UniConFlow is a unified constrained flow‑matching framework that generates robot trajectories while systematically handling both equality and inequality constraints such as collision avoidance, actuation limits, and dynamic consistency. It introduces a prescribed‑time zeroing function to shape a time‑varying guidance field during inference, and employs a violation‑segment extraction protocol and trajectory compression to address computational challenges in long‑horizon, high‑dimensional planning. Experiments on a double inverted pendulum, a real‑to‑sim car racing task, and a sim‑to‑real manipulation task show that UniConFlow outperforms state‑of‑the‑art generative planners and conventional optimization baselines on certified motion planning metrics like safety, kinodynamic consistency, and action feasibility.
By Zewen Yang, Xiaobing Dai, Dian Yu, Zhijun Li, Majid Khadiv, Sandra Hirche, Sami Haddadin
Recent advances in generative planning have made trajectory inpainting a promising approach to offline goal-conditioned reinforcement learning. However, these methods typically specify the planning ho...
HorizonFlow is a hierarchical planner for offline goal-conditioned reinforcement learning that treats the planning horizon as an output rather than a fixed input. It uses a subgoal route planner and an action-prefix controller, both employing insertion-based generation and flow matching, to jointly generate continuous plan content and its length. The method leverages the partially generated plan to guide token insertion and to steer generation toward shorter plans, achieving superior performance on Maze2D, Multi2D, and OGBench benchmarks.
By JunHyeok Oh, Zian Jang, Byung-Jun Lee
arXiv:2504. 16738v3 Announce Type: replace-cross Abstract: Planning long-horizon manipulation motions using a set of predefined skills is a central challenge in robotics; solving it efficiently could enable general-purpose robots to tackle novel tasks by flexibly composing generic skills.
By Itamar Mishani, Yorai Shaoul, Maxim Likhachev
arXiv:2602.20119v2 Announce Type: replace-cross
Abstract: Solving complex long-horizon robotic tasks requires joint reasoning over abstract task structure and low-level physical interaction. While co...
By Jiahui Fu, Junyu Nan, Lingfeng Sun, Hongyu Li, Jianing Qian, Benjamin Yang, Yilun Du, Jennifer L. Barry, Kris Kitani, George Konidaris
Reward‑Guided RRT* is a sampling‑based framework that builds budget‑feasible robotic missions by iteratively selecting rewarding locations and planning motions directly in continuous state space, without a pre‑computed task‑transition graph. The method uses reward‑guided RRT* searches and a return‑feasibility check to ensure each task can be visited within the travel budget. Experiments on increasingly complex obstacle environments and task counts show solutions comparable to VNS‑PRM* while sampling far fewer states, and the framework is extended to Dubins steering and validated on a scaled car‑like robot in CoppeliaSim.
By Vin\'icius Ara\'ujo de Oliveira, Gabriel Ferreira Mendes Louren\c{c}o, Paulo L. J. Drews-Jr, Armando Alves Neto, Douglas G. Macharet
arXiv:2606. 30940v1 Announce Type: cross Abstract: Deep learning methods have vastly expanded the capabilities of motion planning in robotics applications, as learning priors from large-scale data has been shown to be essential in capturing the highly complex behavior required for solving tasks such as manipulation or navigation for autonomous vehicles.
By Lukas Lao Beyer, Sertac Karaman
Shape-Aware Neural Time Fields is a physics‑informed neural motion planning framework that learns a reusable travel‑time (cost‑to‑go) representation for rigid‑body motion, explicitly accounting for distinct geometric restrictions on translation and rotation. The learned field captures local motion difficulty and is combined with a sampling‑based planner that balances progress toward the goal with the cost of reaching nearby poses. Experiments on 2D and 3D planning tasks and non‑prehensile manipulation show substantial improvements in planning success and inference efficiency over neural baselines, while remaining competitive with classical planners at lower planning times, and closed‑loop pushing tests demonstrate fast replanning for reactive execution.
By Yanming Zhou, Yuchen Liu, Ahmed H. Qureshi
The paper introduces CASTER, a framework that formalizes the conversion of synthetic videos into real robot trajectories as a constrained optimization problem. It shows that synthetic videos must probabilistically seed the search within a semantic basin of attraction, allowing local trajectory refinement while maintaining task alignment. By removing simplifying assumptions from existing methods, the authors achieve more robust zero‑shot manipulation and improve task success by 34.1% over the best baseline.
By Yinlong Dai, Homanga Bharadhwaj, Dylan P. Losey
FlowCorrect is a modular interactive imitation learning method that allows real‑time adaptation of generative flow‑matching manipulation policies using sparse, relative human corrections. During task execution, a human provides brief corrective pose nudges through a lightweight VR interface, and FlowCorrect locally adapts the policy without retraining the backbone, maintaining performance on previously learned scenarios. Experiments on a real‑world robot across four tabletop tasks show that, with a low correction budget, FlowCorrect achieves an 80% success rate on previously failed cases while preserving performance on solved scenarios.
By Edgar Welte, Yitian Shi, Rosa Wolf, Maximillian Gilles, Rania Rayyes
Dex-One2Many introduces a real-to-sim-to-real framework that learns a generalizable dexterous manipulation policy from a single human video. By abstracting the video into sequential scene graphs, the method guides reinforcement learning with dense rewards and diverse reset states, enabling efficient exploration while preserving broad generalizability. Trained entirely in simulation, Dex-One2Many achieves zero‑shot transfer to a real multi‑fingered hand and outperforms baselines by 6.5% on seen configurations and 71% on unseen scenarios across five tool‑use and manipulation tasks.
By Jusuk Lee, Sungha Kim, Yeonsoo Park, Jonguk Cheon, Yoonkyo Jung, Yongjun You, H. Jin Kim, Jia-Bin Huang, Furong Huang, Youngseok Jang, Seungjae Lee
AegisFlow is a training‑free framework that guides generative models, such as diffusion and flow matching, along safe paths by formulating the problem as a global optimal control task. It incorporates a time‑varying barrier function into the cost functional, enabling non‑myopic enforcement of constraints throughout the entire generative trajectory. Experiments show that AegisFlow outperforms existing methods in text‑guided image manipulation and robotic planning, achieving a better balance between target alignment and constraint satisfaction.
By Kunpeng Liu, Boshi Zhang, Keyou You