A systematic review on reinforcement learning-based robotics within the last decade
… the authors discussed several learning algorithms and their applicability to bipedal walking
… Nine papers [79]–[81], [83], [85], [91], [92], [96], [97] conducted both simulation and real life …
… Nine papers [79]–[81], [83], [85], [91], [92], [96], [97] conducted both simulation and real life …
NeoRL: A near real-world benchmark for offline reinforcement learning
… The recent work [10] proposes a practical offline workflow for conservative offline RL algorithms
(eg, CQL [11]) on robotic tasks, by utilizing comparative metrics across checkpoints and …
(eg, CQL [11]) on robotic tasks, by utilizing comparative metrics across checkpoints and …
Learning mobile manipulation through deep reinforcement learning
… manipulation algorithm based on deep reinforcement learning. … It is worth noting that the
success rate of the real robot … the simulation and the real world on the robot dynamics, object …
success rate of the real robot … the simulation and the real world on the robot dynamics, object …
Reinforcement learning: An introduction
… necessary to make a robot perform efficiently in the real world suggest that many concurrent,
… The type of algorithm is indicated by the acronym REINFORCE, formed by taking letters …
… The type of algorithm is indicated by the acronym REINFORCE, formed by taking letters …
A survey of reinforcement learning algorithms for dynamically varying environments
S Padakandla - ACM Computing Surveys (CSUR), 2021 - dl.acm.org
… due to ever changing dynamics of data in real world. Each feature distribution and input-label …
in robotics arm manipulation exercises. In such a case, we expect the learning algorithm to …
in robotics arm manipulation exercises. In such a case, we expect the learning algorithm to …
Open-sourced reinforcement learning environments for surgical robotics
… algorithms with such an environment and contribute to solutions that would have real world
significance to robotic … -sourced reinforcement learning environment for surgical robotics and …
significance to robotic … -sourced reinforcement learning environment for surgical robotics and …
Reinforcement learning
MA Wiering, M Van Otterlo - Adaptation, learning, and optimization, 2012 - Springer
… Thus, in addition to the many algorithmic advances as described in the previous three parts
… slow training and testing on real robots, the reality gap between simulators and the real world…
… slow training and testing on real robots, the reality gap between simulators and the real world…
A survey on deep learning and deep reinforcement learning in robotics with a tutorial on deep reinforcement learning
… on machine learning and robotics. In Sect. 3, a brief tutorial on deep reinforcement learning
is … 3.3) is devoted to show how deep learning algorithms can be used, in conjunction with RL, …
is … 3.3) is devoted to show how deep learning algorithms can be used, in conjunction with RL, …
Deep reinforcement learning for industrial insertion tasks with visual inputs and natural rewards
… 1: We train policies directly in the real world to solve connector insertion tasks from raw pixel
… related reinforcement learning algorithms that lend themselves well to real-world learning …
… related reinforcement learning algorithms that lend themselves well to real-world learning …
Awac: Accelerating online reinforcement learning with offline datasets
… We evaluate our algorithm on a wide variety of robotic … separate real-world robots: drawer
opening with a 7-DoF robotic … on a variety of simulated and real world robotic problems. While …
opening with a 7-DoF robotic … on a variety of simulated and real world robotic problems. While …