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Mingyang Hu

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2 papers

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IROS Conference 2025 Conference Paper

Social Robot Haru Assisting Dynamic Group Discussion with Autonomous Eye Gaze Behavior

Fei Tang
Mingyang Hu
Yu Fang 0007
Hongqi Yu
Eric Nichols
Randy Gomez
Guangliang Li

Due to recent advances in large language models and robotics, social robots will potentially play an important role in people’s daily lives soon, and are expected to improve dynamic multi-party group discussions in social scenarios. In this paper, we developed a system to assist dynamic group discussion with our social robot Haru. Our system is composed of three modules: a Dialogue Assistance module via integrating Haru with large language models which facilitates Haru to be an embodied chatbot; a Balancing and Welcoming Behavior module to improve users’ engagement and welcome new users to join the discussion with verbal behaviors; an Autonomous Eye Gazing module to show politeness during group discussion, e. g. , gazing to the talking user or the less-engaging user to encourage her, looking to the new comer when she joins the discussion, gazing via eyeball movement when the current speaking user is close to the previous one. The autonomous eye gazing behavior was first trained via deep reinforcement learning in simulation and transferred to physical Haru in the real world. Results of our user study with 50 subjects show the significant performance of our system in assisting dynamic group discussion.

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NeurIPS Conference 2022 Conference Paper

Exploring evolution-aware & -free protein language models as protein function predictors

Mingyang Hu
Fajie Yuan
Kevin Yang
Fusong Ju
Jin Su
Hui Wang
Fei Yang
Qiuyang Ding

Large-scale Protein Language Models (PLMs) have improved performance in protein prediction tasks, ranging from 3D structure prediction to various function predictions. In particular, AlphaFold, a ground-breaking AI system, could potentially reshape structural biology. However, the utility of the PLM module in AlphaFold, Evoformer, has not been explored beyond structure prediction. In this paper, we investigate the representation ability of three popular PLMs: ESM-1b (single sequence), MSA-Transformer (multiple sequence alignment), and Evoformer (structural), with a special focus on Evoformer. Specifically, we aim to answer the following key questions: (1) Does the Evoformer trained as part of AlphaFold produce representations amenable to predicting protein function? (2) If yes, can Evoformer replace ESM-1b and MSA-Transformer? (3) How much do these PLMs rely on evolution-related protein data? In this regard, are they complementary to each other? We compare these models by empirical study along with new insights and conclusions. All code and datasets for reproducibility are available at https: //github. com/elttaes/Revisiting-PLMs.

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