# Active Constraint Learning in High Dimensions from Demonstrations

arXiv: https://arxiv.org/abs/2512.22757 · HTML: https://mlsanity.com/paper/2512.22757/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Robot learning from demonstration, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 13 of its 27 references that are in the map.
- First posted on arXiv 28 Dec 2025.

## Placed in

- Offline RL and imitation learning from data / [Robot learning from demonstration](https://mlsanity.com/offlinerl/robot-learning-from-demonstration/)

## Builds on (most cited first, 13 of 13)

- [PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training](https://mlsanity.com/paper/2106.05091/) (2021)
- [Efficiently Sampling Functions from Gaussian Process Posteriors](https://mlsanity.com/paper/2002.09309/) (2020)
- [Learning how to Active Learn: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/1708.02383/) (2017)
- [Maximum Likelihood Constraint Inference from Stochastic Demonstrations](https://mlsanity.com/paper/2102.12554/) (2021)
- [Risk-sensitive Inverse Reinforcement Learning via Semi- and Non-Parametric Methods](https://mlsanity.com/paper/1711.10055/) (2017)
- [Learning Constraints from Locally-Optimal Demonstrations under Cost Function Uncertainty](https://mlsanity.com/paper/2001.09336/) (2020)
- [Learning Parametric Constraints in High Dimensions from Demonstrations](https://mlsanity.com/paper/1910.03477/) (2019)
- [Uncertainty-Aware Constraint Learning for Adaptive Safe Motion Planning from Demonstrations](https://mlsanity.com/paper/2011.04141/) (2020)
- [Active Uncertainty Reduction for Human-Robot Interaction: An Implicit Dual Control Approach](https://mlsanity.com/paper/2202.07720/) (2022)
- [Gaussian Process Constraint Learning for Scalable Chance-Constrained Motion Planning from Demonstrations](https://mlsanity.com/paper/2112.04612/) (2021)
- [Bayesian Constraint Inference from User Demonstrations Based on Margin-Respecting Preference Models](https://mlsanity.com/paper/2403.02431/) (2024)
- [Intent Demonstration in General-Sum Dynamic Games via Iterative Linear-Quadratic Approximations](https://mlsanity.com/paper/2402.10182/) (2024)
- [Constraint Inference in Control Tasks from Expert Demonstrations via Inverse Optimization](https://mlsanity.com/paper/2304.03367/) (2023)

## Abstract (arXiv)

We present an iterative active constraint learning (ACL) algorithm, within the learning from demonstrations (LfD) paradigm, which intelligently solicits informative demonstration trajectories for inferring an unknown constraint in the demonstrator's environment. Our approach iteratively trains a Gaussian process (GP) on the available demonstration dataset to represent the unknown constraints, uses the resulting GP posterior to query start/goal states, and generates informative demonstrations which are added to the dataset. Across simulation and hardware experiments using high-dimensional nonlinear dynamics and unknown nonlinear constraints, our method outperforms a baseline, random-sampling based method at accurately performing constraint inference from an iteratively generated set of sparse but informative demonstrations.