Learning from Imperfect Demonstrations via Temporal Behavior Tree-Guided Trajectory Repair

• URL: http://arxiv.org/abs/2604.04225v1
• Date: Sun, 05 Apr 2026 18:49:45 GMT
• Title: Learning from Imperfect Demonstrations via Temporal Behavior Tree-Guided Trajectory Repair
• Abstract summary: Learning robot control policies from demonstrations is a powerful paradigm, yet real-world data is often suboptimal, noisy, or otherwise imperfect.<n>We present a formal framework that leverages Temporal Behavior Trees (TBT) to repair suboptimal trajectories prior to their use in downstream policy learning.<n>We demonstrate the effectiveness of this framework on discrete grid-world navigation and continuous single and multi-agent reach-avoid tasks.
• Score: 5.4872967954342675
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning robot control policies from demonstrations is a powerful paradigm, yet real-world data is often suboptimal, noisy, or otherwise imperfect, posing significant challenges for imitation and reinforcement learning. In this work, we present a formal framework that leverages Temporal Behavior Trees (TBT), an extension of Signal Temporal Logic (STL) with Behavior Tree semantics, to repair suboptimal trajectories prior to their use in downstream policy learning. Given demonstrations that violate a TBT specification, a model-based repair algorithm corrects trajectory segments to satisfy the formal constraints, yielding a dataset that is both logically consistent and interpretable. The repaired trajectories are then used to extract potential functions that shape the reward signal for reinforcement learning, guiding the agent toward task-consistent regions of the state space without requiring knowledge of the agent's kinematic model. We demonstrate the effectiveness of this framework on discrete grid-world navigation and continuous single and multi-agent reach-avoid tasks, highlighting its potential for data-efficient robot learning in settings where high-quality demonstrations cannot be assumed.

Related papers

• Transferable Delay-Aware Reinforcement Learning via Implicit Causal Graph Modeling [11.943568201005931]
  In cross-task scenarios, changes in task objectives and reward formulations reduce the reusability of previously acquired task knowledge.<n>This paper proposes a transferable delay-aware reinforcement learning method based on implicit causal graph modeling.
  arXiv  Detail & Related papers  (2026-05-12T15:58:58Z)
• Build on Priors: Vision--Language--Guided Neuro-Symbolic Imitation Learning for Data-Efficient Real-World Robot Manipulation [4.118262876469644]
  We present a scalable neuro-symbolic framework that autonomously constructs symbolic planning domains and data-efficient control policies.<n>Our method segments demonstrations into skills and employs a Vision-Language Model (VLM) to classify skills.<n>Known controllers can be leveraged for real-world data augmentation by projecting a single demonstration onto other objects in the scene.
  arXiv  Detail & Related papers  (2026-04-04T15:17:59Z)
• Self-adapting Robotic Agents through Online Continual Reinforcement Learning with World Model Feedback [2.165723322157105]
  This work presents a framework for online Continual Reinforcement Learning that enables automated adaptation during deployment.<n>The proposed method leverages world model prediction residuals to detect out-of-distribution events and automatically trigger finetuning.<n>The approach is validated on a variety of contemporary continuous control problems, including a quadruped robot in high-fidelity simulation.
  arXiv  Detail & Related papers  (2026-03-04T13:07:42Z)
• Going with the Flow: Koopman Behavioral Models as Implicit Planners for Visuo-Motor Dexterity [1.6865201067073452]
  We introduce Unified Behavioral Models (UBMs), a framework that learns to represent dexterous skills as coupled dynamical systems.<n>Koopman-UBM is a first instantiation of UBMs that learns a unified representation in which the joint flow of latent visual and proprioceptive features is governed by a structured linear system.
  arXiv  Detail & Related papers  (2026-02-07T07:18:00Z)
• From Physics to Machine Learning and Back: Part II - Learning and Observational Bias in PHM [52.64097278841485]
  Review examines how incorporating learning and observational biases through physics-informed modeling and data strategies can guide models toward physically consistent and reliable predictions.<n>Fast adaptation methods including meta-learning and few-shot learning are reviewed alongside domain generalization techniques.
  arXiv  Detail & Related papers  (2025-09-25T14:15:43Z)
• Dexplore: Scalable Neural Control for Dexterous Manipulation from Reference-Scoped Exploration [58.4036440289082]
  Hand-object motion-capture (MoCap) offer large-scale, contact-rich demonstrations and hold promise for dexterous robotic scopes.<n>We introduce Dexplore, a unified single-loop optimization that performs repositories and tracking to learn robot control policies directly from MoCap at scale.
  arXiv  Detail & Related papers  (2025-09-11T17:59:07Z)
• Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition [63.55828203989405]
  We introduce a novel Topology-Aware Modeling (TAM) framework for Sim2Real UDA on object point clouds.<n>Our approach mitigates the domain gap by leveraging global spatial topology, characterized by low-level, high-frequency 3D structures.<n>We propose an advanced self-training strategy that combines cross-domain contrastive learning with self-training.
  arXiv  Detail & Related papers  (2025-06-26T11:53:59Z)
• SkillMimic-V2: Learning Robust and Generalizable Interaction Skills from Sparse and Noisy Demonstrations [68.9300049150948]
  We address a fundamental challenge in Reinforcement Learning from Interaction Demonstration (RLID)<n>Existing data collection approaches yield sparse, disconnected, and noisy trajectories that fail to capture the full spectrum of possible skill variations and transitions.<n>We present two data augmentation techniques: a Stitched Trajectory Graph (STG) that discovers potential transitions between demonstration skills, and a State Transition Field (STF) that establishes unique connections for arbitrary states within the demonstration neighborhood.
  arXiv  Detail & Related papers  (2025-05-04T13:00:29Z)
• 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)
• Unsupervised Representation Learning of Complex Time Series for Maneuverability State Identification in Smart Mobility [0.0]
  In smart mobility, MTS plays a crucial role in providing temporal dynamics of behaviors such as maneuver patterns. In this work, we aim to address challenges associated with modeling MTS data collected from a vehicle using sensors. Our goal is to investigate the effectiveness of two distinct unsupervised representation learning approaches in identifying maneuvering states in smart mobility.
  arXiv  Detail & Related papers  (2024-08-26T15:16:18Z)
• Dream to Explore: Adaptive Simulations for Autonomous Systems [3.0664963196464448]
  We tackle the problem of learning to control dynamical systems by applying Bayesian nonparametric methods. By employing Gaussian processes to discover latent world dynamics, we mitigate common data efficiency issues observed in reinforcement learning. Our algorithm jointly learns a world model and policy by optimizing a variational lower bound of a log-likelihood.
  arXiv  Detail & Related papers  (2021-10-27T04:27:28Z)
• Hierarchical Few-Shot Imitation with Skill Transition Models [66.81252581083199]
  Few-shot Imitation with Skill Transition Models (FIST) is an algorithm that extracts skills from offline data and utilizes them to generalize to unseen tasks. We show that FIST is capable of generalizing to new tasks and substantially outperforms prior baselines in navigation experiments.
  arXiv  Detail & Related papers  (2021-07-19T15:56:01Z)

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.