Shot Deformable Manipulation with Tools

In this work, we introduce a demonstration-free hierarchical planning approach capable of tackling intricate long-horizon tasks without necessitating any training

Open6DOR: Benchmarking Open-instruction 6-DoF Object Rearrangement and A VLM-based Approach

We present Open6DOR, a challenging and comprehensive benchmark for open-instruction 6-DoF object rearrangement tasks. Following this, we propose a zero-shot and robust method, Open6DORGPT, which proves effective in demanding simulation environments and real-world scenarios.

SAGE🌿: Bridging Semantic and Actionable Parts for Generalizable Manipulation of Articulated Objects

We present SAGE🌿, a framework bridging the understanding of semantic and actionable parts for generalizable manipulation of articulated objects.

FG-NeRF: Flow-GAN based Probabilistic Neural Radiance Field for Independence-Assumption-Free Uncertainty Estimation

We propose an independence-assumption-free probabilistic neural radiance field based on Flow-GAN. By combining the generative capability of adversarial learning and the powerful expressivity of normalizing flow, our method explicitly models the density-radiance distribution of the whole scene.

3D Object Aided Self-Supervised Monocular Depth Estimation

Self-supervised depth estimation methods rely on static world assumption, which produce inaccurate depths of dynamic objects. In this work, we propose to address dynamic object movements through monocular 3D object detection.

Object Clustering with Dirichlet Process Mixture Model for Data Association in Monocular SLAM

We propose a novel data association method for cuboid landmarks based on Dirichlet Process Mixture Model. By jointly considering object class, position, and size, our method can perform data association robustly.

Forked from Leonid Keselman's website