Renming Huang

I am a researcher at Shanghai Jiao Tong University (SJTU), working at the intersection of Robotics and Reinforcement Learning. My research focuses on enabling autonomous agents to learn from imperfect demonstrations and generalize to long-horizon, open-world tasks.

News

Apr 2026 Paper Mimic Intent, Not Just Trajectories accepted at RSS 2026.
Feb 2026 Preprint Any House Any Task: Scalable Long-Horizon Planning for Abstract Human Tasks posted on arXiv.
Oct 2025 Paper AttentionAR: AR Adaptation and Warning for Real-World Safety via Attention Modeling and MLLM Reasoning accepted at UIST 2025.
Nov 2024 Paper Goal-Reaching Policy Learning from Non-Expert Observations via Effective Subgoal Guidance accepted at CoRL 2024.
Sep 2024 Paper Diffusion Models as Optimizers for Efficient Planning in Offline RL accepted at ECCV 2024.
Mar 2023 Paper Multimodal Apology: Using WebXR to Repair Trust with Virtual Companion accepted at IEEE VR 2023.

Selected Publications

Mimic Intent, Not Just Trajectories

Renming Huang, Chengyang Zeng, Weichao Tang, Junyi Cai, Cewu Lu, Panpan Cai

Robotics: Science and Systems RSS 2026 arXiv Code — We propose learning to mimic the underlying intent of demonstrations rather than directly copying trajectories, enabling more robust and generalizable robot behavior.

Any House Any Task: Scalable Long-Horizon Planning for Abstract Human Tasks

Zhihong Liu, Yang Li, Renming Huang, Cewu Lu, Panpan Cai

arXiv preprint arXiv arXiv — A scalable framework for long-horizon task planning that generalizes across diverse house layouts and abstract human task specifications.

Goal-Reaching Policy Learning from Non-Expert Observations via Effective Subgoal Guidance

Renming Huang, Shenyu Liu, Yuying Pei, Peng Wang, Guansong Wang, Yang Yang, Heng Shen

Conference on Robot Learning CoRL 2024 arXiv — We propose a subgoal-guided imitation learning framework that enables goal-reaching policy learning from non-expert, suboptimal observations without requiring expert demonstrations.

Diffusion Models as Optimizers for Efficient Planning in Offline RL

Renming Huang, Yuying Pei, Guansong Wang, Yanjiang Guo, Yang Yang, Peng Wang, Heng Shen

European Conference on Computer Vision ECCV 2024 arXiv — We leverage diffusion models as trajectory optimizers for offline reinforcement learning, achieving efficient planning by treating diffusion sampling as an optimization process.

See the full list on the Publications page or Google Scholar.