trajectory data collection

To address the slow initial solution generation and challenging online optimization in robotic end-effector trajectory tracking, this paper proposes an anytime planning framework capable of interruption and continuous refinement. Methodologically, it integrates kinematic modeling, heuristic sampling, and real-time replanning to reformulate two mainstream algorithms—A* and RRT*. Its key contributions are: (1) the first adaptation of graph-search algorithms to an anytime paradigm; and (2) a guided-path-based directional deviation sampling strategy that jointly optimizes initial solution speed and progressive accuracy improvement. Experimental evaluation across three benchmark scenarios demonstrates an average 3.2× reduction in time-to-first-solution, a 37% decrease in trajectory tracking error, and significantly enhanced convergence stability.

To address the lack of standardized datasets, inconsistent preprocessing protocols, and non-uniform evaluation metrics in UAV trajectory prediction research, this paper proposes the first comprehensive standardization framework for the field. We introduce an integrated pipeline encompassing data cleaning, coordinate normalization, multi-granularity evaluation (ADE, FDE, and collision detection), and interactive visualization. We publicly release Dronalize—a Python-based end-to-end toolbox built on NumPy, Pandas, Matplotlib, and Plotly—that supports seamless adaptation to major benchmarks including UAV123 and DroneVehicle, and incorporates customizable modules such as physics-aware collision detection. Experiments demonstrate a 70% average reduction in preprocessing time; consistent and comparable evaluation results across six state-of-the-art models; and broad adoption, evidenced by over 320 GitHub stars and widespread use in academia.

Industrial research agents often generate experimental trajectories containing invalid or incomplete information, rendering them unreliable for direct decision-making. This work proposes an evidence-oriented framework that automatically transforms such trajectories into structured evidence through a context-isolated generate–verify–repair pipeline. The approach introduces intervention-level claim categorization—distinguishing actionable repairs, diagnostic safeguards, and retained discoveries—and incorporates end-to-end provenance tracking to enable claim scoping and auditability. Experimental results demonstrate that the resulting candidate solutions outperform existing baselines. Audits further reveal that trajectory evolution is non-monotonic, and that applicability assessment constitutes a key performance bottleneck for the controller.

Existing evaluation metrics for visual object tracking lack a comparable, continuous-time measure for the trajectory function of time (FoT), relying instead on discrete-frame assessments that fail to characterize arbitrary-time states or disentangle distinct error types (e.g., localization, false positives, missed detections). Method: We propose Star-ID—the first spatiotemporally aligned trajectory integral distance—defining a rigorous, comparable FoT metric over continuous spacetime. Star-ID strictly distinguishes temporally aligned versus misaligned trajectory segments and analytically decouples detection and localization errors. It introduces time-averaged metrics and a theoretical error decomposition model, supported by a multi-object numerical validation framework. Contribution/Results: We provide formal theoretical analysis and demonstrate—via both single- and multi-object simulations—that Star-ID significantly enhances physical interpretability and fine-grained discriminative power in tracking evaluation, enabling precise, continuous-time performance assessment.

In offline reinforcement learning, conventional single-step transition sampling fails to improve policy performance and often introduces out-of-distribution actions, causing training instability. To address this, we propose Trajectory-level Replay (TR), the first framework to extend prioritized sampling to complete trajectories. TR introduces a reverse-trajectory sampling strategy and a trajectory-level priority metric grounded in both TD error and cumulative return, effectively avoiding out-of-distribution action selection. Furthermore, we incorporate a weighted critic objective to mitigate distributional shift inherent in trajectory-level sampling. Evaluated on the D4RL benchmark, TR consistently enhances state-of-the-art algorithms—including BCQ and CQL—achieving average normalized score improvements of 12%–28%. These results empirically validate the effectiveness and generalizability of trajectory-level data utilization as a novel paradigm for offline RL.