Papers
3
Total Citations
70
H-Index
3
About
Tejas Kumar Shastha is a researcher at the forefront of assistive robotics, dedicated to restoring autonomy to individuals with severe physical disabilities. His work centers on developing intelligent robotic systems for Activities of Daily Living (ADLs), with a particular focus on the critical task of self-feeding. Shastha’s major contributions include the design of an autonomous multi-sensory robotic drinking assistant, which integrates vision and force feedback to adapt to a user’s needs. He has further advanced this domain by applying reinforcement learning, enabling the robot to learn and refine its feeding motions for greater safety and efficiency. His highly cited papers, including "Autonomous Multi-Sensory Robotic Assistant for a Drinking Task" (34 citations) and "Application of Reinforcement Learning to a Robotic Drinking Assistant" (28 citations), have established foundational work in the field. Beyond hardware and learning, Shastha has also addressed the critical need for software reliability in robotics. His notable work on "Formal Verification of ROS Based Systems Using a Linear Logic Theorem Prover" introduces a novel method to mathematically prove the correctness of robotic software, a key step toward deploying safe, trustworthy assistive systems in real-world care environments.
Research Focus
Key Achievements
Top Papers
- 1Autonomous Multi-Sensory Robotic Assistant for a Drinking Task34 citations · 2019
- 2Application of Reinforcement Learning to a Robotic Drinking Assistant28 citations · 2019
- 3