Learning from demonstrations: An intuitive VR environment for imitation learning of construction robots
On the map: Offline RL and imitation learning from data → Imitation learning methods
Abstract (arXiv)
Construction robots are challenging the traditional paradigm of labor intensive and repetitive construction tasks. Present concerns regarding construction robots are focused on their abilities in performing complex tasks consisting of several subtasks and their adaptability to work in unstructured and dynamic construction environments. Imitation learning (IL) has shown advantages in training a robot to imitate expert actions in complex tasks and the policy thereafter generated by reinforcement learning (RL) is more adaptive in comparison with pre-programmed robots. In this paper, we proposed a framework composed of two modules for imitation learning of construction robots. The first module provides an intuitive expert demonstration collection Virtual Reality (VR) platform where a robot will automatically follow the position, rotation, and actions of the expert's hand in real-time, instead of requiring an expert to control the robot via controllers. The second module provides a template for imitation learning using observations and actions recorded in the first module. In the second module, Behavior Cloning (BC) is utilized for pre-training, Generative Adversarial Imitation Learning (GAIL) and Proximal Policy Optimization (PPO) are combined to achieve a trade-off between the strength of imitation vs. exploration. Results show that imitation learning, especially when combined with PPO, could significantly accelerate training in limited training steps and improve policy performance.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data. Builds on 9 of its 58 references that are in the map. First posted on arXiv 23 May 2023.
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9 of 58 references.
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- 2018Behavioral Cloning from Observation4 May
Other references · 8
- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2015Continuous control with deep reinforcement learningcited by 3,407
- 2016Generative Adversarial Imitation Learningcited by 1,597
- 2017Curiosity-driven Exploration by Self-supervised Predictioncited by 1,194
- 2016Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimizationcited by 406
- 2018Unity: A General Platform for Intelligent Agentscited by 236
- 2021Long-Horizon Multi-Robot Rearrangement Planning for Construction Assemblycited by 24
- 2020Learning of Long-Horizon Sparse-Reward Robotic Manipulator Tasks with Base Controllerscited by 8
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- 2023Cloud-Based Hierarchical Imitation Learning for Scalable Transfer of Construction Skills from Human Workers to Assisting Robots
- 2025Sample-Efficient Robot Skill Learning for Construction Tasks: Benchmarking Hierarchical Reinforcement Learning and Vision-Language-Action VLA Model
- 2025Force-Based Robotic Imitation Learning: A Two-Phase Approach for Construction Assembly Tasks
- 2022Robust Learning from Observation with Model Misspecification
- 2025Interoceptive Robots for Convergent Shared Control in Collaborative Construction Work ↗
- 2025Model Predictive Adversarial Imitation Learning for Planning from Observation
- 2023A Survey of Imitation Learning: Algorithms, Recent Developments, and Challenges
- 2023PIRLNav: Pretraining with Imitation and RL Finetuning for ObjectNav