Arun Singh
Papers
1
Total Citations
5
H-Index
1
About
Arun Singh is a robotics researcher advancing the frontier of dual-arm manipulation through the integration of reinforcement learning and variable impedance control. His most-cited work, “Da-Vil: Adaptive Dual-Arm Manipulation with Reinforcement Learning and Variable Impedance Control” (2025, 5 citations), introduces a novel framework that enables robots to dynamically adjust their stiffness and damping during coordinated two-arm tasks. This adaptive approach allows robots to handle large objects, assemble components, and perform human-like interactions with greater dexterity and safety. Singh’s contributions address a critical gap in robotics: enabling machines to perform complex bimanual operations that require both precision and compliance. By combining learning-based methods with real-time impedance adaptation, his work lays the foundation for more versatile and responsive robotic systems in manufacturing, healthcare, and service applications. Though early in his career, Singh’s research has already attracted attention for its practical approach to a challenging problem. His focus on adaptive control and learning promises to shape the next generation of collaborative robots capable of working alongside humans in dynamic environments.
Research Focus
Key Achievements
Top Papers
- 1