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新论文 · v1arXiv:2611.11555

Concurrent Learning-Based Inertial Parameter Estimation and Control of Quadrotors

Sandesh Thapa, He Bai

原文摘要

In this letter, we consider trajectory tracking control and inertial parameter estimation of a quadrotor UAV whose center of mass (CoM) does not coincide with its geometric center. Onboard payloads like an IMU, a camera, or a battery are not necessarily placed at its geometric center. Moreover, during the flight these sensors might move because of external disturbances and cause a CoM offset. Thus, these events and equipment introduce an offset and cause the inertia matrix to be non-diagonal. We develop an integral concurrent learning (ICL)-based geometric controller that estimates the mass, the CoM offset, and the full inertia matrix online, without an accelerometer or numerical derivatives, and tracks a given trajectory corrected by the offset estimated online. We prove that the estimated parameters converge to their true values under a finite excitation condition. We validate the effectiveness of the proposed algorithm using a simulation example.

eess.SY · cs.RO

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首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.475ACL Anthology · ACL 2026

From Past To Path: Masked History Learning for Next-Item Prediction in Generative Recommendation

Kaiwen Wei, Kejun he, Xiaomian Kang Institute of automation, Chinese academy of science, Jie Zhang, Ymyang, Li Jin, Zhenyang Li Hong Kong University of Science and Technology, Jiang Zhong Chongqing University 等 10 位作者

原文摘要

Generative recommendation, which directly generates item identifiers, has emerged as a promising paradigm for recommendation systems. However, this left-to-right paradigm inherently biases the model towards local contexts, failing to capture deeper historical dependencies necessary for understanding complex user intents.To address this limitation, we propose Masked History Learning (MHL), a novel training framework that shifts the objective from simple next-step prediction to deep comprehension of history. MHL augments the standard autoregressive objective with an auxiliary task of reconstructing masked historical items, compelling the model to understand "why” an item path is formed from the user’s past behaviors, rather than just "what” item comes next.We introduce two key contributions to enhance this framework: (1) an entropy-guided masking policy that intelligently targets the most informative historical items for reconstruction, and (2) a curriculum learning scheduler that progressively transitions from history reconstruction to future prediction.Experiments on three public datasets show that our method significantly outperforms state-of-the-art generative models, highlighting that a comprehensive understanding of the past is crucial for accurately predicting a user’s future path. The code is available at https://github.com/CQU-MM-Intelligent-Lab/MHL.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.855ACL Anthology · ACL 2026

ToolPRM: Fine-Grained Inference Scaling of Structured Outputs for Function Calling

Jianghao Lin Shanghai Jiaotong University, Yuanyuan Shi Shanghai Jiaotong University, Xin Peng, Renjie Ding LongShine, Hairui Wang, Yuxuan Peng longshine, Bizhe Bai, Weixi Song Westlake University, Zhejiang University and Shanghai Innovation Institute 等 13 位作者

原文摘要

Large language models (LLMs) excel at function calling, but inference scaling has been explored mainly for unstructured generation. We propose an inference-scaling framework for structured outputs that combines fine-grained beam search with ToolPRM, a process reward model scoring each intra-call decision (function name and argument filling). We build the first fine-grained intra-call supervision dataset via function masking, rollout collection, and step-level annotation. ToolPRM outperforms outcome and coarse-grained reward models in predictive accuracy and yields consistent test-time gains on multiple function-calling benchmarks. We further show that structured generation follows “explore more but retain less”, since early JSON errors are unrecoverable.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表