Results 301 to 310 of about 5,721,941 (348)

Imitation Learning Enabled Task Scheduling for Online Vehicular Edge Computing

IEEE Transactions on Mobile Computing, 2022
Vehicular edge computing (VEC) is a promising paradigm based on the Internet of vehicles to provide computing resources for end users and relieve heavy traffic burden for cellular networks. In this paper, we consider a VEC network with dynamic topologies,
Xiaojie Wang, Zhaolong Ning, Song Guo, Lei Wang   +3 more
semanticscholar   +1 more source

Invariant Causal Imitation Learning for Generalizable Policies

Neural Information Processing Systems, 2023
Consider learning an imitation policy on the basis of demonstrated behavior from multiple environments, with an eye towards deployment in an unseen environment.
Ioana Bica
semanticscholar   +1 more source

On a Connection Between Imitation Learning and RLHF

International Conference on Learning Representations
This work studies the alignment of large language models with preference data from an imitation learning perspective. We establish a close theoretical connection between reinforcement learning from human feedback RLHF and imitation learning (IL ...
Teng Xiao, Yige Yuan, Mingxiao Li, Zhengyu Chen, V.G. Honavar   +4 more
semanticscholar   +1 more source

Federated Imitation Learning for UAV Swarm Coordination in Urban Traffic Monitoring

IEEE Transactions on Industrial Informatics, 2023
The popularization of unmanned aerial vehicles (UAVs) has boosted various civil applications such as traffic monitoring, in which the effective coordination of the UAV swarm plays a significant role in expanding the monitoring range and enhancing the ...
Bo Yang, Huaguang Shi, Xiaofang Xia
semanticscholar   +1 more source

Prompt, Plan, Perform: LLM-based Humanoid Control via Quantized Imitation Learning

IEEE International Conference on Robotics and Automation, 2023
In recent years, reinforcement learning and imitation learning have shown great potential for controlling humanoid robots’ motion. However, these methods typically create simulation environments and rewards for specific tasks, resulting in the ...
Jingkai Sun, Qiang Zhang, Yiqun Duan, Xiaoyang Jiang, Chong Cheng, Renjing Xu   +5 more
semanticscholar   +1 more source

Data Scaling Laws in Imitation Learning for Robotic Manipulation

International Conference on Learning Representations
Data scaling has revolutionized fields like natural language processing and computer vision, providing models with remarkable generalization capabilities.
Fanqi Lin, Yingdong Hu, Pingyue Sheng, Chuan Wen, Jiacheng You, Yang Gao   +5 more
semanticscholar   +1 more source

LocoMuJoCo: A Comprehensive Imitation Learning Benchmark for Locomotion

arXiv.org, 2023
Imitation Learning (IL) holds great promise for enabling agile locomotion in embodied agents. However, many existing locomotion benchmarks primarily focus on simplified toy tasks, often failing to capture the complexity of real-world scenarios and ...
Firas Al-Hafez, Guoping Zhao, Jan Peters, Davide Tateo   +3 more
semanticscholar   +1 more source

Bunny-VisionPro: Real-Time Bimanual Dexterous Teleoperation for Imitation Learning

IEEE/RJS International Conference on Intelligent RObots and Systems
Teleoperation is a crucial tool for collecting human demonstrations, but controlling robots with bimanual dexterous hands remains a challenge. Existing teleoperation systems struggle to handle the complexity of coordinating two hands for intricate ...
Runyu Ding, Yuzhe Qin, Jiyue Zhu, Chengzhe Jia, Shiqi Yang, Ruihan Yang, Xiaojuan Qi, Xiaolong Wang   +7 more
semanticscholar   +1 more source

Personalized Lane Change Planning and Control By Imitation Learning From Drivers

IEEE transactions on industrial electronics (1982. Print), 2023
In this article, we propose a novel personalized planning and control approach for lane change assistance system which can efficiently learn a model prediction control (MPC)-based driver-specific lane-changing policy via end-to-end imitation learning ...
Hanqing Tian, Chao Wei, Chaoyang Jiang, Zirui Li, Jibin Hu   +4 more
semanticscholar   +1 more source

EgoMimic: Scaling Imitation Learning via Egocentric Video

IEEE International Conference on Robotics and Automation
The scale and diversity of demonstration data required for imitation learning is a significant challenge. We present EgoMimic, a full-stack framework which scales manipulation via human embodiment data, specifically egocentric human videos paired with 3D
Simar Kareer, Dhruv Patel, Ryan Punamiya, Pranay Mathur, Shuo Cheng, Chen Wang, Judy Hoffman, Danfei Xu   +7 more
semanticscholar   +1 more source