Zobrazeno 1 - 10
of 6 103
pro vyhledávání: '"FENG, Qian"'
Continual Learning (CL) aims to learn in non-stationary scenarios, progressively acquiring and maintaining knowledge from sequential tasks. Recent Prompt-based Continual Learning (PCL) has achieved remarkable performance with Pre-Trained Models (PTMs
Externí odkaz:
http://arxiv.org/abs/2409.18860
Autor:
Feng, Qian, Lema, David S. Martinez, Malmir, Mohammadhossein, Li, Hang, Feng, Jianxiang, Chen, Zhaopeng, Knoll, Alois
We introduce DexGanGrasp, a dexterous grasping synthesis method that generates and evaluates grasps with single view in real time. DexGanGrasp comprises a Conditional Generative Adversarial Networks (cGANs)-based DexGenerator to generate dexterous gr
Externí odkaz:
http://arxiv.org/abs/2407.17348
Synthesizing diverse dexterous grasps from uncertain partial observation is an important yet challenging task for physically intelligent embodiments. Previous works on generative grasp synthesis fell short of precisely capturing the complex grasp dis
Externí odkaz:
http://arxiv.org/abs/2407.15161
Autor:
Feng, Qian, Zhao, Hanbin, Zhang, Chao, Dong, Jiahua, Ding, Henghui, Jiang, Yu-Gang, Qian, Hui
Incremental Learning (IL) aims to learn deep models on sequential tasks continually, where each new task includes a batch of new classes and deep models have no access to task-ID information at the inference time. Recent vast pre-trained models (PTMs
Externí odkaz:
http://arxiv.org/abs/2407.03813
Learning from demonstrations faces challenges in generalizing beyond the training data and is fragile even to slight visual variations. To tackle this problem, we introduce Lan-o3dp, a language guided object centric diffusion policy that takes 3d rep
Externí odkaz:
http://arxiv.org/abs/2407.00451
Recently, Large Language Models (LLMs) have witnessed remarkable performance as zero-shot task planners for robotic manipulation tasks. However, the open-loop nature of previous works makes LLM-based planning error-prone and fragile. On the other han
Externí odkaz:
http://arxiv.org/abs/2406.00430
High Spectral-Efficiency, Ultra-low MIMO SDM Transmission over a Field-Deployed Multi-Core OAM Fiber
Autor:
Liu, Junyi, Xu, Zengquan, Mo, Shuqi, Huang, Yuming, Huang, Yining, Li, Zhenhua, Guo, Yuying, Shen, Lei, Xu, Shuo, Gao, Ran, Du, Cheng, Feng, Qian, Luo, Jie, Liu, Jie, Yu, Siyuan
Few-mode multi-core fiber (FM-MCF) based Space-Division Multiplexing (SDM) systems possess the potential to maximize the number of multiplexed spatial channels per fiber by harnessing both the space (fiber cores) and mode (optical mode per core) dime
Externí odkaz:
http://arxiv.org/abs/2407.01552
Deep neural network (DNN) typically involves convolutions, pooling, and activation function. Due to the growing concern about privacy, privacy-preserving DNN becomes a hot research topic. Generally, the convolution and pooling operations can be suppo
Externí odkaz:
http://arxiv.org/abs/2403.00239
We propose an SDP-based framework to address the stabilization of input delay systems while taking into account dissipative constraints. A key to our approach is the introduction of the concept of parameterized linear dynamical state feedbacks (LDSFs
Externí odkaz:
http://arxiv.org/abs/2311.14944