Learning Parametric Constraints in High Dimensions from Demonstrations

2019/10/08 by Glen Chou, Chou, Glen, Necmiye Özay +3 · 4 citations
Computer Science · Engineering · #AI-based Problem Solving and Planning #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Reinforcement Learning in Robotics #Robot Manipulation and Learning #Robotics (cs.RO) #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1910.03477

openalex publication_date 2019/10/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We present a scalable algorithm for learning parametric constraints in high dimensions from safe expert demonstrations. To reduce the ill-posedness of the constraint recovery problem, our method uses hit-and-run sampling to generate lower cost, and thus unsafe, trajectories. Both safe and unsafe trajectories are used to obtain a representation of the unsafe set that is compatible with the data by solving an integer program in that representation's parameter space. Our method can either leverage a known parameterization or incrementally grow a parameterization while remaining consistent with the data, and we provide theoretical guarantees on the conservativeness of the recovered unsafe set. We evaluate our method on high-dimensional constraints for high-dimensional systems by learning constraints for 7-DOF arm, quadrotor, and planar pushing examples, and show that our method outperforms baseline approaches.

Citations

• On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming
• Multi-Goal Reinforcement Learning: Challenging Robotics Environments and\n Request for Research

Cited by

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• Learning Constraints from Stochastic Partially-Observed Closed-Loop Demonstrations
• Constraint Learning in Multi-Agent Dynamic Games from Demonstrations of Local Nash Interactions
• DEMONSTRATE: Zero-shot Language to Robotic Control via Multi-task Demonstration Learning

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