Discovering Motor Programs by Recomposing Demonstrations

By Tanmay Shankar, Shubham Tulsiani, Lerrel Pinto, Abhinav Kumar Gupta. 2020

57 citations recorded by the source corpus.

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Citations in the corpus

Accelerating Reinforcement Learning with Learned Skill Priors (2020, methodology): “1 but without skill prior, representative of [22, 16, 29].”
Parrot: Data-Driven Behavioral Priors for Reinforcement Learning (2020, background): “…et al., 2019), and methods that learn temporally extended actions, also known as options , from demonstrations, and then recompose them to perform long-horizon tasks through RL or planning (Fox et al., 2017; Krishnan et al., 2017; Kipf et al., 2019; Shankar et al., 2020; Shankar & Gupta, 2020).”
Continuous Control with Action Quantization from Demonstrations (2021, background): “, 2020), or learned from interactions with the environment (Manschitz et al., 2015; Kipf et al., 2019; Shankar et al., 2019), or hand specified (Pastor et al.”
Learning Robot Skills with Temporal Variational Inference (2020, methodology): “…addresses composing skills, including sequencing skills (Neumann et al., 2014; Peters et al., 2013), hierarchical approaches (Lioutikov et al., 2017; Xu et al., 2018; Huang et al., 2019; Konidaris et al., 2012; Konidaris & Barto, 2009; Shankar et al., 2020), options (Sutton et al., 1999), etc.”
MT-Opt: Continuous Multi-Task Robotic Reinforcement Learning at Scale (2021, background): “Learning complex and composite skills has been addressed through hierarchical reinforcement learning with options [75, 7, 14], combining multiple sub-tasks [6, 16, 49, 26, 69, 89], reusing samples between tasks [39], relabeling experience in hindsight [3], introducing demonstrations [58, 81, 29, 41,”