Results 11 to 20 of about 539,131 (366)
End-to-End Driving Via Conditional Imitation Learning [PDF]
IEEE International Conference on Robotics and Automation, 2018 Deep networks trained on demonstrations of human driving have learned to follow roads and avoid obstacles. However, driving policies trained via imitation learning cannot be controlled at test time.
Felipe Codevilla +4 more
openalex +3 more sources
TransFuser: Imitation With Transformer-Based Sensor Fusion for Autonomous Driving [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 How should we integrate representations from complementary sensors for autonomous driving? Geometry-based fusion has shown promise for perception (e.g., object detection, motion forecasting).
Kashyap Chitta +5 more
semanticscholar +1 more source
A Survey of Imitation Learning: Algorithms, Recent Developments, and Challenges [PDF]
IEEE Transactions on Cybernetics, 2023 In recent years, the development of robotics and artificial intelligence (AI) systems has been nothing short of remarkable. As these systems continue to evolve, they are being utilized in increasingly complex and unstructured environments, such as ...
Maryam Zare +3 more
semanticscholar +1 more source
Goal-Conditioned Imitation Learning using Score-based Diffusion Policies [PDF]
Robotics: Science and Systems, 2023 We propose a new policy representation based on score-based diffusion models (SDMs). We apply our new policy representation in the domain of Goal-Conditioned Imitation Learning (GCIL) to learn general-purpose goal-specified policies from large uncurated ...
Moritz Reuss +3 more
semanticscholar +1 more source
MimicPlay: Long-Horizon Imitation Learning by Watching Human Play [PDF]
Conference on Robot Learning, 2023 Imitation learning from human demonstrations is a promising paradigm for teaching robots manipulation skills in the real world. However, learning complex long-horizon tasks often requires an unattainable amount of demonstrations.
Chen Wang +7 more
semanticscholar +1 more source
Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism [PDF]
IEEE Transactions on Information Theory, 2021 Offline reinforcement learning (RL) algorithms seek to learn an optimal policy from a fixed dataset without active data collection. Based on the composition of the offline dataset, two main methods are used: imitation learning which is suitable for ...
Paria Rashidinejad +4 more
semanticscholar +1 more source
Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning [PDF]
IEEE International Conference on Computer Vision, 2023 The past few years have witnessed the immense success of object detection, while current excellent detectors struggle on tackling size-limited instances.
Xiang Yuan +4 more
semanticscholar +1 more source
Teach a Robot to FISH: Versatile Imitation from One Minute of Demonstrations [PDF]
Robotics: Science and Systems, 2023 While imitation learning provides us with an efficient toolkit to train robots, learning skills that are robust to environment variations remains a significant challenge.
Siddhant Haldar +3 more
semanticscholar +1 more source
Human-to-Robot Imitation in the Wild [PDF]
Robotics: Science and Systems, 2022 We approach the problem of learning by watching humans in the wild. While traditional approaches in Imitation and Reinforcement Learning are promising for learning in the real world, they are either sample inefficient or are constrained to lab settings ...
Shikhar Bahl, Abhi Gupta, Deepak Pathak
semanticscholar +1 more source
Model-Based Imitation Learning for Urban Driving [PDF]
Neural Information Processing Systems, 2022 An accurate model of the environment and the dynamic agents acting in it offers great potential for improving motion planning. We present MILE: a Model-based Imitation LEarning approach to jointly learn a model of the world and a policy for autonomous ...
Anthony Hu +8 more
semanticscholar +1 more source