Aryslan Malik
Papers
8
Total Citations
100
H-Index
4
About
Aryslan Malik is a robotics researcher whose work lies at the intersection of advanced control theory, artificial intelligence, and space applications. His primary research areas include inverse kinematics, trajectory generation, and multi-objective optimization for high-degree-of-freedom robotic manipulators. Malik's most impactful contribution is his development of a deep reinforcement-learning approach for solving inverse kinematics of complex robotic arms, a paper that has garnered 62 citations and addresses fundamental challenges in robotic manipulator control. He has further advanced the field by pioneering the use of swarm intelligence combined with the Product of Exponentials formulation for multi-objective trajectory generation, demonstrating how to optimize both path efficiency and obstacle avoidance simultaneously. His work on generating constant screw axis trajectories using neural networks and machine learning has opened new pathways for end-effector control. Notably, Malik has applied his expertise to space robotics, modeling on-orbit maintenance robotic arm test-beds and developing RGB-D pose estimation systems for servicing arms in extraterrestrial environments. His research also extends to rehabilitation robotics, where he has explored flexible upper-limb rehabilitation using simple 1-DOF four-bar linkages, showing versatility in applying robotic principles across domains.
Research Focus
Key Achievements
Top Papers
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- 6Modeling of an On-Orbit Maintenance Robotic Arm Test-Bed2 citations · 2022
- 7Using Products of Exponentials to Define (Draw) Orbits and More2 citations · 2022
- 8RGB-D Robotic Pose Estimation For a Servicing Robotic Arm2 citations · 2022