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Learning Deployable Navigation Policies at Kilometer Scale from a Single Traversal
Model-free reinforcement learning has recently been shown to be effectiv...
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Zero-shot generalization using cascaded system-representations
This paper proposes a new framework named CASNET to learn control polici...
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Long Range Neural Navigation Policies for the Real World
Learned Neural Network based policies have shown promising results for r...
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Robot Navigation in Constrained Pedestrian Environments using Reinforcement Learning
Navigating fluently around pedestrians is a necessary capability for mob...
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Hierarchical Policy Design for Sample-Efficient Learning of Robot Table Tennis Through Self-Play
Training robots with physical bodies requires developing new methods and...
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A Framework for On-line Learning of Underwater Vehicles Dynamic Models
Learning the dynamics of robots from data can help achieve more accurate...
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Evolving Indoor Navigational Strategies Using Gated Recurrent Units In NEAT
Simultaneous Localisation and Mapping (SLAM) algorithms are expensive to...
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Learning Navigation Skills for Legged Robots with Learned Robot Embeddings
Navigation policies are commonly learned on idealized cylinder agents in simulation, without modelling complex dynamics, like contact dynamics, arising from the interaction between the robot and the environment. Such policies perform poorly when deployed on complex and dynamic robots, such as legged robots. In this work, we learn hierarchical navigation policies that account for the low-level dynamics of legged robots, such as maximum speed, slipping, and achieve good performance at navigating cluttered indoor environments. Once such a policy is learned on one legged robot, it does not directly generalize to a different robot due to dynamical differences, which increases the cost of learning such a policy on new robots. To overcome this challenge, we learn dynamics-aware navigation policies across multiple robots with robot-specific embeddings, which enable generalization to new unseen robots. We train our policies across three legged robots - 2 quadrupeds (A1, AlienGo) and a hexapod (Daisy). At test time, we study the performance of our learned policy on two new legged robots (Laikago, 4-legged Daisy) and show that our learned policy can sample-efficiently generalize to previously unseen robots.
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