Fast biped walking with a reflexive controller and real-time policy searching

By Tao Geng, Bernd Porr, Florentin Wörgötter. 2005

33 citations recorded by the source corpus.

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In Reinforcement Learning

Citations in the corpus

Trust Region Policy Optimization (2015, neutral): “This is in contrast with most prior methods for learning locomotion, which typically rely on hand-architected policy classes that explicitly encode notions of balance and stepping (Tedrake et al., 2004; Geng et al., 2006; Wampler & Popović, 2009).”
High-Dimensional Continuous Control Using Generalized Advantage Estimation (2015, neutral): “This is in contrast with most prior methods for learning locomotion controllers, which typically rely on hand-architected policy classes that explicitly encode notions of balance and stepping [15, 4, 18].”
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): “Neural oscillators with sensor feedback have been used to learn rhythmic movements where open and closedloop information were combined, such as gaits for a two legged robot (Geng et al., 2006; Endo et al., 2008).”
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].”
Learning contact-rich manipulation skills with guided policy search (2015, methodology): “Previous policy search methods in robotics generally use one or more of the following techniques to achieve good performance and reasonable training times: the use of example demonstrations [15], [9], [16], the use of a prior controller that allows the policy to only set high level targets, such as ”
Learning deep control policies for autonomous aerial vehicles with MPC-guided policy search (2015, methodology): “RL has been used to train robotic controllers for games such as ball-in-cup and table tennis [16], manipulation [31], [6], and robotic locomotion [17], [40], [10], [8].”
Optimizing Simulations with Noise-Tolerant Structured Exploration (2018, background): “However, despite many success stories [15], [18], [11], their brittleness in the face of noise and, until recently [17], lack of theoretical basis for optimizing non-smooth objectives has remained a concern.”
Learning Visual Feature Spaces for Robotic Manipulation with Deep Spatial Autoencoders (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].”