Joey Ruan

2026

1. ICRA

   

   Revisiting Replanning from Scratch: Real-Time Incremental Planning with Fast Almost-Surely Asymptotically Optimal Planners

   Mitchell E. C. Sabbadini, Andrew H. Liu, Joseph Ruan, Tyler S. Wilson, Zachary Kingston, and Jonathan D. Gammell

   In IEEE International Conference on Robotics and Automation

   Abs Bib PDF Video

   Robots operating in changing environments either predict obstacle changes and/or plan quickly enough to react to them. Predictive approaches require a strong prior about the position and motion of obstacles. Reactive approaches require no assumptions about their environment but must replan quickly and find high-quality paths to navigate effectively. Reactive approaches often reuse information between queries to reduce planning cost. These techniques are conceptually sound but updating dense planning graphs when information changes can be computationally prohibitive. It can also require significant effort to detect the changes in some applications. This paper revisits the long-held assumption that reactive replanning requires updating existing plans. It shows that the incremental planning problem can alternatively be solved more efficiently as a series of independent problems using fast almost-surely asymptotically optimal (ASAO) planning algorithms. These ASAO algorithms quickly find an initial solution and converge towards an optimal solution which allows them to find consistent global plans in the presence of changing obstacles without requiring explicit plan reuse. This is demonstrated with simulated experiments where Effort Informed Trees (EIT*) finds shorter median solution paths than the tested reactive planning algorithms and is further validated using Asymptotically Optimal RRT-Connect (AORRTC) on a real-world planning problem on a robot arm.

   @inproceedings{sabbadini2026replan,
     author = {Sabbadini, Mitchell E. C. and Liu, Andrew H. and Ruan, Joseph and Wilson, Tyler S. and Kingston, Zachary and Gammell, Jonathan D.},
     booktitle = {IEEE International Conference on Robotics and Automation},
     doi = {10.1109/ICRA57385.2026.11697206},
     pages = {9369--9376},
     title = {Revisiting Replanning from Scratch: Real-Time Incremental Planning with Fast Almost-Surely Asymptotically Optimal Planners},
     year = {2026}
   }

2. arXiv

   Safe Real-Time Policy Steering via Noise-Space Trajectory Optimization for One-Step Generative Policies

   Qingyi Chen, Joseph Ruan, and Zachary Kingston

   Under Review

   Abs Bib PDF Video

   Generative robot policies can represent diverse, multimodal behaviors, but adapting pretrained policies to deployment-time constraints such as collision avoidance and orientation maintenance remains challenging. Existing inference-time steering methods typically apply gradient guidance through iterative diffusion or flow processes, which can be computationally expensive for real-time control. We propose INSPO, which formulates inference-time steering of one-step generative policies as trajectory optimization in the policy’s input noise space. By optimizing the input noise while evaluating constraints on the induced state trajectory, INSPO searches the policy-induced behavior space without directly modifying generated actions. The optimization includes a regularization term that encourages solutions to remain consistent with the policy’s input distribution and is solved online using population-based particle optimization. We evaluate INSPO on state- and image-based task-specific policies and generalist vision-language-action policies across Push-T, Can pick-and-place, and LIBERO-Spatial. INSPO improves task success and constraint satisfaction over best-of-N sampling and action projection, while comparing favorably with gradient-guided generation at lower runtime.

   @misc{chen2026inspo,
     archiveprefix = {arXiv},
     author = {Chen, Qingyi and Ruan, Joseph and Kingston, Zachary},
     eprint = {2609.21220},
     note = {Under Review},
     primaryclass = {cs.RO},
     title = {Safe Real-Time Policy Steering via Noise-Space Trajectory Optimization for One-Step Generative Policies},
     year = {2026}
   }