Tarun Chiruvolu
Papers
1
Total Citations
5
H-Index
1
About
Tarun Chiruvolu is a researcher at the intersection of robotics, reinforcement learning, and large language models (LLMs), with a focus on enabling autonomous systems to solve complex, long-horizon tasks. His most cited work, "Plan-Seq-Learn: Language Model Guided RL for Solving Long Horizon Robotics Tasks" (2024, 5 citations), introduces a novel framework that leverages LLMs for high-level task planning while integrating reinforcement learning to acquire low-level skills without requiring a pre-defined skill library. This contribution addresses a critical bottleneck in robotics—bridging the gap between symbolic planning and physical execution—by allowing robots to autonomously learn and sequence behaviors like picking, placing, or pushing through trial and error. Chiruvolu’s approach reduces reliance on human-engineered skill repositories, making robotic systems more adaptable to unstructured environments. His work has been recognized for its potential to advance embodied AI, and he continues to explore how language models can guide hierarchical learning in robotics. With a growing citation impact, Chiruvolu is establishing himself as a rising voice in the field, pushing the boundaries of how machines can reason, plan, and act in the real world.
Research Focus
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Top Papers
- 1