Shivam Goel

Washington State University, Tufts University

Papers

Total Citations

117

H-Index

About

Shivam Goel’s research sits at the intersection of robotics, artificial intelligence, and human-centered automation, with a focus on enabling intelligent systems to operate seamlessly in complex, real-world environments. His work spans assistive robotics, transfer learning, and continual learning, addressing fundamental challenges in autonomy and adaptability. Goel’s most cited paper, “Robot-enabled support of daily activities in smart home environments” (2018, 110 citations), established a foundational framework for integrating robotic assistance into smart homes, demonstrating how robots can proactively aid humans in daily tasks—a contribution that has shaped subsequent work in ambient assisted living. More recently, he has pioneered approaches to reduce the prohibitive training costs of reinforcement learning in robotics. His 2023 paper on few-shot policy transfer introduces a novel framework combining observation mapping and behavior cloning, enabling robots to transfer skills from simulation to reality (Sim2Real) with minimal data. In 2024, Goel advanced the field further with a neurosymbolic architecture for goal-conditioned continual learning, merging symbolic planning with reinforcement learning to help autonomous systems adapt to unpredictable novelties in open-world environments. This work promises to make robots more resilient and lifelong learners, marking Goel as a rising voice in scalable, intelligent autonomy.