3 accepted papers
We aim to solve the problem of learning user-intended granular skills from multi-granularity demonstrations. Traditional learning-from-demonstration methods typically rely on extensive fine-grained data, interpolation techniques, or dynamics models, which are ineffective at encoding or decoding the …
We aim to solve the problem of spatially localizing composite instructions referring to space: space grounding. Compared to current instance grounding, space grounding is challenging due to the ill-posedness of identifying locations referred to by discrete expressions and the compositional ambiguity…
Despite the prevalence of robotic manipulation tasks in various real-world applications of different requirements and needs, there has been a lack of focus on enhancing the adaptability of robotic grasping systems. Most of the current literature constructs models around a single gripper, succumbing