# The Role of Predictive Uncertainty and Diversity in Embodied AI and Robot Learning

arXiv: https://arxiv.org/abs/2405.03164 · HTML: https://mlsanity.com/paper/2405.03164/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 77 of its 156 references that are in the map.
- First posted on arXiv 6 May 2024.

## Builds on (most cited first, 30 of 77)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Voyager: An Open-Ended Embodied Agent with Large Language Models](https://mlsanity.com/paper/2305.16291/) (2023)
- [Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift](https://mlsanity.com/paper/1906.02530/) (2019)
- [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021)
- [A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification](https://mlsanity.com/paper/2107.07511/) (2021)
- [PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes](https://mlsanity.com/paper/1711.00199/) (2017)
- [Evidential Deep Learning to Quantify Classification Uncertainty](https://mlsanity.com/paper/1806.01768/) (2018)
- [Predictive Uncertainty Estimation via Prior Networks](https://mlsanity.com/paper/1802.10501/) (2018)
- [Habitat 2.0: Training Home Assistants to Rearrange their Habitat](https://mlsanity.com/paper/2106.14405/) (2021)
- [Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm](https://mlsanity.com/paper/1608.04471/) (2016)
- [Measuring Calibration in Deep Learning](https://mlsanity.com/paper/1904.01685/) (2019)
- [Deep Kernel Learning](https://mlsanity.com/paper/1511.02222/) (2015)
- [Deep Evidential Regression](https://mlsanity.com/paper/1910.02600/) (2019)
- [Conformal prediction beyond exchangeability](https://mlsanity.com/paper/2202.13415/) (2022)
- [Occ3D: A Large-Scale 3D Occupancy Prediction Benchmark for Autonomous Driving](https://mlsanity.com/paper/2304.14365/) (2023)
- [Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners](https://mlsanity.com/paper/2307.01928/) (2023)
- [Unsupervised Learning for Physical Interaction through Video Prediction](https://mlsanity.com/paper/1605.07157/) (2016)
- [Hamilton-Jacobi Reachability: A Brief Overview and Recent Advances](https://mlsanity.com/paper/1709.07523/) (2017)
- [iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Tasks](https://mlsanity.com/paper/2108.03272/) (2021)
- [Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP)](https://mlsanity.com/paper/1503.01057/) (2015)
- [Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts](https://mlsanity.com/paper/2006.09239/) (2020)
- [Evaluating Bayesian Deep Learning Methods for Semantic Segmentation](https://mlsanity.com/paper/1811.12709/) (2018)
- [Language Models with Conformal Factuality Guarantees](https://mlsanity.com/paper/2402.10978/) (2024)
- [Epistemic Neural Networks](https://mlsanity.com/paper/2107.08924/) (2021)
- [Safe Planning in Dynamic Environments using Conformal Prediction](https://mlsanity.com/paper/2210.10254/) (2022)
- [A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving](https://mlsanity.com/paper/2011.10671/) (2020)

## Built on by (most cited first, 11 of 11)

- [Failures Are Fated, But Can Be Faded: Characterizing and Mitigating Unwanted Behaviors in Large-Scale Vision and Language Models](https://mlsanity.com/paper/2406.07145/) (2024)
- [RoboMD: Uncovering Robot Vulnerabilities through Semantic Potential Fields](https://mlsanity.com/paper/2412.02818/) (2024)
- [Explainable Concept Generation through Vision-Language Preference Learning for Understanding Neural Networks' Internal Representations](https://mlsanity.com/paper/2408.13438/) (2024)
- [Fairness in Autonomous Driving: Towards Understanding Confounding Factors in Object Detection under Challenging Weather](https://mlsanity.com/paper/2406.00219/) (2024)
- [3D Foundation Models Enable Simultaneous Geometry and Pose Estimation of Grasped Objects](https://mlsanity.com/paper/2407.10331/) (2024)
- [Joint Flow Trajectory Optimization For Feasible Robot Motion Generation from Video Demonstrations](https://mlsanity.com/paper/2509.20703/) (2025)
- [The Anatomy of Uncertainty in LLMs](https://mlsanity.com/paper/2603.24967/) (2026)
- [Cross-Modal Instructions for Robot Motion Generation](https://mlsanity.com/paper/2509.21107/) (2025)
- [The Essence of Balance for Self-Improving Agents in Vision-and-Language Navigation](https://mlsanity.com/paper/2604.19064/) (2026)
- [BaTCAVe: Trustworthy Explanations for Robot Behaviors](https://mlsanity.com/paper/2409.10733/) (2024)
- [Uncertainty, Vagueness, and Ambiguity in Human-Robot Interaction: Why Conceptualization Matters](https://mlsanity.com/paper/2604.15339/) (2026)

## Abstract (arXiv)

Uncertainty has long been a critical area of study in robotics, particularly when robots are equipped with analytical models. As we move towards the widespread use of deep neural networks in robots, which have demonstrated remarkable performance in research settings, understanding the nuances of uncertainty becomes crucial for their real-world deployment. This guide offers an overview of the importance of uncertainty and provides methods to quantify and evaluate it from an applications perspective.