Manoj Bhat
Papers
2
Total Citations
23
H-Index
2
About
Manoj Bhat is a researcher at the intersection of autonomous systems and soft robotics, with key contributions in trajectory prediction for self-driving vehicles and the development of cost-effective soft actuators. His most cited work, "Trajformer: Trajectory Prediction with Local Self-Attentive Contexts for Autonomous Driving," introduces a novel transformer-based architecture that enhances contextual understanding for multimodal trajectory forecasting—a critical challenge in autonomous navigation. With 18 citations, this paper addresses limitations in generative models by improving scene representation, directly impacting the safety and reliability of autonomous driving systems. In parallel, Bhat explores soft robotics through his work on RTV-2 silicone rubber actuators, where he combines experimental and numerical methods to optimize actuator performance. This study, with 5 citations, demonstrates his commitment to making soft robotics more accessible by identifying affordable alternatives to expensive materials. By bridging cutting-edge deep learning with practical materials science, Bhat exemplifies a versatile engineering approach—advancing both the intelligence and the physical embodiment of next-generation robotic systems.
Research Focus
Key Achievements
Top Papers
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- 2