Yuejiang LIU

Scaling Verification Can Be More Effective than Scaling Policy Learning for Vision-Language-Action Alignment.
J Kwok, X Zhang, M Xu, Y Liu^, A Mirhoseini^, C Finn^, M Pavone^
In submission. 2026. [pdf, code]

RoboMME: Benchmarking and Understanding Memory for Robotic Generalist Policies.
Y Dai, H Fu, J Lee, Y Liu, H Zhang, J Yang, C Finn, N Fazeli, J Chai
In submission. 2026. [pdf, code]

Short-to-Long Distillation: Learning Long-Context Policies from Short-Context Supervision.
Y Liu*, Y Qian*, Y Du, C Finn
In submission. Short version at NeurIPS ER, 2025.

Learning Long-Context Diffusion Policies via Past-Token Prediction.
M Villasevil*, A Tang*, Y Liu*, C Finn
Conference on Robot Learning (CoRL). Best paper at RSS RoboReps, 2025. [pdf, code]

Demo SCORE: Curating Demonstrations using Online Experience.
A Chen, A Lessing, Y Liu, C Finn
Robotics: Science and Systems (RSS), 2025. [pdf, code]

Bidirectional Decoding: Improving Action Chunking via Guided Test-Time Sampling.
Y Liu*, J Hamid*, A Xie, Y Lee, M Du, C Finn
International Conference on Learning Representations (ICLR), 2025. [pdf, code]

Targeted Data Selection via Optimal Transport.
L Feng, F Nie, Y Liu^, A Alahi^.
International Conference on Machine Learning (ICML), 2025. [pdf, code]

Sim-to-Real Causal Transfer: A Metric Learning Approach to Causally-Aware Interaction Representations.
A Rahimi*, P Luan*, Y Liu*, F Rajic, A Alahi.
Conference on Computer Vision and Pattern Recognition (CVPR), 2025. [pdf, code]

Co-Supervised Learning: Improving Weak-to-Strong Generalization with Hierarchical Mixture of Experts.
Y Liu, A Alahi.
Preprint. 2024. [pdf, code]

On Pitfalls of Test-time Adaptation.
H Zhao*, Y Liu*, A Alahi, T Lin.
International Conference on Machine Learning (ICML), 2023. [pdf, code]

Causal Triplet: An Open Challenge for Intervention-centric Causal Representation Learning.
Y Liu, A Alahi, C Russell, M Horn, D Zietlow, B Schölkopf, F Locatello.
Conference on Causal Learning and Reasoning (CLeaR), 2023. [pdf, code]

Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective.
Y Liu, R Cadei, J Schweizer, S Bahmani, A Alahi.
Conference on Computer Vision and Pattern Recognition (CVPR), 2022. [pdf, code]

Modular Low-Rank Style Transfer for Deep Motion Forecasting.
P. Kothar*, D. Li*, Y Liu, A Alahi.
Conference on Robot Learning (CoRL), 2022. [pdf, code]

TTT++: When Does Self-supervised Test-time Training Fail or Thrive?
Y Liu, P Kothari, B Delft, B Bellot-Gurlet, T Mordan, A Alahi.
Conference on Neural Information Processing Systems (NeurIPS), 2021. [pdf, code]

Social NCE: Contrastive Learning of Socially-aware Motion Representations.
Y Liu, Q Yan, A Alahi.
International Conference on Computer Vision (ICCV), 2021. [pdf, code]

Collaborative Sampling from Generative Adversarial Networks.
Y Liu*, P Kothari*, A Alahi.
Conference on Artificial Intelligence (AAAI), 2020. [pdf, code]

Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning.
C Chen, Y Liu, S Kreiss, A Alahi.
International Conference on Robotics and Automation (ICRA), 2019. [pdf, code]

Map-based Deep Imitation Learning for Obstacle Avoidance.
Y Liu, A Xu, Z Chen.
International Conference on Intelligent Robots and Systems (IROS), 2018. [pdf]

Real-Time Distributed Algorithms for Nonconvex Optimal Power Flow.
Y Liu, JH Hours, G Stathopoulos, CN Jones.
American Control Conference (ACC), 2017. [pdf]

Review: NeurIPS, ICLR, ICML, CVPR, ICCV, RSS, CoRL, JMLR, PAMI

Organize: CoRL Data in Robotics Workshop