27
May
Motion Planning Deep Learning. Typical TAMP problems are formalized by combining reasoning on a symbolic discrete level eg. State-of-the-art motion planners cannot scale to a large number of systems. Recent works present deep reinforcement learning as a framework to model the complex interactions and cooperation. A wide range of techniques in Machine Learning itself have been developed and this article de-scribes one of these fields Deep Reinforcement Learning DRL.
However while instinctive to humans socially compliant navigation is still difficult. For example training separate networks for object detection motion prediction path planning etc. Multi-Agent Motion Planning using Deep Learning for Space Applications. Students will be challenged to work on real-world robotics problems and develop deeper knowledge by reflecting on and formally evaluating their results. This approach is used by Lyft Tesla etc. An RL based complex motion planning for an industrial robot is presented in 11.
When applied to grasp-optimized motion planning the results suggest that deep learning can reduce the computation time by two orders of magnitude 300 from 29 s.
We aim to transform how college students learn robotics by offering a motion planning curriculum that enhances deep learning and is supported by OMPLapp an integrated software environment. This piqued my curiosity and I started reading. First-order logic with continuous motion planning such as nonlinear trajectory optimization. State-of-the-art motion planners cannot scale to a large number of systems. The paper provides insight into the hierarchical motion planning. Ive been teaching myself machine learning for the past few years and had read about DeepMinds impressive work DQN when it first came out.