Rongtao Xu
Publications
\textsc{NaVIDA}: Vision-Language Navigation with Inverse Dynamics Augmentation
Vision-and-Language Navigation (VLN) requires agents to interpret natural language instructions and act coherently in visually rich environments. However, most existing methods rely on reactive state-action mappings without explicitly modeling how actions causally transform subsequent visual observations. Lacking such vision-action causality, agents cannot anticipate the visual changes induced by its own actions, leading to unstable behaviors, weak generalization, and cumulative error along trajectory. To address these issues, we introduce \textsc{NaVIDA} (\textbf{Nav}igation with \textbf{I}nverse \textbf{D}ynamics \textbf{A}ugmentation), a unified VLN framework that couples policy learning with action-grounded visual dynamics and adaptive execution. \textsc{NaVIDA} augments training with chunk-based inverse-dynamics supervision to learn causal relationship between visual changes and corresponding actions. To structure this supervision and extend the effective planning range, \textsc{NaVIDA} employs hierarchical probabilistic action chunking (HPAC), which organizes trajectories into multi-step chunks and provides discriminative, longer-range visual-change cues. To further curb error accumulation and stabilize behavior at inference, an entropy-guided mechanism adaptively sets the execution horizon of action chunks. Extensive experiments show that \textsc{NaVIDA} achieves superior navigation performance compared to state-of-the-art methods with fewer parameters (3B vs. 8B). Real-world robot evaluations further validate the practical feasibility and effectiveness of our approach. Code and data will be available upon acceptance.
$\mathcal{P}^3$: Toward Versatile Embodied Agents
Embodied agents have demonstrated promising capabilities in interacting with physical environments. Yet, versatile embodied agents face three core bottlenecks: dynamic environmental perception, open tool access, and complex multi-task planning. Prior methods depend entirely on tool feedback to track scene changes and task progress, leading to poor real-time adaptability, error accumulation, and limited tool compatibility; multi-task scheduling is also understudied due to the difficulty of handling task dependencies and conflicting priorities. To address these limitations, we propose $\mathcal P^3$, a unified framework integrating real-time perception and dynamic scheduling, which perceives task-relevant information actively from the environment, plugs and utilizes tools without feedback requirements, and plans multi-task execution by prioritizing urgent tasks and dynamically adjusting task order based on dependencies. We additionally build the Active Task Perception (ATP) benchmark to quantitatively measure VLMs' capacity for active scene understanding and task proposal. Evaluations on the ATP benchmark verify that multiple VLMs can detect and propose active tasks, and comprehensive real-world robot experiments prove our method bridges the gap between benchmarks and practical deployment, yielding transferable general-purpose embodied agents. Code and data are available at https://github.com/fz-zsl/P3.