33 citations · 47 across the 4 of their papers we have counts for
5 papers
SafeAPT: Safe Simulation-to-Real Robot Learning using Diverse Policies Learned in Simulation
Rituraj Kaushik, Karol Arndt, Ville Kyrki
The framework of Simulation-to-real learning, i.e, learning policies in simulation and transferring those policies to the real world is one of the most promising approaches towards…
Fast Online Adaptation in Robotics through Meta-Learning Embeddings of Simulated Priors
Rituraj Kaushik, Timothée Anne, Jean-Baptiste Mouret
Meta-learning algorithms can accelerate the model-based reinforcement learning (MBRL) algorithms by finding an initial set of parameters for the dynamical model such that the model…
Adaptive Prior Selection for Repertoire-based Online Adaptation in Robotics
Rituraj Kaushik, Pierre Desreumaux, Jean-Baptiste Mouret
Repertoire-based learning is a data-efficient adaptation approach based on a two-step process in which (1) a large and diverse set of policies is learned in simulation, and (2) a p…
Multi-objective Model-based Policy Search for Data-efficient Learning with Sparse Rewards
Rituraj Kaushik, Konstantinos Chatzilygeroudis, Jean-Baptiste Mouret
The most data-efficient algorithms for reinforcement learning in robotics are model-based policy search algorithms, which alternate between learning a dynamical model of the robot…
Black-Box Data-efficient Policy Search for Robotics
Konstantinos Chatzilygeroudis, Roberto Rama, Rituraj Kaushik +3
The most data-efficient algorithms for reinforcement learning (RL) in robotics are based on uncertain dynamical models: after each episode, they first learn a dynamical model of th…