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Modular Neural Network Policies for Learning In-Flight Object Catching with a Robot Hand-Arm System
We present a modular framework designed to enable a robot hand-arm system to learn how to catch flying objects, a task that requires fast, reactive, and accurately-timed robot motions.
Intrinsic Language-Guided Exploration for Complex Long-Horizon Robotic Manipulation Tasks
We evaluate our framework and related intrinsic learning methods in an environment challenged with exploration, and a complex robotic manipulation task challenged by both exploration and long-horizons.
RObotic MAnipulation Network (ROMAN) $\unicode{x2013}$ Hybrid Hierarchical Learning for Solving Complex Sequential Tasks
In this work, we present a Hybrid Hierarchical Learning framework, the Robotic Manipulation Network (ROMAN), to address the challenge of solving multiple complex tasks over long time horizons in robotic manipulation.
Identifying Important Sensory Feedback for Learning Locomotion Skills
Robot motor skills can be learned through deep reinforcement learning (DRL) by neural networks as state-action mappings.
