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Found 6 papers, 1 papers with code
Cracking the Code of Juxtaposition: Can AI Models Understand the Humorous Contradictions
Through extensive experimentation and analysis of recent commercial or open-sourced large (vision) language models, we assess their capability to comprehend the complex interplay of the narrative humor inherent in these comics.
iSLAM: Imperative SLAM
Simultaneous Localization and Mapping (SLAM) stands as one of the critical challenges in robot navigation.
Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios
To our knowledge, this is the first application of a combined imitation and reinforcement learning approach in autonomous driving that utilizes large amounts of real-world human driving data.
Hierarchical Model-Based Imitation Learning for Planning in Autonomous Driving
We demonstrate the first large-scale application of model-based generative adversarial imitation learning (MGAIL) to the task of dense urban self-driving.
ADAIL: Adaptive Adversarial Imitation Learning
We present the ADaptive Adversarial Imitation Learning (ADAIL) algorithm for learning adaptive policies that can be transferred between environments of varying dynamics, by imitating a small number of demonstrations collected from a single source domain.
Adaptive Adversarial Imitation Learning
We present the ADaptive Adversarial Imitation Learning (ADAIL) algorithm for learning adaptive policies that can be transferred between environments of varying dynamics, by imitating a small number of demonstrations collected from a single source domain.
