7 papers
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…
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…
Agentic LLM Planning via Step-Wise PDDL Simulation: An Empirical Characterisation
Kai Göbel, Pierrick Lorang, Patrik Zips +1
Task planning, the problem of sequencing actions to reach a goal from an initial state, is a core capability requirement for autonomous robotic systems. Whether large language mode…
The Price Is Not Right: Neuro-Symbolic Methods Outperform VLAs on Structured Long-Horizon Manipulation Tasks with Significantly Lower Energy Consumption
Timothy Duggan, Pierrick Lorang, Hong Lu +1
Vision-Language-Action (VLA) models have recently been proposed as a pathway toward generalist robotic policies capable of interpreting natural language and visual inputs to genera…
Breaking Task Impasses Quickly: Adaptive Neuro-Symbolic Learning for Open-World Robotics
Pierrick Lorang
Adapting to unforeseen novelties in open-world environments remains a major challenge for autonomous systems. While hybrid planning and reinforcement learning (RL) approaches show…
Few-Shot Neuro-Symbolic Imitation Learning for Long-Horizon Planning and Acting
Pierrick Lorang, Hong Lu, Johannes Huemer +2
Imitation learning enables intelligent systems to acquire complex behaviors with minimal supervision. However, existing methods often focus on short-horizon skills, require large d…