# Bayesian inference for data-efficient, explainable, and safe robotic motion planning: A review

arXiv: https://arxiv.org/abs/2307.08024 · HTML: https://mlsanity.com/paper/2307.08024/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 53 of its 132 references that are in the map.
- First posted on arXiv 16 Jul 2023.

## Builds on (most cited first, 30 of 53)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables](https://mlsanity.com/paper/1611.00712/) (2016)
- [A Survey of Deep Active Learning](https://mlsanity.com/paper/2009.00236/) (2020)
- [Mean Field Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1802.05438/) (2018)
- [Monte Carlo Tree Search: A Review of Recent Modifications and Applications](https://mlsanity.com/paper/2103.04931/) (2021)
- [Bayesian Optimization with Safety Constraints: Safe and Automatic Parameter Tuning in Robotics](https://mlsanity.com/paper/1602.04450/) (2016)
- [Uncertainty Weighted Actor-Critic for Offline Reinforcement Learning](https://mlsanity.com/paper/2105.08140/) (2021)
- [Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2006.07169/) (2020)
- [Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learning](https://mlsanity.com/paper/2001.08726/) (2020)
- [Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing](https://mlsanity.com/paper/2102.07475/) (2021)
- [Uncertainty Quantification with Statistical Guarantees in End-to-End Autonomous Driving Control](https://mlsanity.com/paper/1909.09884/) (2019)
- [Constrained Policy Optimization via Bayesian World Models](https://mlsanity.com/paper/2201.09802/) (2022)
- [Parallel Gaussian Process Optimization with Upper Confidence Bound and Pure Exploration](https://mlsanity.com/paper/1304.5350/) (2013)
- [Meta-Learning Priors for Efficient Online Bayesian Regression](https://mlsanity.com/paper/1807.08912/) (2018)
- [Data-efficient Domain Randomization with Bayesian Optimization](https://mlsanity.com/paper/2003.02471/) (2020)
- [Risk-Averse Bayes-Adaptive Reinforcement Learning](https://mlsanity.com/paper/2102.05762/) (2021)
- [AC-Teach: A Bayesian Actor-Critic Method for Policy Learning with an Ensemble of Suboptimal Teachers](https://mlsanity.com/paper/1909.04121/) (2019)
- [GoSafe: Globally Optimal Safe Robot Learning](https://mlsanity.com/paper/2105.13281/) (2021)
- [PlaNet of the Bayesians: Reconsidering and Improving Deep Planning Network by Incorporating Bayesian Inference](https://mlsanity.com/paper/2003.00370/) (2020)
- [Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes](https://mlsanity.com/paper/2006.11441/) (2020)
- [Lifelong Incremental Reinforcement Learning with Online Bayesian Inference](https://mlsanity.com/paper/2007.14196/) (2020)
- [Curiosity-Driven Exploration via Latent Bayesian Surprise](https://mlsanity.com/paper/2104.07495/) (2021)
- [Learning Human Objectives from Sequences of Physical Corrections](https://mlsanity.com/paper/2104.00078/) (2021)
- [Safety Assurances for Human-Robot Interaction via Confidence-aware Game-theoretic Human Models](https://mlsanity.com/paper/2109.14700/) (2021)
- [Cooperation-Aware Reinforcement Learning for Merging in Dense Traffic](https://mlsanity.com/paper/1906.11021/) (2019)
- [Bayesian Controller Fusion: Leveraging Control Priors in Deep Reinforcement Learning for Robotics](https://mlsanity.com/paper/2107.09822/) (2021)
- [Spiking Neural Network on Neuromorphic Hardware for Energy-Efficient Unidimensional SLAM](https://mlsanity.com/paper/1903.02504/) (2019)

## Built on by (most cited first, 1 of 1)

- [Hybrid of representation learning and reinforcement learning for dynamic and complex robotic motion planning](https://mlsanity.com/paper/2309.03758/) (2023)

## Abstract (arXiv)

Bayesian inference has many advantages in robotic motion planning over four perspectives: The uncertainty quantification of the policy, safety (risk-aware) and optimum guarantees of robot motions, data-efficiency in training of reinforcement learning, and reducing the sim2real gap when the robot is applied to real-world tasks. However, the application of Bayesian inference in robotic motion planning is lagging behind the comprehensive theory of Bayesian inference. Further, there are no comprehensive reviews to summarize the progress of Bayesian inference to give researchers a systematic understanding in robotic motion planning. This paper first provides the probabilistic theories of Bayesian inference which are the preliminary of Bayesian inference for complex cases. Second, the Bayesian estimation is given to estimate the posterior of policies or unknown functions which are used to compute the policy. Third, the classical model-based Bayesian RL and model-free Bayesian RL algorithms for robotic motion planning are summarized, while these algorithms in complex cases are also analyzed. Fourth, the analysis of Bayesian inference in inverse RL is given to infer the reward functions in a data-efficient manner. Fifth, we systematically present the hybridization of Bayesian inference and RL which is a promising direction to improve the convergence of RL for better motion planning. Sixth, given the Bayesian inference, we present the interpretable and safe robotic motion plannings which are the hot research topic recently. Finally, all algorithms reviewed in this paper are summarized analytically as the knowledge graphs, and the future of Bayesian inference for robotic motion planning is also discussed, to pave the way for data-efficient, explainable, and safe robotic motion planning strategies for practical applications.