arXiv subjects

Jianzheng Huang

Publications and source records attributed to Jianzheng Huang.

4 recordsLinked to original sources

arXiv2026

$\mathcal{P}^3$: 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.

cs.RO↗

arXiv2026

A0: An Affordance-Aware Hierarchical Model for General Robotic Manipulation

Robotic manipulation faces critical challenges in understanding spatial affordances--the "where" and "how" of object interactions--essential for complex manipulation tasks like wiping a board or stacking objects. Existing methods, including modular-based and end-to-end approaches, often lack robust spatial reasoning capabilities. Unlike recent point-based and flow-based affordance methods that focus on dense spatial representations or trajectory modeling, we propose A0, a hierarchical affordance-aware diffusion model that decomposes manipulation tasks into high-level spatial affordance understanding and low-level action execution. A0 leverages the Embodiment-Agnostic Affordance Representation, which captures object-centric spatial affordances by predicting contact points and post-contact trajectories. A0 is pre-trained on 1 million contact points data and fine-tuned on annotated trajectories, enabling generalization across platforms. Key components include Position Offset Attention for motion-aware feature extraction and a Spatial Information Aggregation Layer for precise coordinate mapping. The model's output is executed by the action execution module. Experiments on multiple robotic systems (Franka, Kinova, Realman, and Dobot) demonstrate A0's superior performance in complex tasks, showcasing its efficiency, flexibility, and real-world applicability.

cs.RO↗

arXiv2025

PinPoint3D: Fine-Grained 3D Part Segmentation from a Few Clicks

Fine-grained 3D part segmentation is crucial for enabling embodied AI systems to perform complex manipulation tasks, such as interacting with specific functional components of an object. However, existing interactive segmentation methods are largely confined to coarse, instance-level targets, while non-interactive approaches struggle with sparse, real-world scans and suffer from a severe lack of annotated data. To address these limitations, we introduce PinPoint3D, a novel interactive framework for fine-grained, multi-granularity 3D segmentation, capable of generating precise part-level masks from only a few user point clicks. A key component of our work is a new 3D data synthesis pipeline that we developed to create a large-scale, scene-level dataset with dense part annotations, overcoming a critical bottleneck that has hindered progress in this field. Through comprehensive experiments and user studies, we demonstrate that our method significantly outperforms existing approaches, achieving an average IoU of around 55.8% on each object part under first-click settings and surpassing 71.3% IoU with only a few additional clicks. Compared to current state-of-the-art baselines, PinPoint3D yields up to a 16% improvement in IoU and precision, highlighting its effectiveness on challenging, sparse point clouds with high efficiency. Our work represents a significant step towards more nuanced and precise machine perception and interaction in complex 3D environments.

cs.CV↗

arXiv2025

GIFT: Generated Indoor video frames for Texture-less point tracking

Point tracking is becoming a powerful solver for motion estimation and video editing. Compared to classical feature matching, point tracking methods have the key advantage of robustly tracking points under complex camera motion trajectories and over extended periods. However, despite certain improvements in methodologies, current point tracking methods still struggle to track any position in video frames, especially in areas that are texture-less or weakly textured. In this work, we first introduce metrics for evaluating the texture intensity of a 3D object. Using these metrics, we classify the 3D models in ShapeNet into three levels of texture intensity and create GIFT, a challenging synthetic benchmark comprising 1800 indoor video sequences with rich annotations. Unlike existing datasets that assign ground truth points arbitrarily, GIFT precisely anchors ground truth on classified target objects, ensuring that each video corresponds to a specific texture intensity level. Furthermore, we comprehensively evaluate current methods on GIFT to assess their performance across different texture intensity levels and analyze the impact of texture on point tracking.

cs.CV↗