6 accepted papers
While current embodied policies exhibit remarkable manipulation skills, their execution remains unsatisfactorily slow as they inherit the tardy pacing of human demonstrations. Existing acceleration methods typically require policy retraining or costly online interactions, limiting their scalability …
Vision-Language-Action (VLA) models show strong generalization for robotic control, but finetuning them with reinforcement learning (RL) is constrained by the high cost and safety risks of real-world interaction. Training VLA models in interactive world models avoids these issues but introduces seve…
To address the challenges posed by lighting variations and fruit occlusion in open orchard environments, which significantly affect the performance of apple-harvesting robots, this study proposes an apple detection method based on the fusion of infrared thermal images and visible-light images. First
Automatic Video Dubbing (AVD) aims to synthesize speech that matches a character’s speaking style and emotion in silent video clips. However, existing approaches rely on attention mechanisms to learn cross-modal prosodic alignment implicitly, making it challenging to capture subtle prosodic variatio…
Multi-objective reinforcement learning (MORL) approaches address real-world problems with multiple objectives by learning policies maximizing returns weighted by different user preferences. Typical methods assume the objectives remain unchanged throughout the agent's lifetime. However, in some real-…
In the Text-to-speech(TTS) task, the latent diffusion model has excellent fidelity and generalization, but its expensive resource consumption and slow inference speed have always been a challenging. To address this issue, this paper proposes the Discrete Diffusion Model with Contrastive Learning for…