Saksham Goel
Papers
1
Total Citations
4
H-Index
1
About
Dr. Saksham Goel is a rising researcher in embodied artificial intelligence, with a focus on object-goal navigation and contextual decision-making under uncertainty. His most cited work, "A Contextual Bandit Approach for Learning to Plan in Environments with Probabilistic Goal Configurations" (2023), addresses a critical limitation in embodied AI: most object-goal navigation systems assume static target objects, such as a television or fridge. Goel’s modular framework introduces a contextual bandit approach that enables agents to adaptively plan and navigate toward objects with probabilistic goal configurations, significantly improving generalization in dynamic, real-world environments. This contribution bridges the gap between reinforcement learning and practical robotics, offering a scalable solution for agents that must search, recognize, and navigate to objects whose locations or states may change. Though early in his career, Goel’s work has already garnered attention (4 citations) and is shaping discussions on adaptive planning in embodied AI. His research holds promise for advancing autonomous systems in applications like service robotics and assistive navigation.
Research Focus
Key Achievements
Top Papers
- 1