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Reinforcement Learning in Robotic Motion Planning by Combined Experience-based Planning and Self-Imitation Learning
High-quality and representative data is essential for both Imitation Learning (IL)- and Reinforcement Learning (RL)-based motion planning tasks.
Simultaneous Multi-View Object Recognition and Grasping in Open-Ended Domains
We demonstrate the ability of our approach to grasp never-seen-before objects and to rapidly learn new object categories using very few examples on-site in both simulation and real-world settings.
Self-Imitation Learning by Planning
Imitation learning (IL) enables robots to acquire skills quickly by transferring expert knowledge, which is widely adopted in reinforcement learning (RL) to initialize exploration.
Accelerating Reinforcement Learning for Reaching using Continuous Curriculum Learning
Reinforcement learning has shown great promise in the training of robot behavior due to the sequential decision making characteristics.
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