12 accepted papers
Recent approaches for few-shot 3D point cloud semantic segmentation typically require a two-stage learning process, i.e., a pre-training stage followed by a few-shot training stage. While effective, these methods face overreliance on pre-training, which hinders model flexibility and adaptability. So
Unsupervised domain adaptation (UDA) is a machine learning approach designed to minimize reliance on labeled data by aligning features between a labeled source domain and an unlabeled target domain, thereby reducing feature discrepancies, which is efficient for multivariate time series (MTS) predict…
Impedance control can be achieved within a model predictive control (MPC) framework for optimization and constraint compliance. However, user-defined or optimization-derived impedance models can be too conservative to achieve a timely convergence, or too aggressive to ensure safety. To address this,
As one of the most fundamental control modes in robotics, force/torque (F/T) control plays an essential role in a wide range of applications. However, classical F/T control fails to offer effective means to regulate the convergence sequence of the controlled states, which is beneficial in many real-
Recent 6D pose estimation methods demonstrate notable performance but still face some practical limitations. For instance, many of them rely heavily on sensor depth, which may fail with challenging surface conditions, such as transparent or highly reflective materials. In the meantime, RGB-based sol…
This paper presents an effective few-shot point cloud semantic segmentation approach for real-world applications. Existing few-shot segmentation methods on point cloud heavily rely on the fully-supervised pretrain with large annotated datasets, which causes the learned feature extraction bias to tho…
Incremental few-shot learning is highly expected for practical robotics applications. On one hand, robot is desired to learn new tasks quickly and flexibly using only few annotated training samples; on the other hand, such new additional tasks should be learned in a continuous and incremental manner…
We aim to tackle the challenging Few-Shot Object Detection (FSOD) where data-scarce categories are presented during the model learning. The failure modes of FSOD are investigated that the performance degradation is mainly due to the classification incapability (false positives), which motivates us t…
Dynamic control for robotic automation tasks is traditionally designed and optimized with a model-based approach, and the performance relies heavily upon accurate system modeling. However, modeling the true dynamics of increasingly complex robotic systems is an extremely challenging task and it ofte…
Robotic systems are increasingly required not only to generate precise motions to complete their tasks but also to handle the interactions with the environment or human. Significantly, soft interaction brings great challenges on the force control due to the nonlinear, viscoelastic and inhomogeneous…
Drift and scale ambiguity are two main issues which reduce localization accuracy in monocular visual odometry (MVO). It is necessary to propose a unified model to represent these measurement uncertainties. In this paper, we present a geometric map-assisted localization approach for mobile robots equ
In this paper, we introduce a novel control scheme based on the social force model for robots navigating in human environments. Social proxemics potential field is constructed based on the theory of proxemics and used to generate social interaction force for design of robot motion control. A combine