6 accepted papers
AI-based code generation is increasingly prevalent, with GitHub Copilot estimated to generate 46% of the code on GitHub. Accurately evaluating how well generated code aligns with developer intent remains a critical challenge. Traditional evaluation methods, such as unit tests, are often unscalable a
Object manipulation in cluttered scenes is a difficult and important problem in robotics. To efficiently manipulate objects, it is crucial to understand their surroundings, especially in cases where multiple objects are stacked one on top of the other, preventing effective grasping. We here present…
We address the problem of devising the means for a robot to rapidly and safely learn insertion skills with just a few human interventions and without hand-crafted rewards or demonstrations. Our InsertionNet version 2.0 provides an improved technique to robustly cope with a wide range of use-cases fe…
We present an approach for multi-robot consistent distributed localization and semantic mapping in an unknown environment, considering scenarios with classification ambiguity, where objects' visual appearance generally varies with viewpoint. Our approach addresses such a setting by maintaining a dis
We present an approach for localization and semantic mapping in ambiguous scenarios by incrementally maintaining a hybrid belief over continuous states and discrete classification and data association variables. Unlike existing incremental approaches, we explicitly maintain data association componen…
State of the art Bayesian classification approaches typically maintain a posterior distribution over possible classes given available sensor observations (images). Yet, while these approaches fuse all classifier outputs thus far, they do not provide any indication regarding how reliable the posterio