Model Predictive Adversarial Imitation Learning for Planning from Observation

• URL: http://arxiv.org/abs/2507.21533v1
• Date: Tue, 29 Jul 2025 06:52:52 GMT
• Title: Model Predictive Adversarial Imitation Learning for Planning from Observation
• Authors: Tyler Han, Yanda Bao, Bhaumik Mehta, Gabriel Guo, Anubhav Vishwakarma, Emily Kang, Sanghun Jung, Rosario Scalise, Jason Zhou, Bryan Xu, Byron Boots,
• Abstract summary: We derive a replacement of the policy in IRL with a planning-based agent.<n>We study and observe significant improvements on sample efficiency, out-of-distribution generalization, and robustness.
• Score: 13.427459817316317
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Human demonstration data is often ambiguous and incomplete, motivating imitation learning approaches that also exhibit reliable planning behavior. A common paradigm to perform planning-from-demonstration involves learning a reward function via Inverse Reinforcement Learning (IRL) then deploying this reward via Model Predictive Control (MPC). Towards unifying these methods, we derive a replacement of the policy in IRL with a planning-based agent. With connections to Adversarial Imitation Learning, this formulation enables end-to-end interactive learning of planners from observation-only demonstrations. In addition to benefits in interpretability, complexity, and safety, we study and observe significant improvements on sample efficiency, out-of-distribution generalization, and robustness. The study includes evaluations in both simulated control benchmarks and real-world navigation experiments using few-to-single observation-only demonstrations.

Related papers

• ActivePusher: Active Learning and Planning with Residual Physics for Nonprehensile Manipulation [2.7405276609125164]
  Planning with learned dynamics models offers a promising approach toward real-world, long-horizon manipulation.<n>ActivePusher is a framework that combines residual-physics modeling with kernel-based uncertainty-driven active learning.<n>We evaluate our approach in both simulation and real-world environments and demonstrate that it improves data efficiency and planning success rates compared to baseline methods.
  arXiv  Detail & Related papers  (2025-06-05T05:28:14Z)
• Latent Diffusion Planning for Imitation Learning [78.56207566743154]
  Latent Diffusion Planning (LDP) is a modular approach consisting of a planner and inverse dynamics model.<n>By separating planning from action prediction, LDP can benefit from the denser supervision signals of suboptimal and action-free data.<n>On simulated visual robotic manipulation tasks, LDP outperforms state-of-the-art imitation learning approaches.
  arXiv  Detail & Related papers  (2025-04-23T17:53:34Z)
• ACT-JEPA: Novel Joint-Embedding Predictive Architecture for Efficient Policy Representation Learning [90.41852663775086]
  ACT-JEPA is a novel architecture that integrates imitation learning and self-supervised learning.<n>We train a policy to predict action sequences and abstract observation sequences.<n>Our experiments show that ACT-JEPA improves the quality of representations by learning temporal environment dynamics.
  arXiv  Detail & Related papers  (2025-01-24T16:41:41Z)
• Sample-efficient Adversarial Imitation Learning [45.400080101596956]
  We propose a self-supervised representation-based adversarial imitation learning method to learn state and action representations. We show a 39% relative improvement over existing adversarial imitation learning methods on MuJoCo in a setting limited to 100 expert state-action pairs.
  arXiv  Detail & Related papers  (2023-03-14T12:36:01Z)
• Imitating, Fast and Slow: Robust learning from demonstrations via decision-time planning [96.72185761508668]
  Planning at Test-time (IMPLANT) is a new meta-algorithm for imitation learning. We demonstrate that IMPLANT significantly outperforms benchmark imitation learning approaches on standard control environments.
  arXiv  Detail & Related papers  (2022-04-07T17:16:52Z)
• Visual Adversarial Imitation Learning using Variational Models [60.69745540036375]
  Reward function specification remains a major impediment for learning behaviors through deep reinforcement learning. Visual demonstrations of desired behaviors often presents an easier and more natural way to teach agents. We develop a variational model-based adversarial imitation learning algorithm.
  arXiv  Detail & Related papers  (2021-07-16T00:15:18Z)
• Imitation by Predicting Observations [17.86983397979034]
  We present a new method for imitation solely from observations that achieves comparable performance to experts on challenging continuous control tasks. Our method, which we call FORM, is derived from an inverse RL objective and imitates using a model of expert behavior learned by generative modelling of the expert's observations. We show that FORM performs comparably to a strong baseline IRL method (GAIL) on the DeepMind Control Suite benchmark, while outperforming GAIL in the presence of task-irrelevant features.
  arXiv  Detail & Related papers  (2021-07-08T14:09:30Z)
• DEALIO: Data-Efficient Adversarial Learning for Imitation from Observation [57.358212277226315]
  In imitation learning from observation IfO, a learning agent seeks to imitate a demonstrating agent using only observations of the demonstrated behavior without access to the control signals generated by the demonstrator. Recent methods based on adversarial imitation learning have led to state-of-the-art performance on IfO problems, but they typically suffer from high sample complexity due to a reliance on data-inefficient, model-free reinforcement learning algorithms. This issue makes them impractical to deploy in real-world settings, where gathering samples can incur high costs in terms of time, energy, and risk. We propose a more data-efficient IfO algorithm
  arXiv  Detail & Related papers  (2021-03-31T23:46:32Z)
• Stochastic Action Prediction for Imitation Learning [1.6385815610837169]
  Imitation learning is a data-driven approach to acquiring skills that relies on expert demonstrations to learn a policy that maps observations to actions. We demonstrate inherentity in demonstrations collected for tasks including line following with a remote-controlled car. We find that accounting for adversariality in the expert data leads to substantial improvement in the success rate of task completion.
  arXiv  Detail & Related papers  (2020-12-26T08:02:33Z)
• Task-Feature Collaborative Learning with Application to Personalized Attribute Prediction [166.87111665908333]
  We propose a novel multi-task learning method called Task-Feature Collaborative Learning (TFCL) Specifically, we first propose a base model with a heterogeneous block-diagonal structure regularizer to leverage the collaborative grouping of features and tasks. As a practical extension, we extend the base model by allowing overlapping features and differentiating the hard tasks.
  arXiv  Detail & Related papers  (2020-04-29T02:32:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.