A representational framework for learning and encoding structurally enriched trajectories in complex agent environments

• URL: http://arxiv.org/abs/2503.13194v1
• Date: Mon, 17 Mar 2025 14:04:27 GMT
• Title: A representational framework for learning and encoding structurally enriched trajectories in complex agent environments
• Authors: Corina Catarau-Cotutiu, Esther Mondragon, Eduardo Alonso,
• Abstract summary: The ability of artificial intelligence agents to make optimal decisions and generalise them to different domains and tasks is compromised in complex scenarios.<n>One way to address this issue has focused on learning efficient representations of the world and on how the actions of agents affect them, such as disentangled representations that exploit symmetries.<n>We propose to enrich the agent's ontology and extend the traditionalisation of trajectories to provide a more nuanced view of task execution.
• Score: 1.904851064759821
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The ability of artificial intelligence agents to make optimal decisions and generalise them to different domains and tasks is compromised in complex scenarios. One way to address this issue has focused on learning efficient representations of the world and on how the actions of agents affect them, such as disentangled representations that exploit symmetries. Whereas such representations are procedurally efficient, they are based on the compression of low-level state-action transitions, which lack structural richness. To address this problem, we propose to enrich the agent's ontology and extend the traditional conceptualisation of trajectories to provide a more nuanced view of task execution. Structurally Enriched Trajectories (SETs) extend the encoding of sequences of states and their transitions by incorporating hierarchical relations between objects, interactions and affordances. SETs are built as multi-level graphs, providing a detailed representation of the agent dynamics and a transferable functional abstraction of the task. SETs are integrated into an architecture, Structurally Enriched Trajectory Learning and Encoding (SETLE), that employs a heterogeneous graph-based memory structure of multi-level relational dependencies essential for generalisation. Using reinforcement learning as a data generation tool, we demonstrate that SETLE can support downstream tasks, enabling agents to recognise task-relevant structural patterns across diverse environments.

Related papers

• Autonomous Structural Memory Manipulation for Large Language Models Using Hierarchical Embedding Augmentation [0.0]
  This study introduces hierarchical embedding augmentation as a means to redefine the representation of tokens through multi-level semantic structures.<n>Results reveal substantial improvements in computational efficiency, with marked reductions in processing overhead for longer input sequences.<n>The ability to dynamically adjust token representations and memory configurations contributed to the model's robustness under varied and unpredictable input conditions.
  arXiv  Detail & Related papers  (2025-01-23T22:20:36Z)
• Interpretable deformable image registration: A geometric deep learning perspective [9.13809412085203]
  We present a theoretical foundation for designing an interpretable registration framework.<n>We formulate an end-to-end process that refines transformations in a coarse-to-fine fashion.<n>We conclude by showing significant improvement in performance metrics over state-of-the-art approaches.
  arXiv  Detail & Related papers  (2024-12-17T19:47:10Z)
• Flex: End-to-End Text-Instructed Visual Navigation with Foundation Models [59.892436892964376]
  We investigate the minimal data requirements and architectural adaptations necessary to achieve robust closed-loop performance with vision-based control policies. Our findings are synthesized in Flex (Fly-lexically), a framework that uses pre-trained Vision Language Models (VLMs) as frozen patch-wise feature extractors. We demonstrate the effectiveness of this approach on quadrotor fly-to-target tasks, where agents trained via behavior cloning successfully generalize to real-world scenes.
  arXiv  Detail & Related papers  (2024-10-16T19:59:31Z)
• Learning Multi-Agent Communication from Graph Modeling Perspective [62.13508281188895]
  We introduce a novel approach wherein we conceptualize the communication architecture among agents as a learnable graph. Our proposed approach, CommFormer, efficiently optimize the communication graph and concurrently refines architectural parameters through gradient descent in an end-to-end manner.
  arXiv  Detail & Related papers  (2024-05-14T12:40:25Z)
• Structural Concept Learning via Graph Attention for Multi-Level Rearrangement Planning [2.7195102129095003]
  We propose a deep learning approach to perform multi-level object rearrangement planning for scenes with structural dependency hierarchies. It is trained on a self-generated simulation data set with intuitive structures and works for unseen scenes with an arbitrary number of objects. We compare our method with a range of classical and model-based baselines to show that our method leverages its scene understanding to achieve better performance, flexibility, and efficiency.
  arXiv  Detail & Related papers  (2023-09-05T19:35:44Z)
• Structure-CLIP: Towards Scene Graph Knowledge to Enhance Multi-modal Structured Representations [70.41385310930846]
  We present an end-to-end framework Structure-CLIP to enhance multi-modal structured representations. We use scene graphs to guide the construction of semantic negative examples, which results in an increased emphasis on learning structured representations. A Knowledge-Enhance (KEE) is proposed to leverage SGK as input to further enhance structured representations.
  arXiv  Detail & Related papers  (2023-05-06T03:57:05Z)
• Neural Constraint Satisfaction: Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement [75.9289887536165]
  We present a hierarchical abstraction approach to uncover underlying entities. We show how to learn a correspondence between intervening on states of entities in the agent's model and acting on objects in the environment. We use this correspondence to develop a method for control that generalizes to different numbers and configurations of objects.
  arXiv  Detail & Related papers  (2023-03-20T18:19:36Z)
• A System for Morphology-Task Generalization via Unified Representation and Behavior Distillation [28.041319351752485]
  In this work, we explore a method for learning a single policy that manipulates various forms of agents to solve various tasks by distilling a large amount of proficient behavioral data. We introduce morphology-task graph, which treats observations, actions and goals/task in a unified graph representation. We also develop MxT-Bench for fast large-scale behavior generation, which supports procedural generation of diverse morphology-task combinations.
  arXiv  Detail & Related papers  (2022-11-25T18:52:48Z)
• Fast Inference and Transfer of Compositional Task Structures for Few-shot Task Generalization [101.72755769194677]
  We formulate it as a few-shot reinforcement learning problem where a task is characterized by a subtask graph. Our multi-task subtask graph inferencer (MTSGI) first infers the common high-level task structure in terms of the subtask graph from the training tasks. Our experiment results on 2D grid-world and complex web navigation domains show that the proposed method can learn and leverage the common underlying structure of the tasks for faster adaptation to the unseen tasks.
  arXiv  Detail & Related papers  (2022-05-25T10:44:25Z)
• Structure-Regularized Attention for Deformable Object Representation [17.120035855774344]
  Capturing contextual dependencies has proven useful to improve the representational power of deep neural networks. Recent approaches that focus on modeling global context, such as self-attention and non-local operation, achieve this goal by enabling unconstrained pairwise interactions between elements. We consider learning representations for deformable objects which can benefit from context exploitation by modeling the structural dependencies that the data intrinsically possesses.
  arXiv  Detail & Related papers  (2021-06-12T03:10:17Z)
• MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and Architectures [61.73533544385352]
  We propose a transferable perturbation, MetaPerturb, which is meta-learned to improve generalization performance on unseen data. As MetaPerturb is a set-function trained over diverse distributions across layers and tasks, it can generalize heterogeneous tasks and architectures.
  arXiv  Detail & Related papers  (2020-06-13T02:54:59Z)

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.