11 accepted papers
Articulated continuum robots (ACRs) are characterized by flexibility, controllability, and adaptability and perform excellently in complex and constrained environments. However, the large number of motor drives limit the ACRs' portability and make them cumbersome to control. This paper presents a no
In this work, we focus on addressing the long-horizon packing tasks in densely cluttered scenes. Such tasks require policies to effectively manage severe occlusions among objects and continually produce precise actions based on visual observations. We propose a vision-based Hierarchical policy for C
Recent advancements in robot learning leverage large language models (LLMs) and sampling-based task and motion planning (TAMP) modules for automatic and scalable robot data generation. This method yields both success trajectories and a large number of failure trajectories. Prior works typically filt…
Soft robotics, renowned for its robust adaptability to complex environments, holds great promise for a wide array of applications. Among the various actuation methods, magnetic fields stand out as particularly advantageous for soft robotics due to their ability to enable remote control and their bio
The true implementation of the Anticipatory Steam Detection for Steamer-Filling(ASDSF) process in baijiu intelligent distillation systems, which involves predicting and precisely spreading distillers’ grains before steam emerges, remains a critical unresolved challenge. In this study, we introduce t
Data-Free Knowledge Distillation (DFKD) is a promising task to train high-performance small models to enhance actual deployment without relying on the original training data. Existing methods commonly avoid relying on private data by utilizing synthetic or sampled data. However a long-overlooked iss…
Adversarial Robustness Distillation (ARD) is a promising task to solve the issue of limited adversarial robustness of small capacity models while optimizing the expensive computational costs of Adversarial Training (AT). Despite the good robust performance, the existing ARD methods are still impract…
Adversarial Robustness Distillation (ARD) is a novel method to boost the robustness of small models. Unlike general adversarial training, its robust knowledge transfer can be less easily restricted by the model capacity. However, the teacher model that provides the robustness of knowledge does not a…
Data-free knowledge distillation is a challenging model lightweight task for scenarios in which the original dataset is not available. Previous methods require a lot of extra computational costs to update one or more generators and their naive imitate-learning lead to lower distillation efficiency.…
Nowadays, driver drowsiness and driver distraction is considered as a major risk for fatal road accidents around the world. As a result, driver monitoring identifying is emerging as an essential function of automotive safety systems. Its basic features include head pose, gaze direction, yawning and…
Vehicle re-identification (re-id) is challenging due to the small inter-class distance. The differences between similar vehicles can be extremely subtle and only captured at particular scales and semantic levels. In this paper, we propose a novel Multi-Scale Deep Feature Fusion Network (MSDeep) to c…