Tyler Feldman

About

Tyler Feldman is a rising researcher in embodied AI and robotic task planning, with a focus on bridging large language models (LLMs) and real-world execution. His most-cited work, "ConceptAgent: LLM-Driven Precondition Grounding and Tree Search for Robust Task Planning and Execution" (2025), tackles the critical challenge of open-world robotic manipulation—where vast state spaces and task variability often derail traditional planners. Feldman’s key contribution is a novel framework that combines LLM-driven precondition grounding with tree search algorithms, enabling robots to robustly decompose and execute complex tasks despite perceptual uncertainty. This work has already garnered early citations, signaling its impact on the field. By grounding abstract LLM knowledge in concrete environmental preconditions, Feldman addresses a core bottleneck in autonomous robotics: the gap between high-level reasoning and low-level control. His approach promises more adaptable and reliable robots for unstructured settings, from homes to industrial sites. As an early-career researcher, Feldman is establishing himself at the forefront of LLM-robot integration, with potential to shape how machines understand and act in the physical world.

Research Focus

Key Achievements

Top Papers

1. 1
   ConceptAgent: LLM-Driven Precondition Grounding and Tree Search for Robust Task Planning and Execution

   2 citations · 2025

Key Collaborators

Contact & Links