Learning Robotic Navigation from Experience: Principles, Methods, and Recent Results
Navigation is one of the most heavily studied problems in robotics, and is conventionally approached as a geometric mapping and planning problem. However, real-world navigation presents a complex set of physical challenges that defies simple geometric abstractions. Machine learning offers a promising way to go beyond geometry and conventional planning, allowing for navigational systems that make decisions based on actual prior experience. Such systems can reason about traversability in ways that go beyond geometry, accounting for the physical outcomes of their actions and exploiting patterns in real-world environments. They can also improve as more data is collected, potentially providing a powerful network effect. In this article, we present a general toolkit for experiential learning of robotic navigation skills that unifies several recent approaches, describe the underlying design principles, summarize experimental results from several of our recent papers, and discuss open problems and directions for future work.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems
This paper was motivated by the problem of how to make robots fuse and transfer their experience so that they can effectively use prior knowledge and quickly adapt to new environments. To address the problem, we present …
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Robot Navigation+1Principles and Guidelines for Evaluating Social Robot Navigation Algorithms
A major challenge to deploying robots widely is navigation in human-populated environments, commonly referred to as social robot navigation. While the field of social navigation has advanced tremendously in recent years,…
BenchmarkingRobot NavigationSocial NavigationAutonomous Goal Exploration using Learned Goal Spaces for Visuomotor Skill Acquisition in Robots
The automatic and efficient discovery of skills, without supervision, for long-living autonomous agents, remains a challenge of Artificial Intelligence. Intrinsically Motivated Goal Exploration Processes give learning ag…
Lifelong learningRepresentation LearningE2Map: Experience-and-Emotion Map for Self-Reflective Robot Navigation with Language Models
Large language models (LLMs) have shown significant potential in guiding embodied agents to execute language instructions across a range of tasks, including robotic manipulation and navigation. However, existing methods …
General KnowledgeRobot NavigationThe Grossberg Code: Universal Neural Network Signatures of Perceptual Experience
Two universal functional principles of Adaptive Resonance Theory simulate the brain code of all biological learning and adaptive intelligence. Low level representations of multisensory stimuli in their immediate environm…