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Yunfeng Lin

7 accepted papers

2026

Embodiment‑Aware Generalist Specialist Distillation for Unified Humanoid Whole-Body Control

ICRA 2026poster

Humanoid Whole-Body Controllers trained with reinforcement learning (RL) have recently achieved remarkable performance, yet many target a single robot embodiment. Variations in dynamics, degrees of freedom (DoFs), and kinematic topology still hinder a single policy from commanding diverse humanoids.…

2026

H-Zero: Cross-Humanoid Locomotion Pretraining Enables Few-Shot Novel Embodiment Transfer

ICRA 2026poster

The rapid advancement of humanoid robotics has intensified the need for robust and adaptable controllers to enable stable and efficient locomotion across diverse platforms. However, developing such controllers remains a significant challenge because existing solutions are tailored to specific robot …

2026

Manipulation as in Simulation: Enabling Accurate Geometry Perception in Robots

ICLR 2026poster

Modern robotic manipulation primarily relies on visual observations in a 2D color space for skill learning but suffers from poor generalization. In contrast, humans, living in a 3D world, depend more on physical properties-such as distance, size, and shape-than on texture when interacting with objec…

Cited by 0SourcecodeScholar
2026

Scalable and General Whole-Body Control for Cross-Humanoid Locomotion

ICML 2026poster

Learning-based whole-body controllers have become a key driver for humanoid robots, yet most existing approaches require robot-specific training. In this paper, we study the problem of cross-embodiment humanoid control and show that a single policy can robustly generalize across a wide range of huma…

Cited by 0SourceScholar
2025

World Model-Based Perception for Visual Legged Locomotion

ICRA 2025

Legged locomotion over various terrains is challenging and requires precise perception of the robot and its surroundings from both proprioception and vision. However, learning directly from high-dimensional visual input is often data-inefficient and intricate. To address this issue, traditional meth

Cited by 23SourcecodeScholar
2024

GenSim2: Scaling Robot Data Generation with Multi-modal and Reasoning LLMs

CoRL 2024poster

Robotic simulation today remains challenging to scale up due to the human efforts required to create diverse simulation tasks and scenes. Simulation-trained policies also face scalability issues as many sim-to-real methods focus on a single task. To address these challenges, this work proposes GenSi…

Cited by 11SourceScholar
2020

Residual Distillation: Towards Portable Deep Neural Networks without Shortcuts

NeurIPS 2020poster

By transferring both features and gradients between different layers, shortcut connections explored by ResNets allow us to effectively train very deep neural networks up to hundreds of layers. However, the additional computation costs induced by those shortcuts are often overlooked. For example, du…