3 papers
cs.RO2026
Build on Priors: Vision--Language--Guided Neuro-Symbolic Imitation Learning for Data-Efficient Real-World Robot Manipulation
Pierrick Lorang, Johannes Huemer, Timothy Duggan +3
Enabling robots to learn long-horizon manipulation tasks from a handful of demonstrations remains a central challenge in robotics. Existing neuro-symbolic approaches often rely on…
cs.RO2026
Novelty Adaptation Through Hybrid Large Language Model (LLM)-Symbolic Planning and LLM-guided Reinforcement Learning
Hong Lu, Pierrick Lorang, Timothy R. Duggan +2
In dynamic open-world environments, autonomous agents often encounter novelties that hinder their ability to find plans to achieve their goals. Specifically, traditional symbolic p…
cs.RO2025
Optimal Design of Continuum Robots with Reachability Constraints
Hyunmin Cheong, Mehran Ebrahimi, Timothy Duggan
While multi-joint continuum robots are highly dexterous and flexible, designing an optimal robot can be challenging due to its kinematics involving curvatures. Hence, the current w…