1 citations · 1 across the 8 of their papers we have counts for
8 papers
IPD: Boosting Sequential Policy with Imaginary Planning Distillation in Offline Reinforcement Learning
Yihao Qin, Yuanfei Wang, Hang Zhou +3
Decision transformer based sequential policies have emerged as a powerful paradigm in offline reinforcement learning (RL), yet their efficacy remains constrained by the quality of…
Physics-informed Diffusion Mamba Transformer for Real-world Driving
Hang Zhou, Qiang Zhang, Peiran Liu +3
Autonomous driving systems demand trajectory planners that not only model the inherent uncertainty of future motions but also respect complex temporal dependencies and underlying p…
Traj2Action: A Co-Denoising Framework for Trajectory-Guided Human-to-Robot Skill Transfer
Han Zhou, Jinjin Cao, Liyuan Ma +2
Learning diverse manipulation skills for real-world robots is severely bottlenecked by the reliance on costly and hard-to-scale teleoperated demonstrations. While human videos offe…
TopoNav: Topological Graphs as a Key Enabler for Advanced Object Navigation
Peiran Liu, Qiang Zhang, Daojie Peng +6
Object Navigation (ObjectNav) has made great progress with large language models (LLMs), but still faces challenges in memory management, especially in long-horizon tasks and dynam…
NeSyPack: A Neuro-Symbolic Framework for Bimanual Logistics Packing
Bowei Li, Peiqi Yu, Zhenran Tang +4
This paper presents NeSyPack, a neuro-symbolic framework for bimanual logistics packing. NeSyPack combines data-driven models and symbolic reasoning to build an explainable hierarc…
A Value Function Space Approach for Hierarchical Planning with Signal Temporal Logic Tasks
Peiran Liu, Yiting He, Yihao Qin +2
Signal Temporal Logic (STL) has emerged as an expressive language for reasoning intricate planning objectives. However, existing STL-based methods often assume full observation and…
Physics-informed Imitative Reinforcement Learning for Real-world Driving
Hang Zhou, Yihao Qin, Dan Xu +1
Recent advances in imitative reinforcement learning (IRL) have considerably enhanced the ability of autonomous agents to assimilate expert demonstrations, leading to rapid skill ac…
Enhance Planning with Physics-informed Safety Controller for End-to-end Autonomous Driving
Hang Zhou, Haichao Liu, Hongliang Lu +3
Recent years have seen a growing research interest in applications of Deep Neural Networks (DNN) on autonomous vehicle technology. The trend started with perception and prediction…