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Found 6 papers, 1 papers with code
RL-RRT: Kinodynamic Motion Planning via Learning Reachability Estimators from RL Policies
Through the combination of sampling-based planning, a Rapidly Exploring Randomized Tree (RRT) and an efficient kinodynamic motion planner through machine learning, we propose an efficient solution to long-range planning for kinodynamic motion planning.
Long Range Neural Navigation Policies for the Real World
Learned Neural Network based policies have shown promising results for robot navigation.
Long-Range Indoor Navigation with PRM-RL
Long-range indoor navigation requires guiding robots with noisy sensors and controls through cluttered environments along paths that span a variety of buildings.
Learning Navigation Behaviors End-to-End with AutoRL
The policies are trained in small, static environments with AutoRL, an evolutionary automation layer around Reinforcement Learning (RL) that searches for a deep RL reward and neural network architecture with large-scale hyper-parameter optimization.
Visual Representations for Semantic Target Driven Navigation
We propose to using high level semantic and contextual features including segmentation and detection masks obtained by off-the-shelf state-of-the-art vision as observations and use deep network to learn the navigation policy.
PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-based Planning
The RL agents learn short-range, point-to-point navigation policies that capture robot dynamics and task constraints without knowledge of the large-scale topology.
