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pro vyhledávání: '"Valada, Abhinav"'
Efficient learning from demonstration for long-horizon tasks remains an open challenge in robotics. While significant effort has been directed toward learning trajectories, a recent resurgence of object-centric approaches has demonstrated improved sa
Externí odkaz:
http://arxiv.org/abs/2411.03408
Autor:
Kurenkov, Michael, Marvi, Sajad, Schmidt, Julian, Rist, Christoph B., Canevaro, Alessandro, Yu, Hang, Jordan, Julian, Schildbach, Georg, Valada, Abhinav
The increasing interest in autonomous driving systems has highlighted the need for an in-depth analysis of human driving behavior in diverse scenarios. Analyzing human data is crucial for developing autonomous systems that replicate safe driving prac
Externí odkaz:
http://arxiv.org/abs/2411.01909
Autor:
Irshad, Muhammad Zubair, Comi, Mauro, Lin, Yen-Chen, Heppert, Nick, Valada, Abhinav, Ambrus, Rares, Kira, Zsolt, Tremblay, Jonathan
Neural Fields have emerged as a transformative approach for 3D scene representation in computer vision and robotics, enabling accurate inference of geometry, 3D semantics, and dynamics from posed 2D data. Leveraging differentiable rendering, Neural F
Externí odkaz:
http://arxiv.org/abs/2410.20220
Multi-sensor fusion is crucial for accurate 3D object detection in autonomous driving, with cameras and LiDAR being the most commonly used sensors. However, existing methods perform sensor fusion in a single view by projecting features from both moda
Externí odkaz:
http://arxiv.org/abs/2410.07475
Autor:
Distelzweig, Aron, Look, Andreas, Kosman, Eitan, Janjoš, Faris, Wagner, Jörg, Valada, Abhinav
In autonomous driving, accurate motion prediction is essential for safe and efficient motion planning. To ensure safety, planners must rely on reliable uncertainty information about the predicted future behavior of surrounding agents, yet this aspect
Externí odkaz:
http://arxiv.org/abs/2410.01628
Demonstration data plays a key role in learning complex behaviors and training robotic foundation models. While effective control interfaces exist for static manipulators, data collection remains cumbersome and time intensive for mobile manipulators
Externí odkaz:
http://arxiv.org/abs/2409.15095
Forecasting the semantics and 3D structure of scenes is essential for robots to navigate and plan actions safely. Recent methods have explored semantic and panoptic scene forecasting; however, they do not consider the geometry of the scene. In this w
Externí odkaz:
http://arxiv.org/abs/2409.12008
Autor:
Distelzweig, Aron, Kosman, Eitan, Look, Andreas, Janjoš, Faris, Manivannan, Denesh K., Valada, Abhinav
Forecasting the future trajectories of surrounding agents is crucial for autonomous vehicles to ensure safe, efficient, and comfortable route planning. While model ensembling has improved prediction accuracy in various fields, its application in traj
Externí odkaz:
http://arxiv.org/abs/2409.10585
Learning from expert demonstrations is a promising approach for training robotic manipulation policies from limited data. However, imitation learning algorithms require a number of design choices ranging from the input modality, training objective, a
Externí odkaz:
http://arxiv.org/abs/2409.07343
Autor:
Prasanna, Sai, Honerkamp, Daniel, Sirohi, Kshitij, Welschehold, Tim, Burgard, Wolfram, Valada, Abhinav
Publikováno v:
Proceedings of the International Symposium on Robotics Research (ISRR), 2024
Embodied AI has made significant progress acting in unexplored environments. However, tasks such as object search have largely focused on efficient policy learning. In this work, we identify several gaps in current search methods: They largely focus
Externí odkaz:
http://arxiv.org/abs/2408.02297