A Lifelong Learning Approach to Mobile Robot Navigation

By Bo Liu, Xuesu Xiao, Peter Stone. 2021

118 citations recorded by the source corpus.

This letter presents a self-improving lifelong learning framework for a mobile robot navigating in different environments. Classical static navigation methods require environment-specific in-situ system adjustment, e.g., from human experts, or may repeat their mistakes regardless of how many times they have navigated in the same environment. Having the potential to improve with experience, learning-based navigation is highly dependent on access to training resources, e.g., sufficient memory and fast computation, and is prone to forgetting previously learned capability, especially when facing different environments. In this work, we propose Lifelong Learning for Navigation (LLfN) which (1) improves a mobile robot's navigation behavior purely based on its own experience, and (2) retains the robot's capability to navigate in previous environments after learning in new ones. LLfN is implemented and tested entirely onboard a physical robot with a limited memory and computation budget.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Reinforcement Learning

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Citations in the corpus

BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage (2022, background): “…with appropriate learning systems, could then potentially keep improving over time (Carlson et al., 2010; Kiela et al., 2021; Agichtein et al., 2006; Liu et al., 2021; Madotto et al., 2021; Shuster et al., 2021b), where (Hancock et al., 2019) refer to this approach as a self-feeding chatbot.”
Continual Learning and Private Unlearning (2022, methodology): “Due to its generality, CL has been applied to a variety of areas including computer vision (e.g. Kirkpatrick et al., 2017), reinforcement learning (e.g. Kirk-patrick et al., 2017; Riemer et al., 2018), natural language processing (e.g. Biesialska et al., 2020), and robotics (e.g. Liu et al., 2021).”
Π-ORFit: One-Pass Learning with Bregman Projection (2024, methodology): “It was utilized as a means to retain the memory of previously visited areas in the lifelong navigation framework in [17].”
Lifelong Learning and Personalization in Long-Term Human-Robot Interaction (LEAP-HRI) (2021, background): “prediction [14], manipulation [15]–[19], dialogue [20], navigation [1], [16], [21]–[23] and learning socially appropriate robot behaviors [24], [25].”