6 accepted papers
Dexterous manipulation has advanced rapidly, with policies now capable of performing complex, contact-rich tasks in simulation. However, transferring these policies from simulation to real world remains a significant challenge. A key obstacle is the mismatch in low-level controller dynamics, where s…
Multimodal fusion object detection faces a substantial modality gap at the same backbone stage. This makes predefined stage-aligned fusion insufficient for cross-stage interactions. We propose MFH-NAS, a hybrid neural architecture search framework that automatically discovers fusion architectures to…
Robotic grasping, crucial for robot interaction with objects, still struggles with counter-intuitive or long-tailed scenarios like uncommon materials and shapes. Humans, however, intuitively adjust grasps with their physics-informed interpretations of the object, using visual and linguistic cues. Th
Recent advancements have exploited diffusion models for the synthesis of either LiDAR point clouds or camera image data in driving scenarios. Despite their success in modeling single-modality data marginal distribution, there is an under- exploration in the mutual reliance between different modaliti…
We focus on the task of language-conditioned grasping in clutter, in which a robot is supposed to grasp the target object based on a language instruction. Previous works separately conduct visual grounding to localize the target object, and generate a grasp for that object. However, these works requ…
Self-assessment rules play an essential role in safe and effective real-world robotic applications, which verify the feasibility of the selected action before actual execution. But how to utilize the self-assessment results to re-choose actions remains a challenge. Previous methods eliminate the sel…