Auto-CEI pushes and estimates the limits of LLM reasoning capacities and aligns LLM's assertive and conservative response behaviours according to these limits for reliable reasoning.

We propose an easy-to-use framework that leverages sample and computational complexity from machine learning theory to analyze reasoning and planning problems and to design/optimize LLM-based reasoning methods.

We use Large Language Models as both the commonsense world model and the heuristic policy within Monte Carlo Tree Search. LLM's world model provides with MCTS a commonsense prior belief of states for reasoned decision-making. The LLM's heuristic policy guides the search to relevant parts of the tree, substantially reducing the search complexity.

We proposed ParaGon for language-conditioned object placing. ParaGon integrates a parsing algorithm into an end-to-end trainable neural network. It is data-efficient and generalizable for learning compositional instructions, and robust to noisy, ambiguous language inputs.

Risk-sensitive inverse reinforcement learning provides an general model to capture how human assess the distribution of a stochastic outcome when the true distribution is unknown (ambiguous). This work enables an RS-IRL learner to actively query expert demonstrations for faster risk envelope approximation.

Semantic SLAM projecting semantic meaning into 3D point clouds generated by ORB SLAM algorithm.