Papers
5
Total Citations
23
H-Index
3
About
Yash Goel is a robotics researcher whose work lies at the intersection of motion planning, navigation under uncertainty, and semantic scene understanding. His most impactful contributions center on developing real-time, collision-free navigation frameworks for robots operating in dynamic, human-shared environments. Goel introduced the Inverse Velocity Obstacle (IVO) framework, an egocentric approach that improves upon traditional velocity obstacle methods for both single and multi-agent systems. He extended this work with PIVO, a probabilistic variant that explicitly handles state estimation and motion uncertainties, making navigation more robust in unpredictable settings. More recently, Goel has advanced context-aware robot exploration, developing semantically informed Model Predictive Control (MPC) and dense cost map prediction methods for object goal navigation—where a robot must locate a semantically specified target (e.g., "find a couch") in an unknown environment. His papers have accumulated over 20 citations, with his foundational IVO work receiving the most attention. By bridging low-level collision avoidance with high-level semantic reasoning, Goel’s research is paving the way for more intelligent, autonomous robots that can navigate complex, real-world spaces with greater efficiency and awareness.
Research Focus
Key Achievements
Top Papers
- 1IVO9 citations · 2019
- 2
- 3Semantically Informed MPC for Context-Aware Robot Exploration3 citations · 2023
- 4
- 5IVO: Inverse Velocity Obstacles for Real Time Navigation2 citations · 2019