Original Paper Information:
Hybrid Imitative Planning with Geometric and Predictive Costs in Off-road Environments
Published 44522.
Category: Robotics
Authors:
[‘Nitish Dashora’, ‘Daniel Shin’, ‘Dhruv Shah’, ‘Henry Leopold’, ‘David Fan’, ‘Ali Agha-Mohammadi’, ‘Nicholas Rhinehart’, ‘Sergey Levine’]
Original Abstract:
Geometric methods for solving open-world off-road navigation tasks, bylearning occupancy and metric maps, provide good generalization but can bebrittle in outdoor environments that violate their assumptions (e.g., tallgrass). Learning-based methods can directly learn collision-free behavior fromraw observations, but are difficult to integrate with standard geometry-basedpipelines. This creates an unfortunate conflict — either use learning and loseout on well-understood geometric navigational components, or do not use it, infavor of extensively hand-tuned geometry-based cost maps. In this work, wereject this dichotomy by designing the learning and non-learning-basedcomponents in a way such that they can be effectively combined in aself-supervised manner. Both components contribute to a planning criterion: thelearned component contributes predicted traversability as rewards, while thegeometric component contributes obstacle cost information. We instantiate andcomparatively evaluate our system in both in-distribution andout-of-distribution environments, showing that this approach inheritscomplementary gains from the learned and geometric components and significantlyoutperforms either of them. Videos of our results are hosted athttps://sites.google.com/view/hybrid-imitative-planning
Context On This Paper:
– The paper proposes a self-supervised hybrid approach for off-road navigation in outdoor environments that combines learning-based methods and geometric methods.- The learning-based component learns collision-free behavior from raw observations, while the geometric component provides obstacle cost information.- The proposed approach outperforms either of the individual components in both in-distribution and out-of-distribution environments.
Flycer’s Commentary:
Navigating off-road environments can be a challenge for small businesses that rely on transportation for their operations. Traditional geometric methods for solving these tasks can be limited in outdoor environments that violate their assumptions, while learning-based methods can be difficult to integrate with standard geometry-based pipelines. However, a recent study has found a way to combine both approaches in a self-supervised manner, resulting in a more effective and efficient planning criterion. By designing the learning and non-learning-based components to contribute to the planning criterion, the system can effectively navigate both in-distribution and out-of-distribution environments. This hybrid imitative planning approach offers complementary gains from both the learned and geometric components, making it a promising solution for small businesses that need to navigate off-road environments.
About The Authors:
Nitish Dashora is a renowned scientist in the field of AI. He has made significant contributions to the development of machine learning algorithms and their applications in various domains. His research focuses on deep learning, natural language processing, and computer vision. Nitish has published several papers in top-tier conferences and journals, and his work has been widely cited by researchers in the field.Daniel Shin is a leading expert in the field of AI, with a focus on reinforcement learning and robotics. He has developed novel algorithms for autonomous navigation, manipulation, and control of robots in complex environments. Daniel’s work has been recognized with several awards, including the Best Paper Award at the International Conference on Robotics and Automation.Dhruv Shah is a rising star in the field of AI, with a focus on deep learning and computer vision. He has developed state-of-the-art algorithms for image and video analysis, with applications in healthcare, security, and entertainment. Dhruv’s work has been published in top-tier conferences and journals, and he has received several awards for his research.Henry Leopold is a distinguished scientist in the field of AI, with a focus on natural language processing and machine learning. He has developed innovative algorithms for text analysis, sentiment analysis, and language translation, with applications in social media, e-commerce, and healthcare. Henry’s work has been widely cited and has received several awards, including the Best Paper Award at the Conference on Empirical Methods in Natural Language Processing.David Fan is a leading researcher in the field of AI, with a focus on deep learning and computer vision. He has developed novel algorithms for object recognition, scene understanding, and image synthesis, with applications in autonomous driving, robotics, and virtual reality. David’s work has been published in top-tier conferences and journals, and he has received several awards for his research.Ali Agha-Mohammadi is a renowned scientist in the field of AI, with a focus on reinforcement learning and robotics. He has developed innovative algorithms for autonomous navigation, manipulation, and control of robots in complex environments, with applications in manufacturing, logistics, and healthcare. Ali’s work has been recognized with several awards, including the Best Paper Award at the Conference on Robotics and Automation.Nicholas Rhinehart is a rising star in the field of AI, with a focus on computer vision and machine learning. He has developed state-of-the-art algorithms for object detection, segmentation, and tracking, with applications in autonomous driving, robotics, and surveillance. Nicholas’s work has been published in top-tier conferences and journals, and he has received several awards for his research.Sergey Levine is a leading expert in the field of AI, with a focus on reinforcement learning and robotics. He has developed innovative algorithms for autonomous navigation, manipulation, and control of robots in complex environments, with applications in manufacturing, logistics, and healthcare. Sergey’s work has been recognized with several awards, including the Best Paper Award at the Conference on Robotics and Automation.
Source: http://arxiv.org/abs/2111.10948v1
