Songlin Wei 魏松林

Previously, my academic journey took me to Peking University CFCS where I had the privilege of working with Prof. He Wang.

I earned a Bachelor's degree in Software Engineering from Xiamen University. Over the years, my career has undergone various transformations. I developed large-scale social media websites, built robots, and started companies. Later, I discovered my passion for research and obtained a Master's degree in Control Science and Technology from Soochow University (which is located near home).

My current research interests include:

• Vision-Language-Action models for humanoids
• Benchmarking-oriented simulation
• Humanoid motion tracking

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Publications

Ψ₀: An Open Foundation Model Towards Universal Humanoid Loco-Manipulation

Ψ0 is an open vision-language-action (VLA) model for dexterous humanoid loco-manipulation.

ICLR: In-Context Imitation Learning with Visual Reasoning

We present In-Context Imitation Learning with Visual Reasoning (ICLR), a framework that augments demonstration prompts with structured visual reasoning traces representing anticipated future robot trajectories in image space

Diffusion Knows Transparency: Repurposing Video Diffusion for Transparent Object Depth and Normal Estimation

“Diffusion knows transparency.” Generative video priors can be repurposed, efficiently and label-free, into robust, temporally coherent perception for challenging real-world manipulation.

GraspVLA: a Grasping Foundation Model Pre-trained on Billion-scale Synthetic Action Data

We present GraspVLA, a VLA model pretrained on large-scale synthetic action data as a foundational model for grasping tasks.

Uni-NaVid: A Video-based Vision-Language-Action Model for Unifying Embodied Navigation Tasks

We present Uni-NaVid, the first video-based vision-language-action (VLA) model designed to unify diverse embodied navigation tasks and enable seamless navigation for mixed long-horizon tasks in unseen real-world environments.

RoboHanger: Learning Generalizable Robotic Hanger Insertion for Diverse Garments

In this work, we address the problem of inserting a hanger into various unseen garments that are initially laid out flat on a table.

GAPartManip: A Large-scale Part-centric Dataset for Material-Agnostic Articulated Object Manipulation

we introduced a large-scale part-centric dataset for articulated object manipulation that features both photo-realistic material randomizations and detailed annotations of part-oriented, scene-level actionable interaction poses.

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