Hafiq Anas
Papers
2
Total Citations
8
H-Index
2
About
Hafiq Anas is an emerging researcher specializing in deep reinforcement learning (DRL) and autonomous mobile robotics, with a particular focus on robot navigation in complex and dynamic environments. His work bridges the gap between theoretical reinforcement learning algorithms and their practical application in real-world robotic systems. His most influential contribution, "Comparison of Deep Q-Learning, Q-Learning and SARSA Reinforced Learning for Robot Local Navigation" (2022), has garnered 6 citations and provides a systematic evaluation of classical and deep reinforcement learning methods for local robot navigation — offering valuable benchmarking insights for robotics researchers selecting appropriate learning paradigms. Building on this foundation, his 2023 work on mapless crowd navigation introduces an innovative Collision Probability metric integrated directly into the observation space, addressing longstanding challenges of generalization and scalability that have hindered DRL-based crowd navigation systems. Though early in his research career, Anas demonstrates a clear trajectory toward advancing safe and intelligent robot autonomy in human-populated environments. His contributions are particularly relevant for researchers and students exploring socially aware robotics, reinforcement learning algorithm design, and the deployment of mobile robots in unstructured, real-world settings.
Research Focus
Key Achievements
Top Papers
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- 2