5 accepted papers
In robotic operation, asymmetric tasks requiring dual-arm cooperation are the highly challenging research direction. Autonomous operation generally has a low success rate or poor generalization because of its excessive dependence on the accuracy of sub-goals from asymmetric tasks. Although teleopera
Multimodal large language models (MLLMs) have shown remarkable performance across various visual understanding tasks. However, most existing MLLMs still lack image detail perception, limiting their effectiveness in tasks that require detailed visual information. In this paper, we introduce Percept-D
Vision-and-Language Navigation (VLN) has garnered widespread attention and research interest due to its potential applications in real-world scenarios. Despite significant progress in the VLN field in recent years, limitations persist. Many agents struggle to make accurate decisions when faced with
Grasping a target object in the cluttered environment is challenging due to potential collisions. Taking pre-grasp manipulations such as pushing, sliding and poking is an effective way to singulate the target. However, the success rate is heavily affected by the dimension disaster derived from compl
Adversarial Inverse Reinforcement Learning (AIRL) has gained popularity as an alternative to supervised imitation learning, addressing the distributional bias issue of the latter. However, it still faces significant challenges in long-horizon tasks due to the lack of effective exploration. In our le