* External authors




Benchmarking Reinforcement Learning Techniques for Autonomous Navigation

Zifan Xu*

Bo Liu*

Xuesu Xiao*

Anirudh Nair*

Peter Stone

* External authors

ICRA 2023



Deep reinforcement learning (RL) has broughtmany successes for autonomous robot navigation. However,there still exists important limitations that prevent real-worlduse of RL-based navigation systems. For example, most learningapproaches lack safety guarantees; and learned navigationsystems may not generalize well to unseen environments.Despite a variety of recent learning techniques to tackle thesechallenges in general, a lack of an open-source benchmarkand reproducible learning methods specifically for autonomousnavigation makes it difficult for roboticists to choose whatlearning methods to use for their mobile robots and for learningresearchers to identify current shortcomings of general learningmethods for autonomous navigation. In this paper, we identifyfour major desiderata of applying deep RL approaches forautonomous navigation: (D1) reasoning under uncertainty, (D2)safety, (D3) learning from limited trial-and-error data, and (D4)generalization to diverse and novel environments. Then, weexplore four major classes of learning techniques with thepurpose of achieving one or more of the four desiderata:memory-based neural network architectures (D1), safe RL (D2),model-based RL (D2, D3), and domain randomization (D4). Bydeploying these learning techniques in a new open-source large-scale navigation benchmark and real-world environments, weperform a comprehensive study aimed at establishing to whatextent can these techniques achieve these desiderata for RL-based navigation systems

Related Publications

Symbolic State Space Optimization for Long Horizon Mobile Manipulation Planning.

International Conference on Intelligent Robots and Systems, 2023
Xiaohan Zhang*, Yifeng Zhu*, Yan Ding*, Yuqian Jiang*, Yuke Zhu*, Peter Stone, Shiqi Zhang*

In existing task and motion planning (TAMP) research, it is a common assumption that experts manually specify the state space for task-level planning. A welldeveloped state space enables the desirable distribution of limited computational resources between task planning an…

Event Tables for Efficient Experience Replay

CoLLAs, 2023
Varun Kompella, Thomas Walsh, Samuel Barrett, Peter R. Wurman, Peter Stone

Experience replay (ER) is a crucial component of many deep reinforcement learning (RL) systems. However, uniform sampling from an ER buffer can lead to slow convergence and unstable asymptotic behaviors. This paper introduces Stratified Sampling from Event Tables (SSET), whi…

Composing Efficient, Robust Tests for Policy Selection

UAI, 2023
Dustin Morrill, Thomas Walsh, Daniel Hernandez, Peter R. Wurman, Peter Stone

Modern reinforcement learning systems produce many high-quality policies throughout the learning process. However, to choose which policy to actually deploy in the real world, they must be tested under an intractable number of environmental conditions. We introduce RPOSST, a…

  • HOME
  • Publications
  • Benchmarking Reinforcement Learning Techniques for Autonomous Navigation


Shape the Future of AI with Sony AI

We want to hear from those of you who have a strong desire
to shape the future of AI.