Shuo Cheng

Hi there! I'm Shuo Cheng (成硕), I'm a Ph.D. student at Georgia Tech advised by Prof. Danfei Xu. I'm generally interested in robotics, computer vision, and machine learning, where my current focus is to enable robots with the ability to reason and perform in complex and highly variable environments for achieving long-horizon tasks. I'm open to discussions and collaborations, please feel free to drop me an email if you are interested.

Before studying at Georgia Tech, I'm also very fortunate to work with Prof. Hao Su, Prof. Ravi Ramamoorthi, Prof. Lin Shao, and Prof. Bingbing Ni.

[E-mail][Twitter][Google Scholar][Github][Curriculum Vitae]

Featured Publications (Full List)

NOD-TAMP: Generalizable Long-Horizon Planning with Neural Object Descriptors

Shuo Cheng, Caelan Garrett*, Ajay Mandlekar*, Danfei Xu

CoRL 2024

CoRL 2023, Workshop on Learning Effective Abstractions for Planning (Oral presentation)

[pdf][project]

A TAMP-based framework featuring neural object descriptors, capable of learning from only a handful of brief demonstrations yet exhibiting robust performance in long-horizon tasks involving diverse object shapes, poses, and goal configurations.

A Survey of Optimization-based Task and Motion Planning: From Classical To Learning Approaches

Zhigen Zhao, Shuo Cheng, Yan Ding, Ziyi Zhou, Shiqi Zhang, Danfei Xu, Ye Zhao

T-MECH 2024

[pdf]

A comprehensive review on popular TAMP solutions as well as their integrations with learning components. We also discuss challenges and potential future directions.

Neural Field Dynamics Model for Granular Object Piles Manipulation

Shangjie Xue, Shuo Cheng, Pujith Kachana, Danfei Xu

CoRL 2023

ICRA 2023, Workshop on Representing and Manipulating Deformable Objects (Oral presentation, Best Paper Finalist)

[pdf][project]

A new field-based representation (occupancy density) to model and optimize granular object manipulation.

LEAGUE: Guided Skill Learning and Abstraction for Long-Horizon Manipulation

Shuo Cheng, Danfei Xu

RA-L 2023 (Best Paper Honorable Mention, 1 of 5 over 1200+ accepted papers)

CoRL 2022, Long Horizon Planning Workshop (Oral presentation, Best Paper Finalist)

[pdf][project]

We use Task and Motion Planning (TAMP) as guidance for learning generalizable and composable sensorimotor skills.

Learning to Regrasp by Learning to Place

Shuo Cheng, Kaichun Mo, Lin Shao

CoRL 2021

[pdf][code][project]

We propose a point-cloud-based system for robots to transform the initial grasp pose to the desired grasp pose, where stable object placements serve as intermediate waypoints. A challenging synthetic dataset is introduced for learning and evaluating the proposed approach.

Deep Stereo using Adaptive Thin Volume Representation with Uncertainty Awareness

Shuo Cheng*, Zexiang Xu*, Shilin Zhu, Zhuwen Li, Li Erran Li, Ravi Ramamoorthi, Hao Su (*equal contribution)

CVPR 2020 (Oral presentation, top 5.7% accepted papers)

[pdf][code][talk]

We design a novel multi-stage framework that progressively sub-divides the vast scene space with increasing depth resolution and precision, which enables scene reconstruction with high completeness and accuracy in a coarse-to-fine fashion.

Fine-grained Video Captioning for Sports Narrative

Huanyu Yu*, Shuo Cheng*, Bingbing Ni*, Minsi Wang, Jian Zhang, Xiaokang Yang (*equal contribution)

CVPR 2018 (Spotlight presentation, top 6.7% accepted papers)

[pdf]

We develop a well-modularized pipeline for automatic sports game narration. We incorporate human pose and optical flow to depict the movements of each player, which facilitates action recognition and human interaction learning.

Service

Reviewer: RSS, CVPR, ICML, NeurIPS, RA-L, CoRL, ICLR, T-PAMI, ICCV, ECCV, T-ASE, T-MECH, ICRA, Auton. Robots, IROS, CASE, T-MM, AAAI, Neurocomputing, CIKM, WACV, J. Supercomput., ACCV, T-CSVT

Co‐organizer: Learning for Task and Motion Planning Workshop, RSS 2023