Active Constraint Learning in High Dimensions from Demonstrations

Zheng Qiu, Chih-Yuan Chiu, Glen Chou
Proceedings of The 8th Annual Learning for Dynamics and Control Conference, PMLR 331:532-556, 2026.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v331-qiu26a, title = {Active Constraint Learning in High Dimensions from Demonstrations}, author = {Qiu, Zheng and Chiu, Chih-Yuan and Chou, Glen}, booktitle = {Proceedings of The 8th Annual Learning for Dynamics and Control Conference}, pages = {532--556}, year = {2026}, editor = {Sukhatme, Gaurav and Lindemann, Lars and Tu, Stephen and Wierman, Adam and Atanasov, Nikolay}, volume = {331}, series = {Proceedings of Machine Learning Research}, month = {17--19 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v331/main/assets/qiu26a/qiu26a.pdf}, url = {https://proceedings.mlr.press/v331/qiu26a.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T Active Constraint Learning in High Dimensions from Demonstrations %A Zheng Qiu %A Chih-Yuan Chiu %A Glen Chou %B Proceedings of The 8th Annual Learning for Dynamics and Control Conference %C Proceedings of Machine Learning Research %D 2026 %E Gaurav Sukhatme %E Lars Lindemann %E Stephen Tu %E Adam Wierman %E Nikolay Atanasov %F pmlr-v331-qiu26a %I PMLR %P 532--556 %U https://proceedings.mlr.press/v331/qiu26a.html %V 331 %X 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.
APA
Qiu, Z., Chiu, C. & Chou, G.. (2026). Active Constraint Learning in High Dimensions from Demonstrations. Proceedings of The 8th Annual Learning for Dynamics and Control Conference, in Proceedings of Machine Learning Research 331:532-556 Available from https://proceedings.mlr.press/v331/qiu26a.html.

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