Search Results for author:
Found 2 papers, 1 papers with code
Reward Conditioned Neural Movement Primitives for Population Based Variational Policy Optimization
For this, the experience of the robot, which can be bootstrapped from demonstrated trajectories, is used to train a novel Neural Processes-based deep network that samples from its latent space and generates the required trajectories given desired rewards.
ACNMP: Skill Transfer and Task Extrapolation through Learning from Demonstration and Reinforcement Learning via Representation Sharing
To equip robots with dexterous skills, an effective approach is to first transfer the desired skill via Learning from Demonstration (LfD), then let the robot improve it by self-exploration via Reinforcement Learning (RL).
Cannot find the paper you are looking for? You can
Submit a new open access paper.
