7 papers
OIL: An Object Interaction Layer for Humanoid Whole-Body Control
Pranay Dugar, Mohitvishnu S. Gadde, Rajesh Mangannavar +2
Recent progress in learned whole-body controllers for humanoid robots has enabled robust free-space motion by tracking kinematic targets such as joint or end-effector trajectories.…
Training the Orchestrator: A Supervised Approach to End-to-End PDDL Planning with LLM Agents
Rajesh Mangannavar, Zachary Coalson, Pranay Dugar +1
Translating natural-language planning intent into verified plans is a longstanding challenge: people communicate goals in language, while classical planners require formal PDDL spe…
GLARE: A Natural Language Interface for Querying Global Explanations
Bhavan Vasu, Rajesh Mangannavar
While global explanations are crucial for understanding vision models across datasets, classes, and decision contexts, their complex and monolithic nature often hinders practical e…
GammaZero: Learning To Guide POMDP Belief Space Search With Graph Representations
Rajesh Mangannavar, Prasad Tadepalli
We introduce an uncertainty-aware graph representation framework for learning to guide planning in Partially Observable Markov Decision Processes (POMDPs). Unlike existing approach…
Planning with affordances: Integrating learned affordance models and symbolic planning
Rajesh Mangannavar
Intelligent agents working in real-world environments must be able to learn about the environment and its capabilities which enable them to take actions to change to the state of t…
Graph Neural Network Based Action Ranking for Planning
Rajesh Mangannavar, Stefan Lee, Alan Fern +1
We propose a novel approach to learn relational policies for classical planning based on learning to rank actions. We introduce a new graph representation that explicitly captures…
Hierarchical Object-Oriented POMDP Planning for Object Rearrangement
Rajesh Mangannavar, Alan Fern, Prasad Tadepalli
We present an online planning framework and a new benchmark dataset for solving multi-object rearrangement problems in partially observable, multi-room environments. Current object…