Search arXiv⌕ Search

arXiv · 2610.24256

TrajEdit: Real-Time Flow-Matching Trajectory Adaptation via FlowEdit and CBF Safety Filter

Abstract

Safe, real-time motion planning is especially difficult when a dynamic obstacle actively pursues the ego agent, requiring rapid adaptation as the environment changes. We present TrajEdit, a hierarchical trajectory planner for pursuit-evasion that combines flow matching, trajectory editing, and safety filtering. Rather than regenerating trajectories from scratch at every step, TrajEdit follows the current plan under nominal conditions and adapts its remaining portion through FlowEdit as the pursuer approaches, and falls back to a control barrier function safety filter if the adversarial agent is within a safety threshold. Our use of FlowEdit for trajectory editing reduces computation time while maintaining a comparable success rate. We evaluate TrajEdit in 2D pursuit-evasion with static obstacles and an adversarial pursuer, demonstrating high success rates with low capture and collision rates, while achieving computational time savings over full replanning baselines.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Wondmgezahu Teshome, Arya Honarpisheh, Octavia Camps, Mario Sznaier. 2026-10-16. TrajEdit: Real-Time Flow-Matching Trajectory Adaptation via FlowEdit and CBF Safety Filter. https://arxiv.org/abs/2610.24256

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

AnyNav: Visual Neuro-Symbolic Friction Learning for Off-road Navigation

Off-road navigation is critical for a wide range of field robotics applications from planetary exploration to disaster response. However, it remains a longstanding challenge due to unstructured environments and the inherently complex terrain-vehicle interactions. Traditional physics-based methods struggle to accurately capture the nonlinear dynamics underlying these interactions, while purely data-driven approaches often overfit to specific motion patterns, vehicle geometries, or platforms, limiting their generalization in diverse, real-world scenarios. To address these limitations, we introduce AnyNav, a vision-based friction estimation and navigation framework grounded in neuro-symbolic principles. Our approach integrates neural networks for visual perception with symbolic physical models for reasoning about terrain-vehicle dynamics. To enable self-supervised learning in real-world settings, we adopt the imperative learning paradigm, employing bilevel optimization to train the friction network through physics-based optimization. This explicit incorporation of physical reasoning substantially enhances generalization across terrains, vehicle types, and operational conditions. Leveraging the predicted friction coefficients, we further develop a physics-informed navigation system capable of generating physically feasible, time-efficient paths together with corresponding speed profiles. We demonstrate that AnyNav seamlessly transfers from simulation to real-world robotic platforms, exhibiting strong robustness across different four-wheeled vehicles and diverse off-road environments.

cs.RO↗

LLM-Flock: LLM-Driven Decentralized Multi-Robot Flocking via Influence-Based Consensus with Collision Avoidance

Large Language Models (LLMs) have shown strong capabilities in reasoning and planning, prompting researchers to explore their use as decentralized decision-makers for multi-robot formation control. However, directly applying LLMs to such tasks often leads to unstable behavior, in which robots may collapse to the centroid of their positions or diverge entirely, due to hallucinated reasoning and limited coordination awareness. We propose LLM-Flock, a decentralized multi-robot flocking framework that addresses these challenges through two key mechanisms: (1) an influence-based consensus protocol, where robots iteratively refine individually generated formation plans by adopting those of more connected neighbors, driving the team toward a unified plan without centralized control, and (2) a two-layer collision avoidance strategy that pairs LLM-based urgency reasoning with a programmatic collision checker and re-prompting mechanism. We evaluate LLM-Flock across five state-of-the-art LLMs, including both closed-source (GPT-5, Claude 4.5) and open-source (Llama3.1-405b, Qwen-Max, DeepSeek-R1) models, demonstrating notable improvements in formation stability and convergence over prior LLM-based methods. Real-world experiments with Crazyflie drones further validate the framework's applicability to physical multi-robot systems.

cs.RO↗

RoboPocket: Improve Robot Policies Instantly with Your Phone

Scaling robot learning is bottlenecked not only by data volume, but also by whether the data targets the policy's failures. Existing robot-free handheld interfaces enable portable in-the-wild data collection, yet remain open-loop: users record demonstrations without feedback from the current policy. Interactive methods such as DAgger provide such feedback by collecting corrective on-policy data, but require deploying imperfect policies on physical robots, making them costly, unsafe, and difficult to scale. We present RoboPocket, a smartphone-based system for robot-free policy iteration. By combining on-device sensing, display, and communication with cloud-side policy inference, RoboPocket provides real-time collection guidance and visualizes the policy's predicted trajectory in augmented reality as AR Visual Foresight. This allows users to identify policy weaknesses and collect targeted corrective data without deploying a physical robot. An asynchronous finetuning pipeline continuously incorporates new corrections and updates the policy within minutes, realizing a DAgger-inspired robot-free correction loop. Across diverse real-world manipulation tasks, we validate RoboPocket as a scalable data engine and show that online corrective data collection improves sample efficiency by up to $2\times$ over scaling offline demonstrations. Project page and videos: https://robo-pocket.github.io.

cs.RO↗