Sergey Levine目前是UC Berkeley电气工程与计算机科学系的副教授，同时是RAIL(Robotic AI&Learning Lab@BAI...
collective robotic learning:从多个机器人的学习过程中提取经验来加速机器人的学习过程 在深度强化学习中,并行学习也常常用于仿真中,加速学习过程 在以往的并行学习中,其主要思路是减少总共的训练时间,基于仿真时间非常便宜以及训练主要是神经网络的计算的假设,而本文主要是减少在真实机器人上训练的时间,基于在真实机器人...
Deep Reinforcement Learning for Robotic Manipulation. ICML, 2016. 2S. Gu, E. Holly, T. Lillicrap, and S. Levine. Deep Reinforcement Learning for Robotic Manipulation. ICML, 2016.Shixiang Gu, Ethan Holly, Timothy P. Lillicrap, et al. "Deep Reinforcement Learning for Robotic Manipula- tion"...
目标很简单,就是让机器人学习去抓一个绿色的棒子,机器人的运动过程就是参考sergey levine IJRR中提出的那个运动过程(Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection) 奖励就是机器人抓到则给个10000,反之就-1000 首先,这是一个稀疏reward问题。 (我们这先...
《Deep Reinforcement Learning for Robotic Manipulation》S Gu, E Holly, T Lillicrap, S Levine [Google Brain & Google DeepMind] (2016) http://t.cn/RVhtLTw ref:《How Robots Can Acquire New Skills from...
由于成功的generalization通常需要训练大量的objects和scenes [33] [24],需要多个视角和control,因此on-policy不适用于多样化的grasping scenarios,而Off-policy reinforcement learning methods不错～ Aim:to understand which off-policy RL algorithms are best suited for vision-based robotic grasping. ...
The focus of this work is to enumerate the various approaches and algorithms that center around application of reinforcement learning in robotic ma- ]]nipulation tasks. Earlier methods utilized specialized policy representations and human demonstrations to constrict the policy. Such methods worked well ...
Bi-Manual Block Assembly via Sim-to-Real Reinforcement Learning Most successes in robotic manipulation have been restricted to single-arm gripper robots, whose low dexterity limits the range of solvable tasks to pick-an... S Kataoka,Y Chung,SKS Ghasemipour,... - 《Arxiv》 被引量: 0发表: ...
On use in complex robotics settings. The algorithm does not scale naively to settings where huge amounts of exploration are difficult to obtain. For instance, in robotic settings one might have a single (or few) robots, interacting with the world in real time. This prohibits naive applications...
On use in complex robotics settings. The algorithm does not scale naively to settings where huge amounts of exploration are difficult to obtain. For instance, in robotic settings one might have a single (or few) robots, interacting with the world in real time. This prohibits naive applications...