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Chao Ji

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4 papers

2 author rows

AAAI Conference 2026 Conference Paper

Bridging Scale Discrepancies in Robotic Control via Language-Based Action Representations

Yuchi Zhang
Churui Sun
Shiqi Liang
Diyuan Liu
Chao Ji
Weinan Zhang
Ting Liu

Recent end-to-end robotic manipulation research increasingly adopts architectures inspired by large language models to enable robust manipulation. However, a critical challenge arises from severe distribution shifts between robotic action data, primarily due to substantial numerical variations in action commands across diverse robotic platforms and tasks, hindering the effective transfer of pretrained knowledge. To address this limitation, we propose a semantically grounded linguistic representation to normalize actions for efficient pretraining. Unlike conventional discretized action representations that are sensitive to numerical scales, the motion representation specifically disregards numeric scale effects, emphasizing directionality instead. This abstraction mitigates distribution shifts, yielding a more generalizable pretraining representation. Moreover, using the motion representation narrows the feature distance between action tokens and standard vocabulary tokens, mitigating modality gaps. Multi-task experiments on two benchmarks demonstrate that the proposed method significantly improves generalization performance and transferability in robotic manipulation tasks.

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IROS Conference 2025 Conference Paper

High-Precision Transformer-Based Visual Servoing for Humanoid Robots in Aligning Tiny Objects

Jialong Xue
Wei Gao
Yu Wang
Chao Ji
Dongdong Zhao
Shi Yan
Shiwu Zhang

High-precision tiny object alignment remains a common and critical challenge for humanoid robots in real world. To address this problem, this paper proposes a vision-based framework for precisely estimating and controlling the relative position between a handheld tool and a target object for humanoid robots, e. g. , a screwdriver tip and a screw head slot. By fusing images from the head and torso cameras on a robot with its head joint angles, the proposed Transformer-based visual servoing method can correct the handheld tool’s positional errors effectively, especially at a close distance. Experiments on M4-M8 screws demonstrate an average convergence error of 0. 8-1. 3 mm and a success rate of 93%-100%. Through comparative analysis, the results validate that this capability of high-precision tiny object alignment is enabled by the Distance Estimation Transformer architecture and the Multi-Perception-Head mechanism proposed in this paper.

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IROS Conference 2024 Conference Paper

Torque Ripple Reduction in Quasi-Direct Drive Motors Through Angle-Based Repetitive Learning Observer and Model Predictive Torque Controller

Hefei Zhang
Xiaohu Zhang
Jinyu Cheng
Jiangtao Hu
Chao Ji
Yu Wang 0038
Yutong Jiang
Zhen Han

Torque ripple reduction in quasi-direct drive (QDD) motors is crucial in their robotic applications for dynamic locomotion and dexterous manipulation. In this paper, we present a novel approach for reducing torque ripples of QDD motors, which integrates an angle-based repetitive learning observer (ARLO) and a model predictive control-based field-oriented controller (MPC-FOC). The proposed method successfully improves the torque loop control bandwidth and surpasses conventional proportional-integral (PI) controllers owing to the integrated physical constraints inside MPC. Additionally, the ARLO portion is able to mitigate ripple caused by the inherent cogging torque in brushless motors and also the periodic friction torque from the planetary gearboxes in QDD systems. The effectiveness of the proposed method is demonstrated through both simulation of a single QDD motor and experiments on a two-degree-of-freedom robotic leg, where the performance improvement can be 72. 7% in speed tracking and 58. 5% in trajectory tracking. The proposed method shows great potential in facilitating smooth motion and precise force control in future robotic applications.

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IROS Conference 2024 Conference Paper

TriLoc-NetVLAD: Enhancing Long-term Place Recognition in Orchards with a Novel LiDAR-Based Approach

Na Sun
Zhengqiang Fan
Quan Qiu
Tao Li 0015
Qingchun Feng
Chao Ji
Chunjiang Zhao 0001

Accurate long-term place recognition is crucial for agricultural robots operating in unstructured environments. However, in the challenging scene of orchard with high-frequency repetitive features, traditional LiDAR-based localization methods relying on geometric features prove to be inadequate. To address this challenge, we propose TriLoc-NetVLAD, a novel LiDAR-based long-term place recognition approach designed to handle the repetitive and ambiguous features of orchards. This approach initially fuses the point cloud density, height and spatial information to encode unordered 3D point clouds into a spatial context descriptor. then channel selection strategy based on descriptor’s sublayer similarity between query and its corresponding positive and negative samples is proposed to amplify the differences in environmental features. Finally, we use a Triplet Network to extract local features, encompassing both high-dimensional and low-dimensional information. These local features are then cascaded through NetVLAD layer to form a global descriptor. Furthermore, we have built a cross-seasonal orchard dataset to evaluate the performance of our place recognition method. The experiment results demonstrate the advantageous localization performances of the proposed place recognition algorithm over the existing methods.

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