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Pei Lin

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3 papers

2 author rows

AAAI Conference 2025 Conference Paper

HandDiffuse: Generative Controllers for Two-Hand Interactions via Diffusion Models

Pei Lin

Existing hands datasets are largely short-range and the interaction is weak due to the self-occlusion and self-similarity of hands, which can not yet fit the need for interacting hands motion generation. To rescue the data scarcity, we propose HandDiffuse12.5M, a novel and real dataset that consists of temporal sequences with strong two-hand interactions. HandDiffuse12.5M has the largest scale and richest interactions among the existing two-hand datasets. We further present a strong baseline method HandDiffuse for the controllable motion generation of interacting hands using various controllers. Specifically, we apply the diffusion model as the backbone and design two motion representations for different controllers. To reduce artifacts, we also propose Interaction Loss which explicitly quantifies the dynamic interaction process. Our HandDiffuse enables various applications, i.e., motion in-betweening and trajectory controled generation. Experiments show that our method outperforms the state-of-the-art techniques in motion generation. The vivid two-hand motions generated by our method can also construct synthetic datasets and enhances the accuracy of existing hand motion capture algorithms.

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IROS Conference 2025 Conference Paper

R-Tac0: A Rounded High-Frequency Transferable Monochrome Vision-based Tactile Sensor for Shape Reconstruction

Wanlin Li
Pei Lin
Meng Wang 0051
Chenxi Xiao
Kaspar Althoefer
Yao Su 0001
Ziyuan Jiao
Hangxin Liu

Endowing the curved surfaces of rounded vision-based tactile fingers is essential for dexterous robotic manipulation, as they offer more sufficient contact with the environment. However, current rounded designs are constrained by a low sensing frequency (30–60 Hz) and the need for recalibration when adapting to new sensors due to the reliance on multi-channel captures, which hinders their performance in dynamic robotic tasks and large-scale deployment. In this work, we introduce R-Tac0, a low-cost rounded VBTS engineered for high-resolution and high-speed perception. The key innovation is a monochrome vision-based sensing principle: utilizing a black-and-white camera to capture the reflection properties of the compound rounded elastomer under monochromatic illumination. This single-channel imaging significantly reduces data volume and simplifies computational complexity, enabling 120 Hz tactile perception. A lightweight neural network can calibrate the sensor to achieve a depth reconstruction accuracy of 0. 169 mm per pixel, while exhibiting surprisingly good transferability to new sensors. In experiments, we demonstrate the advantages of R-Tac0’s rounded design by evaluating its performance under different contact angles, its high-frequency perception in slip detection, and its effectiveness in robotic dynamic pose estimation.

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NeurIPS Conference 2024 Conference Paper

Molecule Design by Latent Prompt Transformer

Deqian Kong
Yuhao Huang
Jianwen Xie
Edouardo Honig
Ming Xu
Shuanghong Xue
Pei Lin
Sanping Zhou

This work explores the challenging problem of molecule design by framing it as a conditional generative modeling task, where target biological properties or desired chemical constraints serve as conditioning variables. We propose the Latent Prompt Transformer (LPT), a novel generative model comprising three components: (1) a latent vector with a learnable prior distribution modeled by a neural transformation of Gaussian white noise; (2) a molecule generation model based on a causal Transformer, which uses the latent vector as a prompt; and (3) a property prediction model that predicts a molecule's target properties and/or constraint values using the latent prompt. LPT can be learned by maximum likelihood estimation on molecule-property pairs. During property optimization, the latent prompt is inferred from target properties and constraints through posterior sampling and then used to guide the autoregressive molecule generation. After initial training on existing molecules and their properties, we adopt an online learning algorithm to progressively shift the model distribution towards regions that support desired target properties. Experiments demonstrate that LPT not only effectively discovers useful molecules across single-objective, multi-objective, and structure-constrained optimization tasks, but also exhibits strong sample efficiency.

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