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Peiqi Liu | 刘沛淇
I am a senior undergraduate student major in computer science at Peking University, advised by
Prof. Hao Dong as a part of PKU-Agibot Lab.
Previously, I was a visiting student at MIT CSAIL, where I
worked in the groups
led by Prof. Leslie Pack Kaelbling and Prof. Josh Tenenbaum, advised by Jiayuan Mao. I also spent a semester on exchange at the
University of California,
Berkeley, and interned with the BAIR.
Always excited to discussing cool ideas and potential collaborations. Feel free to book a meeting
with me!
Email /
Github /
LinkedIn /
Book a Meeting
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Research
My long-term research goal is to build robots that can acquire new skills as efficiently as humans,
generalize across
diverse tasks, and perform everyday physical labor. I am particularly interested in exploring novel
algorithms and
representations that improve the efficiency of robot skill acquisition and adaptation, enabling
one-shot and zero-shot
learning.
Now: I am working toward becoming a full-stack robotics
researcher while learning both robotics algorithms and hardware.
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BiDexAffordance: Learning Collaborative Affordances for Efficient Bimanual
Dexterous Grasping
Peiqi Liu, Jingwen Li, Zeyuan Chen, Yue Chen, Shuqi Zhao, Yuanpei Chen, Chenfeng Xu,
Masayoshi Tomizuka, Wei Zhan, Ruihai Wu
ECCV 2026 Under Review
BiDexAffordance is a collaborative affordance-driven framework that learns object-centric,
physics-grounded bimanual affordance maps to efficiently generate robust and generalizable bimanual
dexterous grasps across diverse and unseen objects.
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Lifelong Experience Abstraction and Planning
Peiqi Liu,
Joshua B. Tenenbaum,
Leslie Pack Kaelbling,
Jiayuan Mao
ICML 2025 Workshop PRAL (Oral)
Project Page
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Paper
A framework for lifelong experience abstraction and planning that enables agents to learn and adapt
continuously
across different environments and tasks.
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MO-DDN: A Coarse-to-Fine Attribute-based Exploration Agent for Multi-object
Demand-driven Navigation
Hongcheng Wang*,
Peiqi Liu*,
Wenzhe Cai,
Mingdong Wu,
Zhengyu Qian,
Hao Dong
NeurIPS 2024
Project Page
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arXiv
We propose a multi-object demand-driven navigation benchmark and train an coarse-to-fine
attribute-based exploration
agent to solve this task.
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Template, Last updated: Apr 2026 © Peiqi Liu
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