Search Results - "Proceedings., IEEE International Conference on Robotics and Automation"

SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud by Wu, Bichen, Zhou, Xuanyu, Zhao, Sicheng, Yue, Xiangyu, Keutzer, Kurt

“…Earlier work demonstrates the promise of deep-learning-based approaches for point cloud segmentation; however, these approaches need to be improved to be…”
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Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks by Cui, Henggang, Radosavljevic, Vladan, Chou, Fang-Chieh, Lin, Tsung-Han, Nguyen, Thi, Huang, Tzu-Kuo, Schneider, Jeff, Djuric, Nemanja

“…Autonomous driving presents one of the largest problems that the robotics and artificial intelligence communities are facing at the moment, both in terms of…”
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Crowd-Robot Interaction: Crowd-Aware Robot Navigation With Attention-Based Deep Reinforcement Learning by Chen, Changan, Liu, Yuejiang, Kreiss, Sven, Alahi, Alexandre

“…Mobility in an effective and socially-compliant manner is an essential yet challenging task for robots operating in crowded spaces. Recent works have shown the…”
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Sim-to-Real Transfer of Robotic Control with Dynamics Randomization by Xue Bin Peng, Andrychowicz, Marcin, Zaremba, Wojciech, Abbeel, Pieter

“…Simulations are attractive environments for training agents as they provide an abundant source of data and alleviate certain safety concerns during the…”
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Mini Cheetah: A Platform for Pushing the Limits of Dynamic Quadruped Control by Katz, Benjamin, Carlo, Jared Di, Kim, Sangbae

“…Mini Cheetah is a small and inexpensive, yet powerful and mechanically robust quadruped robot, intended to enable rapid development of control systems for…”
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Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience by Chebotar, Yevgen, Handa, Ankur, Makoviychuk, Viktor, Macklin, Miles, Issac, Jan, Ratliff, Nathan, Fox, Dieter

“…We consider the problem of transferring policies to the real world by training on a distribution of simulated scenarios. Rather than manually tuning the…”
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End-to-End Driving Via Conditional Imitation Learning by Codevilla, Felipe, Muller, Matthias, Lopez, Antonio, Koltun, Vladlen, Dosovitskiy, Alexey

“…Deep networks trained on demonstrations of human driving have learned to follow roads and avoid obstacles. However, driving policies trained via imitation…”
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Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning by Nagabandi, Anusha, Kahn, Gregory, Fearing, Ronald S., Levine, Sergey

“…Model-free deep reinforcement learning algorithms have been shown to be capable of learning a wide range of robotic skills, but typically require a very large…”
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Overcoming Exploration in Reinforcement Learning with Demonstrations by Nair, Ashvin, McGrew, Bob, Andrychowicz, Marcin, Zaremba, Wojciech, Abbeel, Pieter

“…Exploration in environments with sparse rewards has been a persistent problem in reinforcement learning (RL). Many tasks are natural to specify with a sparse…”
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Target-driven visual navigation in indoor scenes using deep reinforcement learning by Yuke Zhu, Mottaghi, Roozbeh, Kolve, Eric, Lim, Joseph J., Gupta, Abhinav, Fei-Fei, Li, Farhadi, Ali

“…Two less addressed issues of deep reinforcement learning are (1) lack of generalization capability to new goals, and (2) data inefficiency, i.e., the model…”
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MVX-Net: Multimodal VoxelNet for 3D Object Detection by Sindagi, Vishwanath A., Zhou, Yin, Tuzel, Oncel

“…Many recent works on 3D object detection have focused on designing neural network architectures that can consume point cloud data. While these approaches…”
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Tightly Coupled 3D Lidar Inertial Odometry and Mapping by Ye, Haoyang, Chen, Yuying, Liu, Ming

“…Ego-motion estimation is a fundamental requirement for most mobile robotic applications. By sensor fusion, we can compensate the deficiencies of stand-alone…”
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Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates by Shixiang Gu, Holly, Ethan, Lillicrap, Timothy, Levine, Sergey

“…Reinforcement learning holds the promise of enabling autonomous robots to learn large repertoires of behavioral skills with minimal human intervention…”
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SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud by Wu, Bichen, Wan, Alvin, Yue, Xiangyu, Keutzer, Kurt

“…We address semantic segmentation of road-objects from 3D LiDAR point clouds. In particular, we wish to detect and categorize instances of interest, such as…”
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ProgPrompt: Generating Situated Robot Task Plans using Large Language Models by Singh, Ishika, Blukis, Valts, Mousavian, Arsalan, Goyal, Ankit, Xu, Danfei, Tremblay, Jonathan, Fox, Dieter, Thomason, Jesse, Garg, Animesh

“…Task planning can require defining myriad domain knowledge about the world in which a robot needs to act. To ameliorate that effort, large language models…”
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Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks by Lee, Michelle A., Zhu, Yuke, Srinivasan, Krishnan, Shah, Parth, Savarese, Silvio, Fei-Fei, Li, Garg, Animesh, Bohg, Jeannette

“…Contact-rich manipulation tasks in unstructured environments often require both haptic and visual feedback. However, it is non-trivial to manually design a…”
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Learning to Drive in a Day by Kendall, Alex, Hawke, Jeffrey, Janz, David, Mazur, Przemyslaw, Reda, Daniele, Allen, John-Mark, Lam, Vinh-Dieu, Bewley, Alex, Shah, Amar

“…We demonstrate the first application of deep reinforcement learning to autonomous driving. From randomly initialised parameters, our model is able to learn a…”
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Time-Contrastive Networks: Self-Supervised Learning from Video by Sermanet, Pierre, Lynch, Corey, Chebotar, Yevgen, Hsu, Jasmine, Jang, Eric, Schaal, Stefan, Levine, Sergey, Brain, Google

“…We propose a self-supervised approach for learning representations and robotic behaviors entirely from unlabeled videos recorded from multiple viewpoints, and…”
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PointNetGPD: Detecting Grasp Configurations from Point Sets by Liang, Hongzhuo, Ma, Xiaojian, Li, Shuang, Gorner, Michael, Tang, Song, Fang, Bin, Sun, Fuchun, Zhang, Jianwei

“…In this paper, we propose an end-to-end grasp evaluation model to address the challenging problem of localizing robot grasp configurations directly from the…”
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Intent-Aware Pedestrian Prediction for Adaptive Crowd Navigation by Katyal, Kapil D., Hager, Gregory D., Huang, Chien-Ming

“…Mobile robots capable of navigating seamlessly and safely in pedestrian rich environments promise to bring robotic assistance closer to our daily lives. In…”
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