On Learning Informative Trajectory Embeddings for Imitation, Classification and Regression

• URL: http://arxiv.org/abs/2501.09327v2
• Date: Fri, 17 Jan 2025 18:30:04 GMT
• Title: On Learning Informative Trajectory Embeddings for Imitation, Classification and Regression
• Authors: Zichang Ge, Changyu Chen, Arunesh Sinha, Pradeep Varakantham,
• Abstract summary: In real-world sequential decision making tasks, learning from observed state-action trajectories is critical for tasks like imitation, classification, and clustering. We propose a novel method for embedding state-action trajectories into a latent space that captures the skills and competencies in the dynamic underlying decision-making processes.
• Score: 19.01804572722833
• License:
• Abstract: In real-world sequential decision making tasks like autonomous driving, robotics, and healthcare, learning from observed state-action trajectories is critical for tasks like imitation, classification, and clustering. For example, self-driving cars must replicate human driving behaviors, while robots and healthcare systems benefit from modeling decision sequences, whether or not they come from expert data. Existing trajectory encoding methods often focus on specific tasks or rely on reward signals, limiting their ability to generalize across domains and tasks. Inspired by the success of embedding models like CLIP and BERT in static domains, we propose a novel method for embedding state-action trajectories into a latent space that captures the skills and competencies in the dynamic underlying decision-making processes. This method operates without the need for reward labels, enabling better generalization across diverse domains and tasks. Our contributions are threefold: (1) We introduce a trajectory embedding approach that captures multiple abilities from state-action data. (2) The learned embeddings exhibit strong representational power across downstream tasks, including imitation, classification, clustering, and regression. (3) The embeddings demonstrate unique properties, such as controlling agent behaviors in IQ-Learn and an additive structure in the latent space. Experimental results confirm that our method outperforms traditional approaches, offering more flexible and powerful trajectory representations for various applications. Our code is available at https://github.com/Erasmo1015/vte.

Related papers

• Conditional Neural Expert Processes for Learning Movement Primitives from Demonstration [1.9336815376402723]
  Conditional Neural Expert Processes (CNEP) learns to assign demonstrations from different modes to distinct expert networks. CNEP does not require supervision on which mode the trajectories belong to. Our system is capable of on-the-fly adaptation to environmental changes via an online conditioning mechanism.
  arXiv  Detail & Related papers  (2024-02-13T12:52:02Z)
• Unsupervised 3D registration through optimization-guided cyclical self-training [71.75057371518093]
  State-of-the-art deep learning-based registration methods employ three different learning strategies. We propose a novel self-supervised learning paradigm for unsupervised registration, relying on self-training. We evaluate the method for abdomen and lung registration, consistently surpassing metric-based supervision and outperforming diverse state-of-the-art competitors.
  arXiv  Detail & Related papers  (2023-06-29T14:54:10Z)
• Self-Supervised Reinforcement Learning that Transfers using Random Features [41.00256493388967]
  We propose a self-supervised reinforcement learning method that enables the transfer of behaviors across tasks with different rewards. Our method is self-supervised in that it can be trained on offline datasets without reward labels, but can then be quickly deployed on new tasks.
  arXiv  Detail & Related papers  (2023-05-26T20:37:06Z)
• Unsupervised Self-Driving Attention Prediction via Uncertainty Mining and Knowledge Embedding [51.8579160500354]
  We propose an unsupervised way to predict self-driving attention by uncertainty modeling and driving knowledge integration. Results show equivalent or even more impressive performance compared to fully-supervised state-of-the-art approaches.
  arXiv  Detail & Related papers  (2023-03-17T00:28:33Z)
• Learning to Walk Autonomously via Reset-Free Quality-Diversity [73.08073762433376]
  Quality-Diversity algorithms can discover large and complex behavioural repertoires consisting of both diverse and high-performing skills. Existing QD algorithms need large numbers of evaluations as well as episodic resets, which require manual human supervision and interventions. This paper proposes Reset-Free Quality-Diversity optimization (RF-QD) as a step towards autonomous learning for robotics in open-ended environments.
  arXiv  Detail & Related papers  (2022-04-07T14:07:51Z)
• Learning Transferable Motor Skills with Hierarchical Latent Mixture Policies [37.09286945259353]
  We propose an approach to learn abstract motor skills from data using a hierarchical mixture latent variable model. We demonstrate in manipulation domains that the method can effectively cluster offline data into distinct, executable behaviours.
  arXiv  Detail & Related papers  (2021-12-09T17:37:14Z)
• Towards Optimal Strategies for Training Self-Driving Perception Models in Simulation [98.51313127382937]
  We focus on the use of labels in the synthetic domain alone. Our approach introduces both a way to learn neural-invariant representations and a theoretically inspired view on how to sample the data from the simulator. We showcase our approach on the bird's-eye-view vehicle segmentation task with multi-sensor data.
  arXiv  Detail & Related papers  (2021-11-15T18:37:43Z)
• IQ-Learn: Inverse soft-Q Learning for Imitation [95.06031307730245]
  imitation learning from a small amount of expert data can be challenging in high-dimensional environments with complex dynamics. Behavioral cloning is a simple method that is widely used due to its simplicity of implementation and stable convergence. We introduce a method for dynamics-aware IL which avoids adversarial training by learning a single Q-function.
  arXiv  Detail & Related papers  (2021-06-23T03:43:10Z)
• Domain-Robust Visual Imitation Learning with Mutual Information Constraints [0.0]
  We introduce a new algorithm called Disentangling Generative Adversarial Imitation Learning (DisentanGAIL) Our algorithm enables autonomous agents to learn directly from high dimensional observations of an expert performing a task.
  arXiv  Detail & Related papers  (2021-03-08T21:18:58Z)
• Diverse Complexity Measures for Dataset Curation in Self-driving [80.55417232642124]
  We propose a new data selection method that exploits a diverse set of criteria that quantize interestingness of traffic scenes. Our experiments show that the proposed curation pipeline is able to select datasets that lead to better generalization and higher performance.
  arXiv  Detail & Related papers  (2021-01-16T23:45:02Z)
• Behaviorally Diverse Traffic Simulation via Reinforcement Learning [16.99423598448411]
  This paper introduces an easily-tunable policy generation algorithm for autonomous driving agents. The proposed algorithm balances diversity and driving skills by leveraging the representation and exploration abilities of deep reinforcement learning. We experimentally show the effectiveness of our methods on several challenging intersection scenes.
  arXiv  Detail & Related papers  (2020-11-11T12:49:11Z)

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.