Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
Deep reinforcement learning for robotics: A survey of real-world successes
Reinforcement learning (RL), particularly its combination with deep neural networks,
referred to as deep RL (DRL), has shown tremendous promise across a wide range of …
referred to as deep RL (DRL), has shown tremendous promise across a wide range of …
How to train your robot with deep reinforcement learning: lessons we have learned
Deep reinforcement learning (RL) has emerged as a promising approach for autonomously
acquiring complex behaviors from low-level sensor observations. Although a large portion of …
acquiring complex behaviors from low-level sensor observations. Although a large portion of …
Driving into the future: Multiview visual forecasting and planning with world model for autonomous driving
In autonomous driving predicting future events in advance and evaluating the foreseeable
risks empowers autonomous vehicles to plan their actions enhancing safety and efficiency …
risks empowers autonomous vehicles to plan their actions enhancing safety and efficiency …
Bridgedata v2: A dataset for robot learning at scale
We introduce BridgeData V2, a large and diverse dataset of robotic manipulation behaviors
designed to facilitate research in scalable robot learning. BridgeData V2 contains 53,896 …
designed to facilitate research in scalable robot learning. BridgeData V2 contains 53,896 …
Droid: A large-scale in-the-wild robot manipulation dataset
A Khazatsky, K Pertsch, S Nair, A Balakrishna… - ar** stone on the path toward more capable and robust robotic manipulation policies …
Daydreamer: World models for physical robot learning
To solve tasks in complex environments, robots need to learn from experience. Deep
reinforcement learning is a common approach to robot learning but requires a large amount …
reinforcement learning is a common approach to robot learning but requires a large amount …
Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration0
Large, high-capacity models trained on diverse datasets have shown remarkable successes
on efficiently tackling downstream applications. In domains from NLP to Computer Vision …
on efficiently tackling downstream applications. In domains from NLP to Computer Vision …
Zero-shot robotic manipulation with pretrained image-editing diffusion models
If generalist robots are to operate in truly unstructured environments, they need to be able to
recognize and reason about novel objects and scenarios. Such objects and scenarios might …
recognize and reason about novel objects and scenarios. Such objects and scenarios might …
Prompting decision transformer for few-shot policy generalization
Human can leverage prior experience and learn novel tasks from a handful of
demonstrations. In contrast to offline meta-reinforcement learning, which aims to achieve …
demonstrations. In contrast to offline meta-reinforcement learning, which aims to achieve …
Masked world models for visual control
Visual model-based reinforcement learning (RL) has the potential to enable sample-efficient
robot learning from visual observations. Yet the current approaches typically train a single …
robot learning from visual observations. Yet the current approaches typically train a single …