Henrique Ferreira
Papers
2
Total Citations
5
H-Index
2
About
Henrique Ferreira is a robotics researcher focused on advancing autonomous manipulation and navigation through deep reinforcement learning (DRL). His work tackles two core challenges in embodied AI: enabling robots to move safely through cluttered spaces and to grasp objects adaptively in dynamic settings. In his 2023 paper “Using Deep Reinforcement Learning for Navigation in Simulated Hallways” (3 citations), Ferreira demonstrated how DRL agents can learn collision-free path planning without pre-existing maps, using only raw sensor inputs—a key step toward truly autonomous mobile robots. His more recent 2025 study “Deep Reinforcement Learning for Adaptive Robotic Grasping and Post-Grasp Manipulation in Simulated Dynamic Environments” (2 citations) introduces **UR5GraspingEnv**, a PyBullet-based simulation environment integrated with OpenAI Gym. By applying the Soft Actor-Critic (SAC) algorithm, he achieved robust, real-time grasping and post-grasp adjustments under moving obstacles, bridging the gap between simulation and real-world dexterity. Though early in his career, Ferreira’s contributions are already shaping how reinforcement learning can be practically deployed in robotics, offering scalable solutions for industrial automation, assistive robotics, and autonomous exploration.
Research Focus
Key Achievements
Top Papers
- 1Using Deep Reinforcement Learning for Navigation in Simulated Hallways3 citations · 2023
- 2