arXiv subjects

Hongyu Song

Publications and source records attributed to Hongyu Song.

5 recordsLinked to original sources

arXiv2025

ARCADE: Adaptive Robot Control with Online Changepoint-Aware Bayesian Dynamics Learning

Real-world robots must operate under evolving dynamics caused by changing operating conditions, external disturbances, and unmodeled effects. These may appear as gradual drifts, transient fluctuations, or abrupt shifts, demanding real-time adaptation that is robust to short-term variation yet responsive to lasting change. We propose a framework for modeling the nonlinear dynamics of robotic systems that can be updated in real time from streaming data. The method decouples representation learning from online adaptation, using latent representations learned offline to support online closed-form Bayesian updates. To handle evolving conditions, we introduce a changepoint-aware mechanism with a latent variable inferred from data likelihoods that indicates continuity or shift. When continuity is likely, evidence accumulates to refine predictions; when a shift is detected, past information is tempered to enable rapid re-learning. This maintains calibrated uncertainty and supports probabilistic reasoning about transient, gradual, or structural change. We prove that the adaptive regret of the framework grows only logarithmically in time and linearly with the number of shifts, competitive with an oracle that knows timings of shift. We validate on cartpole simulations and real quadrotor flights with swinging payloads and mid-flight drops, showing improved predictive accuracy, faster recovery, and more accurate closed-loop tracking than relevant baselines.

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arXiv2025

SoraNav: Adaptive UAV Task-Centric Navigation via Zeroshot VLM Reasoning

Autonomous navigation under natural language instructions represents a crucial step toward embodied intelligence, enabling complex task execution in environments ranging from industrial facilities to domestic spaces. However, language-driven 3D navigation for Unmanned Aerial Vehicles (UAVs) requires precise spatial reasoning, a capability inherently lacking in current zero-shot Vision-Language Models (VLMs) which often generate ambiguous outputs and cannot guarantee geometric feasibility. Furthermore, existing Vision-Language Navigation (VLN) methods are predominantly tailored for 2.5D ground robots, rendering them unable to generalize to the unconstrained 3D spatial reasoning required for aerial tasks in small-scale, cluttered environments. In this paper, we present SoraNav, a novel framework enabling zero-shot VLM reasoning for UAV task-centric navigation. To address the spatial-semantic gap, we introduce Multi-modal Visual Annotation (MVA), which encodes 3D geometric priors directly into the VLM's 2D visual input. To mitigate hallucinated or infeasible commands, we propose an Adaptive Decision Making (ADM) strategy that validates VLM proposals against exploration history, seamlessly switching to geometry-based exploration to avoid dead-ends and redundant revisits. Deployed on a custom PX4-based micro-UAV, SoraNav demonstrates robust real-world performance. Quantitative results show our approach significantly outperforms state-of-the-art baselines, increasing Success Rate (SR) by 25.7% and navigation efficiency (SPL) by 17.3% in 2.5D scenarios, and achieving improvements of 39.3% (SR) and 24.7% (SPL) in complex 3D scenarios.

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arXiv2025

Impedance and Stability Targeted Adaptation for Aerial Manipulator with Unknown Coupling Dynamics

Stable aerial manipulation during dynamic tasks such as object catching, perching, or contact with rigid surfaces necessarily requires compliant behavior, which is often achieved via impedance control. Successful manipulation depends on how effectively the impedance control can tackle the unavoidable coupling forces between the aerial vehicle and the manipulator. However, the existing impedance controllers for aerial manipulator either ignore these coupling forces (in partitioned system compliance methods) or require their precise knowledge (in complete system compliance methods). Unfortunately, such forces are very difficult to model, if at all possible. To solve this long-standing control challenge, we introduce an impedance controller for aerial manipulator which does not rely on a priori knowledge of the system dynamics and of the coupling forces. The impedance control design can address unknown coupling forces, along with system parametric uncertainties, via suitably designed adaptive laws. The closed-loop system stability is proved analytically and experimental results with a payload-catching scenario demonstrate significant improvements in overall stability and tracking over the state-of-the-art impedance controllers using either partitioned or complete system compliance.

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arXiv2025

Safe Distributed Learning-Enhanced Predictive Control for Multiple Quadrupedal Robots

Quadrupedal robots exhibit remarkable adaptability in unstructured environments, making them well-suited for formation control in real-world applications. However, keeping stable formations while ensuring collision-free navigation presents significant challenges due to dynamic obstacles, communication constraints, and the complexity of legged locomotion. This paper proposes a distributed model predictive control framework for multi-quadruped formation control, integrating Control Lyapunov Functions to ensure formation stability and Control Barrier Functions for decentralized safety enforcement. To address the challenge of dynamically changing team structures, we introduce Scale-Adaptive Permutation-Invariant Encoding (SAPIE), which enables robust feature encoding of neighboring robots while preserving permutation invariance. Additionally, we develop a low-latency Data Distribution Service-based communication protocol and an event-triggered deadlock resolution mechanism to enhance real-time coordination and prevent motion stagnation in constrained spaces. Our framework is validated through high-fidelity simulations in NVIDIA Omniverse Isaac Sim and real-world experiments using our custom quadrupedal robotic system, XG. Results demonstrate stable formation control, real-time feasibility, and effective collision avoidance, validating its potential for large-scale deployment.

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arXiv2022

Multi-UAV Coverage Planning with Limited Endurance in Disaster Environment

For scenes such as floods and earthquakes, the disaster area is large, and rescue time is tight. Multi-UAV exploration is more efficient than a single UAV. Existing UAV exploration work is modeled as a Coverage Path Planning (CPP) task to achieve full coverage of the area in the presence of obstacles. However, the endurance capability of UAV is limited, and the rescue time is urgent. Thus, even using multiple UAVs cannot achieve complete disaster area coverage in time. Therefore, in this paper we propose a multi-Agent Endurance-limited CPP (MAEl-CPP) problem based on a priori heatmap of the disaster area, which requires the exploration of more valuable areas under limited energy. Furthermore, we propose a path planning algorithm for the MAEl-CPP problem, by ranking the possible disaster areas according to their importance through satellite or remote aerial images and completing path planning according to the importance level. Experimental results show that our proposed algorithm is at least twice as effective as the existing method in terms of search efficiency.

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