How to Mitigate the Distribution Shift Problem in Robotics Control: A Robust and Adaptive Approach Based on Offline to Online Imitation Learning

• URL: http://arxiv.org/abs/2605.25414v1
• Date: Mon, 25 May 2026 04:30:51 GMT
• Title: How to Mitigate the Distribution Shift Problem in Robotics Control: A Robust and Adaptive Approach Based on Offline to Online Imitation Learning
• Abstract summary: Distribution shift in imitation learning refers to the problem that the agent cannot plan proper actions for a state that has not been visited during the training.<n>This problem can be largely attributed to the inherently narrow state-action coverage provided by expert demonstrations over the full environment.<n>We propose a robust offline to adaptive online imitation learning framework that handles the distribution shift problem in a lifelong, multi-phase scheme.
• Score: 15.32457564038423
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Distribution shift in imitation learning refers to the problem that the agent cannot plan proper actions for a state that has not been visited during the training. This problem can be largely attributed to the inherently narrow state-action coverage provided by expert demonstrations over the full environment. In this paper, we propose a robust offline to adaptive online imitation learning framework that handles the distribution shift problem in a lifelong, multi-phase scheme. In the offline learning phase, we leverage supplementary demonstrations to broaden the state-action coverage of the policy by utilizing a discriminator to effectively train the policy with supplementary demonstrations, thereby enhancing the robustness of the policy to distribution shift. In the subsequent online inference phase, our framework detects the occurrence of distribution shift and conducts self-supervised imitation learning from online experiences to adapt the policy to the online environments. Through extensive evaluations in MuJoCo environments, we demonstrate that our method exhibits better robustness to distribution shift and better adaptation performance to online environments than the baseline algorithms, which indicates superior performance of our framework against the distribution shift.

Related papers

• Behavior-Invariant Task Representation Learning with Transformer-based World Models for Offline Meta-Reinforcement Learning [13.491848927208373]
  We propose a novel framework that integrates information-theoretic task representation learning with a Transformer-based world model.<n>Our method outperforms state-of-the-art approaches, with superior stability and under out-of-distribution and sparse-reward settings.
  arXiv  Detail & Related papers  (2026-05-30T15:53:03Z)
• OLIVIA: Online Learning via Inference-time Action Adaptation for Decision Making in LLM ReAct Agents [74.20327254615854]
  Large language model agents interleave reasoning, action selection, and observation to solve sequential decision-making tasks.<n>Existing inference-time adaptation methods for LLM agents mainly rely on prompting or retrieval.<n>We propose OLIVIA, an inference-time action adaptation framework for ReAct-style agents.
  arXiv  Detail & Related papers  (2026-05-11T19:28:20Z)
• Adversarial Fine-tuning in Offline-to-Online Reinforcement Learning for Robust Robot Control [12.961180148172199]
  This study introduces an offline-to-online framework that trains policies on clean data and then performs adversarial fine-tuning.<n>A performance-aware curriculum adjusts the perturbation probability during training via an exponential-moving-average signal.<n>Experiments on continuous-control locomotion tasks demonstrate that the proposed method consistently improves robustness over offline-only baselines.
  arXiv  Detail & Related papers  (2025-10-15T09:45:24Z)
• Robot Policy Transfer with Online Demonstrations: An Active Reinforcement Learning Approach [3.637365301757111]
  We present Policy Transfer with Online Demonstrations, an active Learning from Demonstration algorithm for policy transfer.<n>We evaluate our method in eight robotic scenarios, involving policy transfer across diverse environment characteristics.<n>Results show that our method significantly outperforms all baselines in terms of average success rate and sample efficiency.
  arXiv  Detail & Related papers  (2025-03-17T09:47:42Z)
• Distributionally Adaptive Meta Reinforcement Learning [85.17284589483536]
  We develop a framework for meta-RL algorithms that behave appropriately under test-time distribution shifts. Our framework centers on an adaptive approach to distributional robustness that trains a population of meta-policies to be robust to varying levels of distribution shift. We show how our framework allows for improved regret under distribution shift, and empirically show its efficacy on simulated robotics problems.
  arXiv  Detail & Related papers  (2022-10-06T17:55:09Z)
• Regularizing a Model-based Policy Stationary Distribution to Stabilize Offline Reinforcement Learning [62.19209005400561]
  offline reinforcement learning (RL) extends the paradigm of classical RL algorithms to purely learning from static datasets. A key challenge of offline RL is the instability of policy training, caused by the mismatch between the distribution of the offline data and the undiscounted stationary state-action distribution of the learned policy. We regularize the undiscounted stationary distribution of the current policy towards the offline data during the policy optimization process.
  arXiv  Detail & Related papers  (2022-06-14T20:56:16Z)
• Latent-Variable Advantage-Weighted Policy Optimization for Offline RL [70.01851346635637]
  offline reinforcement learning methods hold the promise of learning policies from pre-collected datasets without the need to query the environment for new transitions. In practice, offline datasets are often heterogeneous, i.e., collected in a variety of scenarios. We propose to leverage latent-variable policies that can represent a broader class of policy distributions. Our method improves the average performance of the next best-performing offline reinforcement learning methods by 49% on heterogeneous datasets.
  arXiv  Detail & Related papers  (2022-03-16T21:17:03Z)
• Variational Beam Search for Learning with Distribution Shifts [26.345665980534374]
  We propose a new Bayesian meta-algorithm that can both (i) make inferences about subtle distribution shifts based on minimal sequential observations and (ii) accordingly adapt a model in an online fashion. Our proposed approach is model-agnostic, applicable to both supervised and unsupervised learning, and yields significant improvements over state-of-the-art Bayesian online learning approaches.
  arXiv  Detail & Related papers  (2020-12-15T05:28:47Z)
• Strictly Batch Imitation Learning by Energy-based Distribution Matching [104.33286163090179]
  Consider learning a policy purely on the basis of demonstrated behavior -- that is, with no access to reinforcement signals, no knowledge of transition dynamics, and no further interaction with the environment. One solution is simply to retrofit existing algorithms for apprenticeship learning to work in the offline setting. But such an approach leans heavily on off-policy evaluation or offline model estimation, and can be indirect and inefficient. We argue that a good solution should be able to explicitly parameterize a policy, implicitly learn from rollout dynamics, and operate in an entirely offline fashion.
  arXiv  Detail & Related papers  (2020-06-25T03:27:59Z)
• Non-Stationary Off-Policy Optimization [50.41335279896062]
  We study the novel problem of off-policy optimization in piecewise-stationary contextual bandits. In the offline learning phase, we partition logged data into categorical latent states and learn a near-optimal sub-policy for each state. In the online deployment phase, we adaptively switch between the learned sub-policies based on their performance.
  arXiv  Detail & Related papers  (2020-06-15T09:16:09Z)

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.