Weijun Dong

Ph.D. student @ucsd_cse | Robot Learning | Prev: M.Eng. & B.Eng. @Tsinghua_IIIS

[Weijun Dong @dwjshift]

Learning from vast video data allows point-flow-based planners to create generalizable task plans, guiding robots via future keypoint trajectories.

But how can we ensure low-level execution doesn't become the system's bottleneck?

Introducing our #ICLR2026 paper, HinFlow: Translating Flow to Policy via Hindsight Online Imitation

[Runhan Huang @RunhanH]

Flexible Locomotion Learning with Diffusion Model Predictive Control

Excited to share that our paper has been accepted to #ICRA2026 @ieee_ras_icra!

A diffusion-planning framework for flexible real-world quadruped locomotion. Instead of learning a fixed RL policy or relying on hand-crafted dynamics for MPC, we train a diffusion trajectory prior that jointly predicts future states and actions.

Key Ideas:

Diffusion-MPC: A diffusion planner unlocks flexible locomotion through test-time reward and constraint adaptation
Interactive reward-weighted finetuning enables continual behavior refinement from online environment feedback
Real-world deployment on Unitree Go2 with efficient and adaptive planning

The same planner can adapt at test time to height changes, posture/joint constraints, balancing under external disturbances, energy-aware locomotion, and zero-shot outdoor walking on grass and slopes.

🌐Homepage: https://t.co/TSXUZAL5nT
📖Paper: https://t.co/de4dUZf5AA
🔗Code: https://t.co/NLFB1alhWJ

This work is by @RunhanH, Haldun Balim, @hankyang94 , and @du_yilun.

#ICRA2026 #Robotics #LeggedRobots #RobotLearning #DiffusionModels #MPC #MachineLearning

[Zhangchen Ye @YeZhangchen0731]

Proud to be a co-first author of this work! 🎉
Really excited to share our #ICLR2026 paper — HinFlow: Translating Flow to Policy via Hindsight Online Imitation.
Great collaboration with the entire team!

[Weijun Dong @dwjshift]

Huge thanks to collaborators and supervisors: Yitian Zheng, @YeZhangchen0731, @ShengjieWa34067, Yuyang Liu, Chongjie Zhang, @ChuanWen15, and @gao_young!

[Weijun Dong @dwjshift]

Learning from vast video data allows point-flow-based planners to create generalizable task plans, guiding robots via future keypoint trajectories.

But how can we ensure low-level execution doesn't become the system's bottleneck?

Introducing our #ICLR2026 paper, HinFlow: Translating Flow to Policy via Hindsight Online Imitation

[Weijun Dong @dwjshift]

We hope our work hints at the broader potential of hindsight relabeling — perhaps as a useful ingredient in the era of scaling up robotic foundation models.

Check for more details!
Webpage: https://t.co/9dMDYZX4n0
Arxiv: https://t.co/9KkrWjztKr
Code: https://t.co/98OMINVGhR