4 accepted papers
Diffusion models have been promising for trajectory generation tasks due to their ability to capture multimodal behaviors, but their closed-box nature and unconstrained denoising often undermine kinodynamic feasibility. We propose a novel Coupled-Layer Diffusion Model (CLDM) that explicitly embeds a
Fully fine-tuning large pre-trained models for each downstream task is impractical due to prohibitive memory, computation, and storage costs. Although parameter-efficient fine-tuning (PEFT) methods address this issue, leading methods like LoRA still exhibit linear scaling of trainable parameters wit
Indoor disaster relief and rescue missions require quadrotors to fully exploit their maneuverability in real-time. However, the computational complexity induced by the underactuated kinodynamics conflicts with the rapid replanning requirement. For agile trajectory planning in cluttered and unknown e
Large-scale protein language models (PLMs), such as the ESM family, have achieved remarkable performance in various downstream tasks related to protein structure and function by undergoing unsupervised training on residue sequences. They have become essential tools for researchers and practitioners…