Dynamic Replanning

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17 papers2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Execution-Aligned Progressive Noise for Consistent Asynchronous Replanning in Generative Robot Policies
Oct 5, 2026Di Wu, Ping Liu, Xuhua Chen +3Robot Policy Learning Diffusion-Based Planning
Magiclab Robotics Technology Co., Ltd., China · Southeast University, Nanjing, China
ADM-Planner: LLM-Guided Long-Horizon Planning for Mobile Manipulators with Attention-Enhanced Dynamic Memory
Sep 24, 2026Jiaping Xiao, Pingyuan Ji, Mir FeroskhanMemory-Augmented Language Model Agents Mobile Manipulation
School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
Bayesian Partner Modelling enables Adaptive Replanning for LLM Coordination
Aug 19, 2026Harsh Goel, Aditya Sai Ellendula, Vaishnav Tadiparthi +3Dynamic Replanning Multi-Agent LLM Systems
The University of Texas at Austin · Honda Research Institute, USA
Rethink Before You Execute: Adaptive Execution for World Action Models
Aug 10, 2026Feng Ye, Yiming Zhao, Yong Yu +5World Models for Robotics LLM Planning
Wangxuan Institute of Computer Technology, Peking University · Simplexity Robotics
Continue or Replan? Bernoulli-Continuation Policy Learning for Adaptive Horizon Execution
Aug 4, 2026Weichen Xu, Zhenhua Liu, Lin Luo +8Efficient VLA Model Inference Robotic Manipulation
1Peking University · 2Microsoft Research Asia
When Replanning Becomes the Bottleneck: Budgeted Replanning for Embodied Agents
Aug 2, 2026Shuaijun Liu, Feiyang You, Xingwei Chen +1Large Language Model-Based Robot Planning Token Pruning
IoT, Information Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China.
$π\mathbf{R}^2$ : Reactive Real-time Flow Policies
Jul 28, 2026Sungjae Park, Shubham TulsianiVisuomotor Policy Learning Flow Matching
Carnegie Mellon University
Towards Reliable Aerial Ground Vehicle Collaboration: An Integrated Planning and Autonomy Framework for Field Deployment
Jul 7, 2026Md Safwan Mondal, Luca Russo, James D. Humann +2Vehicle Routing Multi-Robot Coordination
Department of Mechanical and Industrial Engineering, University of Illinois Chicago, IL, 60607 USA · DEVCOM Army Research Laboratory, Minneapolis, Minnesota, USA · DEVCOM Army Research Laboratory, Aberdeen Proving Grounds, Aberdeen, MD 21005 USA
AdaReP:Adaptive Re-Planning under Model Mismatch for Neural World-Model Predictive Control
Jun 22, 2026Yutian Cheng, Xiaojian Ma, Xianhao Wang +6World Models for Robotics Sequential Decision Making
Shanghai Jiao Tong University · Beijing Institute for General Artificial Intelligence (BIGAI) · University of Science and Technology of China
When Should a Robot Replan? Regret-Guided Update Scheduling in Time-Varying MDPs
Jun 15, 2026Negin Musavi, Gokul Puthumanaillam, Ruben Hernandez +2Non-Stationary RL Robot Policy Adaptation
Department of Aerospace Engineering, University of Illinois Urbana–Champaign, Champaign, IL 61820, USA
Denoising Tells When to Replan: Denoising-Variance Adaptive Chunking for Flow-Based Robot Policies
Jun 2, 2026Xiangdong Feng, Yuxuan Cheng, Chen Shi +5Robot Policy Learning Action Chunking
Beijing Institute of Technology · Shenzhen Loop Area Institute · The Chinese University of Hong Kong, Shenzhen +4
STT-Arena: A More Realistic Environment for Tool-Using with Spatio-Temporal Dynamics
May 18, 2026Tingfeng Hui, Hao Xu, Pengyu Zhu +5Spatiotemporal Reasoning LLM Planning
Hong Kong Institute of AI for Science, City University of Hong Kong · Beijing University of Posts and Telecommunications · Li Auto Inc. +2
Should I Replan? Learning to Spot the Right Time in Robust MAPF Execution
Apr 28, 2026David Zahrádka, David Woller, Denisa Mužíková +2Robust MAPF Multi-Agent Path Finding
Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague · Faculty of Electrical Engineering, Czech Technical University in Prague · Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Czechia. +1
TAPE: Tool-Guided Adaptive Planning and Constrained Execution in Language Model Agents
Feb 23, 2026Jongwon Jeong, Jungtaek Kim, Kangwook LeeDynamic Replanning LLM Agent Planning
Electrical and Computer Engineering, University of Wisconsin–Madison · KRAFTON · Ludo Robotics
Precomputing Multi-Agent Path Replanning Using Temporal Flexibility
Jan 8, 2026Issa Hanou, Eric Kemmeren, Devin Wild Thomas +1Safe Interval Path Planning Multi-Agent Path Finding
Delft University of Technology · University of New Hampshire
CostBench: Evaluating Multi-Turn Cost-Optimal Planning and Adaptation in Dynamic Environments for LLM Tool-Use Agents
Nov 4, 2025Jiayu Liu, Cheng Qian, Zhaochen Su +4LLM Planning LLM Agent Evaluation
The Hong Kong University of Science and Technology · University of Illinois Urbana-Champaign · Tsinghua University
Video Replanning via Latent Embedding Refinement and Rejection-Based Sampling
Oct 20, 2025Po-Chen Ko, Yu Chan, Yu-Hsiang Fu +7Robot System Identification Robot Failure Recovery
National Taiwan University · MIT CSAIL · Cornell University +1

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Execution-Aligned Progressive Noise for Consistent Asynchronous Replanning in Generative Robot Policies

Di Wu, Ping Liu, Xuhua Chen, He Zheng+2

Magiclab Robotics Technology Co., Ltd., China · Southeast University, Nanjing, China

Robot Policy Learning Diffusion-Based Planning Robot Motion Generation Dynamic Replanning

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Continuous asynchronous replanning is essential for real-time generative robot policies, but independent stochastic initialization can cause mode switching and inconsistent continuation across action chunks. We propose Execution-Aligned Progressive Noise (EAPN), which introduces structured stochasticity at both inter-chunk and intra-chunk levels. Across replanning steps, EAPN propagates a shared noise trajectory and aligns it with the actual execution displacement, establishing execution-aligned inter-chunk correlation. Within each action chunk, it models temporal correlation along action time. The aligned stochastic history is further combined with committed action context to condition subsequent generation, allowing new chunks to continue from execution-consistent generative states rather than restart from independent noise. We evaluate EAPN on D3IL, Kinetix, LIBERO, and real-world manipulation tasks. EAPN improves multimodal behavior consistency on D3IL and achieves an average success rate of 88.59% on Kinetix. On LIBERO, it remains robust and maintains strong task performance even under long inference delays. Real-robot experiments further achieve 90.0% success on Object Storage and 96.7% on bimanual Cloth Folding, demonstrating reliable continuous execution under asynchronous replanning.