5 accepted papers
This work addresses the challenge of personalizing trajectories generated in automated decision-making systems by introducing a resource-efficient approach that enables rapid adaptation to individual users' preferences. Our method leverages a pretrained conditional diffusion model with Preference La
Deploying a safe mobile robot policy in scenarios with human pedestrians is challenging due to their unpredictable movements. Current Reinforcement Learningbased motion planners rely on a single policy to simulate pedestrian movements and could suffer from the over-fitting issue. Alternatively, fram…
Offline Reinforcement Learning (RL) offers an attractive alternative to interactive data acquisition by leveraging pre-existing datasets. However, its effectiveness hinges on the quantity and quality of the data samples. This work explores the use of more readily available, albeit off-dynamics datas…
In reinforcement learning with image-based inputs, it is crucial to establish a robust and generalizable state representation. Recent advancements in metric learning, such as deep bisimulation metric approaches, have shown promising results in learning structured low-dimensional representation space…
For the distant speech recognition, the multi-channel processing has been proven to significantly improve the ASR performances compared to the single channel approaches. However, there is very little work has done to provide a comparative evaluation of the approaches, particularly with the modern De…