4 accepted papers
We introduce Consistent Instance Field, a continuous and probabilistic spatio-temporal representation for dynamic scene understanding.Unlike prior methods that rely on discrete tracking or view-dependent features, our approach disentangles visibility from persistent object identity by modeling each
Real-time execution is essential for cyber-physical systems such as robots. These systems operate in dynamic real-world environments where even small delays can undermine responsiveness and compromise performance. Asynchronous inference has recently emerged as a system-level paradigm for real-time r…
This paper enhances model-based offline inverse reinforcement learning (IRL) by refining conservative Markov decision process (MDP) frameworks, traditionally employing uncertainty penalties to deter exploitation in uncertain areas. Existing methods, dependent on neural network ensembles to model MDP…
Route planning for navigation under partial observability plays a crucial role in modern robotics and autonomous driving. Existing route planning approaches can be categorized into two main classes: traditional autoregressive and diffusion-based methods. The former often fails due to its myopic natu…