6 papers
Jaxolotl: A Unified High-Performance Benchmark Suite for LTL-Based Multi-Task RL
Mathias Jackermeier, Jacques Cloete, Alessandro Abate
Training agents to follow arbitrary instructions is an important goal of multi-task reinforcement learning (RL). Linear temporal logic (LTL) provides a precise and structured forma…
Zero-Shot Instruction Following in RL via Structured LTL Representations
Mathias Jackermeier, Mattia Giuri, Jacques Cloete +1
We study instruction following in multi-task reinforcement learning, where an agent must zero-shot execute novel tasks not seen during training. In this setting, linear temporal lo…
PlatoLTL: Scaling LTL-Guided Multi-Task RL
Jacques Cloete, Mathias Jackermeier, Ioannis Havoutis +1
Linear temporal logic (LTL) has emerged as a powerful formalism for specifying structured, temporally extended tasks in multi-task reinforcement learning (RL). However, while exist…
Vision-Language-Policy Model for Dynamic Robot Task Planning
Jin Wang, Kim Tien Ly, Jacques Cloete +3
Bridging the gap between natural language commands and autonomous execution in unstructured environments remains an open challenge for robotics. This requires robots to perceive an…
SPoRt -- Safe Policy Ratio: Certified Training and Deployment of Task Policies in Model-Free RL
Jacques Cloete, Nikolaus Vertovec, Alessandro Abate
To apply reinforcement learning to safety-critical applications, we ought to provide safety guarantees during both policy training and deployment. In this work, we present theoreti…
Adaptive Manipulation using Behavior Trees
Jacques Cloete, Wolfgang Merkt, Ioannis Havoutis
Many manipulation tasks pose a challenge since they depend on non-visual environmental information that can only be determined after sustained physical interaction has already begu…