Geometric Adaptive Control
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Geometric Adaptive Control for a Quadrotor UAV with Wind Disturbance Rejection
Announce Type: cross Abstract: This paper presents a geometric adaptive control scheme for a quadrotor unmanned aerial vehicle, where the effects of unknown, unstructured disturbances are mitigated by a multilayer neural network that is adjusted online. The stability of the proposed controller is analyzed with Lyapunov stability theory on the special Euclidean group, and it is shown that the tracking errors are uniformly ultimately bounded with an ultimate bound that can be abridged...
Geometric Adaptive Control with Neural Networks for a Quadrotor UAV in Wind fields
arXiv:1903.02091v1 Announce Type: cross Abstract: This paper proposes a geometric adaptive controller for a quadrotor unmanned aerial vehicle with artificial neural networks. It is assumed that the dynamics of a quadrotor is disturbed by arbitrary, unstructured forces and moments caused by wind.
Repurposing Adversarial Perturbations for Continual Learning: From Defense to Active Alignment
Announce Type: new Abstract: In dynamic environments, large language models need to keep adapting to new tasks, but continual learning often suffers from forgetting, limited transfer, and vulnerability to adversarial perturbations. To address this, we present AdvCL, which repurposes adversarial perturbations as a geometric control signal for stable continual adaptation.
Geometric scaling limits of phase-only control in multimode coherent systems
arXiv:2606.04288v1 Announce Type: new Abstract: Control of coherent waves is often restricted to phase-only actuation in multimode systems, yet the resulting physical limits remain poorly understood. Here, we show that restricting control to relative phases confines dynamics to a compact manifold whose geometry produces isolated stationary interference basins with robustness governed by local curvature. Imperfections act as smooth perturbations that soften basin structure without eliminating...
Alpha-RTL: Test-Time Training for RTL Hardware Optimization
arXiv:2606.05253v1 Announce Type: new Abstract: Large language models (LLMs) have shown increasing promise in generating functionally correct register-transfer-level (RTL) hardware designs. Recent systems improve further through EDA-integrated reinforcement learning with syntax, simulation, and PPA rewards, but train a general RTL generator before deployment while test-time approaches search with a frozen policy. We instead perform reinforcement learning at test time, allowing the LLM policy...
Direct 3D-Aware Object Insertion via Decomposed Visual Proxies
Announce Type: new Abstract: Object insertion aims to seamlessly composite a reference object into a specified region of a background image. Recent diffusion-based methods achieve high visual quality but formulate insertion as a simple 2D inpainting task, providing no explicit control over the object's 3D pose and limiting their practical applicability. We propose DIRECT (Decomposed Injection for Reference Composition and Target-integration), a novel framework that integrates interactive...
Technical note on Sequential Test-Time Adaptation via Martingale-Driven Fisher Prompting
Announce Type: replace Abstract: We present a theoretical framework for M-FISHER, a method for sequential distribution shift detection and stable adaptation in streaming data. For detection, we construct an exponential martingale from non-conformity scores and apply Ville's inequality to obtain time-uniform guarantees on false alarm control, ensuring statistical validity at any stopping time. Under sustained shifts, we further bound the expected detection delay as...
Implicit Drifting Policy: One-Step Action Generation via Conditional Expert Geometry
Announce Type: new Abstract: Generative action policies based on diffusion or flow matching excel in behavior cloning, yet their iterative sampling is prohibitive for high-frequency robot control. While recent one-step formulations alleviate this latency, they inevitably discard the intermediate trajectory evolution that provides crucial action correction. Directly recovering this mechanism by explicitly estimating a training-time drifting field is mathematically ill-posed due to extreme...
Representation Collapse in Sequential Post-Training of Large Language Models
Announce Type: new Abstract: Large language models are now adapted through chains of post-training stages rather than through a single instruction-tuning pass. This paper studies whether such sequential post-training gradually compresses internal representations into low-rank, anisotropic, and homogeneous feature spaces. We define a measurement suite for hidden states, logits, token trajectories, and LoRA updates, and we use it to analyze supervised fine-tuning, preference optimization,...
TASE: Truncation-Aware Semantic Embeddings for 3D Scene Understanding and Editing
arXiv:2606.03314v1 Announce Type: new Abstract: High-fidelity semantic 3D scene representations are crucial for numerous applications, including robotics, autonomous driving, and simulation. Beyond this, the ability to edit such representations enables developers to adapt these applications more easily to specific target scenarios. Current approaches provide limited support for controllable editing.