4 accepted papers
Reward function design remains a fundamental challenge in reinforcement learning of humanoid robots, where handcrafted rewards often fail to capture human-like behavior, limit generalization, and require costly manual tuning. Addressing this bottleneck is essential for enabling scalable, adaptive, i
Vision-Language Models (VLMs) perform well on commonsense reasoning tasks but struggle with visual spatial reasoning. Most existing solutions introduce extra 3D priors or external spatial encoders, which increase complexity and degrade the underlying VLMs' general-purpose capabilities after spatial …
Collision-free motion planning has a well-established research history, but the majority of studies have been centered around Euclidean space and conducted offline. The primary challenge in online motion planning lies in circumventing local minima, which become more pronounced in configuration space
Collision-free motion planning has a well-established research history, yet the majority of studies have been centered around Cartesian space and assume the exact positions and shapes of the obstacles in the environment are known. In this letter, we introduce a perceptual local reactive motion contr