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The Intelligent Robot Simulator is developed, a lightweight declarative simulator implemented as a Python library to support navigation learning and benchmarking through rapid construction of reusable and diverse scenarios and efficient execution on accessible platforms.
Shape-Aware Reinforcement Learned Model Predictive Control is proposed, a method for safe, efficient, and adaptive navigation in crowds with heterogeneous shapes without geometry simplification that preserves the safety structure and generalizability of MPC while integrating the adaptability and intelligence of RL.
A neuro-symbolic program synthesis approach for batch-editing collision meshes is presented, which synthesizes a reusable program that captures the editing intent and applies it to non-demonstration meshes.
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