Autrio Das

Indian Institute of Technology Hyderabad

Papers

Total Citations

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About

Autrio Das is a robotics researcher whose work focuses on advancing dual-arm manipulation through the integration of reinforcement learning and adaptive control strategies. His most-cited paper, "Da-Vil: Adaptive Dual-Arm Manipulation with Reinforcement Learning and Variable Impedance Control" (2025, 5 citations), introduces a novel framework that enables robots to perform complex, coordinated bimanual tasks—such as handling large objects and assembling components—by dynamically adjusting impedance in response to environmental interactions. This contribution addresses a critical gap in robotics, where dual-arm coordination remains challenging due to the need for real-time adaptability and human-like dexterity. Das’s work demonstrates how reinforcement learning can be combined with variable impedance control to achieve robust, adaptive manipulation, paving the way for more versatile robotic systems in industrial and service settings. Though early in his career, his research has already garnered attention for its practical implications in automating tasks that require two-arm coordination. Das’s approach not only enhances robotic efficiency but also brings us closer to robots that can seamlessly collaborate with humans in shared workspaces. His ongoing efforts promise to further bridge the gap between simulation and real-world deployment, making him a rising figure in the field of robotic manipulation.