Mobile robots have become indispensable in various environments, requiring them to perceive and understand their surroundings to perform tasks effectively. The deployment of service robots in familiar or partially known user environments has proven successful. However, exploring and exploiting unknown environments remains a challenging and time-consuming process. In this paper, a novel Trimmed Q-learning algorithm is introduced, enabling robots to predict interesting scenes through efficient memorability-oriented robotic behavioral scene activity training. This training process consists of online learning, short-term learning, and long-term learning modules, enabling autonomous exploration and better decision-making in unfamiliar environments.
Improving Scene Recognition
Efficient scene recognition is crucial for effective robotic navigation and exploration. The ability to quickly differentiate objects in the visual environment is a fundamental skill possessed by the human brain. Training artificial systems to achieve human-level performance in object differentiation remains a significant challenge. Navigation tasks become particularly challenging due to the limited information available. Designing an autonomous system requires accurate representation of initial and intended positions.
Importance of Scene Recognition
Scene recognition plays a vital role in the development of intelligent exploration in mobile robotics. Identifying interesting scenes allows robots to make informed decisions during navigation, avoiding obstacles or choosing optimal paths. Conventional methods struggle in unknown environments, often missing engaging scenes or getting stuck in repetitive ones. Existing approaches like interestingness detection, saliency detection, anomaly detection, novelty detection, and meaningfulness detection cannot effectively learn and classify scenes in both offline and online scenarios.
Addressing Object Detection Challenges
Accurate object detection is fundamental for a comprehensive understanding of images. However, factors such as varying viewpoints, poses, occlusion, and lighting conditions make object detection tedious and time-consuming. Object detection involves two crucial processes: selecting informative regions and extracting significant features. The former is challenging due to the presence of objects with different aspect ratios in an image, often leading to computational inefficiencies. Extracting reliable visual features is hindered by the diverse nature of images and their inherent properties like faded appearance and varying backgrounds.
The proposed Trimmed Q-learning algorithm overcomes these challenges and significantly improves scene recognition in mobile robotics. Experimental evaluations conducted on public datasets, SubT, and SUN databases confirm the efficacy of the framework. Short-term and online learning modules achieve a memorability score of 72.84%, while long-term learning modules achieve a score of 68.63%. By leveraging efficient memorability-oriented training, mobile robots can enhance their performance in exploration and decision-making processes, making them more capable in unknown environments.
Q: What is the major challenge in exploring unknown environments using mobile robots?
A: Exploring and exploiting unknown environments is a tedious task for mobile robots.
Q: How does the proposed Trimmed Q-learning algorithm help improve scene recognition?
A: The Trimmed Q-learning algorithm enables robots to predict interesting scenes through efficient memorability-oriented training.
Q: What are the stages involved in the training process?
A: The training process involves online learning, short-term learning, and long-term learning modules.
Q: What are some existing approaches that struggle in learning scenes in unknown environments?
A: Existing approaches like interestingness detection, saliency detection, anomaly detection, novelty detection, and meaningfulness detection face challenges in both offline and online learning scenarios.
Q: What is the effectiveness of the proposed framework?
A: The proposed framework achieves better memorability scores of 72.84% in short-term and online learning and 68.63% in long-term learning.