Papers
8
Total Citations
129
H-Index
5
About
Gautham Vasan is a researcher specializing in reinforcement learning, robotics, and assistive technology, with work spanning both theoretical foundations and real-world applications. His most influential contribution, "Benchmarking Reinforcement Learning Algorithms on Real-World Robots" (2018, 46 citations), helped bridge the gap between simulation-based successes and physical robotic deployment, providing the research community with reproducible benchmarks for continuous control tasks. Equally impactful is his work on prosthetic limb control, where his 2017 paper on teaching myoelectric prostheses through reinforcement learning (39 citations) demonstrated how intact-limb demonstrations could enable more natural, intuitive movement for amputees — a meaningful advance in assistive technology. His 2019 paper on autoregressive policies introduced novel exploration strategies beyond standard Gaussian approaches, improving trajectory smoothness in deep reinforcement learning. Vasan has also contributed to autonomous robotics, including UAV landing systems and solar panel cleaning robots. More recently, his work on real-time reinforcement learning for distributed robotic systems addresses practical constraints of deploying learning agents in dynamic environments. With over 125 total citations, Vasan's research consistently pursues the challenge of making reinforcement learning work reliably and meaningfully in the physical world.
Research Focus
Key Achievements
Top Papers
- 1Benchmarking Reinforcement Learning Algorithms on Real-World Robots46 citations · 2018
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- 3A Control Strategy for an Autonomous Robotic Vacuum Cleaner for Solar Panels17 citations · 2014
- 4Autoregressive Policies for Continuous Control Deep Reinforcement Learning13 citations · 2019
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