Anderson da Silva Soares
Papers
4
Total Citations
42
H-Index
3
About
Anderson da Silva Soares is a researcher at the forefront of intelligent robotics and autonomous systems, with a primary focus on reinforcement learning (RL) and evolutionary algorithms for robotic control and navigation. His most significant contribution is the development of a unified RL framework that simultaneously addresses both strategic decision-making and low-level control in complex environments, as demonstrated in his highly cited work on multiagent self-play for robotic soccer (21 citations). Soares has also pioneered hybrid approaches that accelerate learning, notably combining Deep Q-Networks with the Extended Kalman Filter (EKF-DQN) to improve localization and reward maximization in autonomous navigation. His earlier work on evolutionary algorithms for obstacle avoidance (15 citations) laid the groundwork for these advances. Beyond mobile robots, Soares has contributed to biomechanical engineering through the conceptual and mechanical design of an anthropometric-scaled biped robot capable of reproducing human gait patterns, both normal and pathological. This breadth—from evolutionary control to hybrid RL and humanoid design—positions Soares as a versatile innovator pushing the boundaries of how robots learn, navigate, and interact with the physical world.
Research Focus
Key Achievements
Top Papers
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- 2An Evolutionary Algorithm for Autonomous Robot Navigation15 citations · 2016
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