Ashish Kapoor
Papers
2
Total Citations
6
H-Index
2
About
Ashish Kapoor is a researcher working at the intersection of robotics, autonomous systems, and machine learning, with a focus on safe planning and control under uncertainty. His work addresses fundamental challenges in making autonomous systems operate reliably in complex, real-world environments. In his 2017 paper on fast second-order cone programming, Kapoor tackled a critical bottleneck in robust mission planning — the computational infeasibility of existing SOCP solvers — proposing faster methods that make safe control more practically viable for dynamic systems navigating uncertain environments. This work has garnered 4 citations, reflecting its contribution to the optimization and control communities. His 2020 investigation into adversarial attacks on optimization-based planners highlights a growing concern in autonomous systems research: the vulnerability of trajectory planning algorithms to deliberate perturbations. With 2 citations, this work contributes to an emerging dialogue on the security and robustness of robot planning pipelines. Together, Kapoor's research underscores a commitment to bridging theoretical rigor with practical safety, making autonomous systems both computationally efficient and resilient — qualities increasingly essential as robotics and AI move into high-stakes deployment scenarios.
Research Focus
Key Achievements
Top Papers
- 1Fast second-order cone programming for safe mission planning4 citations · 2017
- 2Adversarial Attacks on Optimization based Planners2 citations · 2020