Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence Reinforcement Learning to Adjust Robot Movements to New Situations

Subfields: Reinforcement Learning, Robotics & Control

Citations in the corpus

• End-to-End Training of Deep Visuomotor Policies (2015, background): “They have been applied to a range of robotic tasks, including manipulation [2, 13] and locomotion [5, 7, 15, 39].”
• Reinforcement learning in robotics: A survey (2013, methodology): “, 2010a), and setting the meta-actions for dart throwing and table tennis hitting tasks (Kober et al., 2010).”
• Deep spatial autoencoders for visuomotor learning (2015, methodology): “Reinforcement learning and policy search methods have been applied to perform a variety of robotic tasks, such as table tennis [9], object manipulation [10], [11], and locomotion [12], [13], [14], [15].”
• Path integral guided policy search (2016, methodology): “Policy search methods have been used in robotics for a variety of tasks, such as manipulation [4, 9, 10], playing table tennis [11] and ball-in-a-cup [3] games, regrasping [12], and locomotion [1, 2, 13].”
• Learning contact-rich manipulation skills with guided policy search (2015, background): “Note that the number of samples required to learn a successful controller is in the range of 20-25, substantially lower than many previously proposed policy search methods in the literature [8], [11], [16], [3].”
• Deep predictive policy training using reinforcement learning (2017, background): “They have been successfully applied to a number of robotic skilled behavior learning tasks, such as robot tennis swings [25], ball-in-a-cup and underactuated swing-up [26], dart throwing and table tennis [27].”
• Learning robot in-hand manipulation with tactile features (2015, methodology): “To obtain a generalizing policy, cost-sensitive Gaussian Processes (GPs) are used, as introduced in the cost-regularized kernel regression (CrKR) algorithm [38].”
• TossingBot: Learning to Throw Arbitrary Objects With Residual Physics (2019, background): “More recently, learning-based systems for robotic throwing [2, 14, 18, 11] have also been proposed, which ignore low-level dynamics and directly optimize for task-level success signals (e.”
• Meta Reinforcement Learning with Latent Variable Gaussian Processes (2018, background): “Much of the literature on meta and transfer learning in RL has focused on multi-task learning, i.e., cases where the system/robot is the same, but the task changes [16, 29, 3, 5, 20, 21, 24, 8, 12].”
• Learning Dexterous Manipulation Policies from Experience and Imitation (2016, methodology): “Indeed RL has been successfully applied to a range of robotic tasks (Tedrake, Zhang and Seung 2004; Kober, Oztop and Peters 2010; Pastor, Hoffmann, Asfour and Schaal 2009; Deisenroth, Rasmussen and Fox 2011), however dimensionality and sample complexity have presented major challenges (Kober,…”
• Manipulators and Manipulation in high dimensional spaces (2016, background): “Indeed RL has been successfully applied to a range of robotic tasks [26, 43, 78, 101], however dimensionality and sample complexity have presented major challenges [25, 42].”
• Learning Visual Affordances for Robotic Manipulation (2019, background): “More recently, learning-based systems for robotic throwing [6, 45, 57, 30] have also been proposed, which ignore low-level dynamics and directly optimize for task-level success signals (e.”
• A Survey on Policy Search for Robotics (2013, methodology): “CRKR was also used to learn to return table tennis balls [38].”
• Learning compact parameterized skills with a single regression (2013, background): “In recent years, dynamical movement primitives (DMPs) [10] have become a common underlying skill representation for learning parameterized skills [5]–[9].”
• Variational Inference for Policy Search in changing situations (2011, methodology): “Note that CRKR and RWR have only been used to learn meta-parameters of the motion (Kober et al., 2010) (like the duration or the end-point of the motion).”
• Tensor Based Knowledge Transfer Across Skill Categories for Robot Control (2017, methodology): “Object Throwing (Throw) The learner aims to throw a ball into a basket by holding it with the end-effector and then releasing it during dynamic arm motion [Kober et al., 2010].”