Robust Unsupervised Learning of Temporal Dynamic Interactions

• URL: http://arxiv.org/abs/2006.10241v1
• Date: Thu, 18 Jun 2020 02:39:45 GMT
• Title: Robust Unsupervised Learning of Temporal Dynamic Interactions
• Authors: Aritra Guha, Rayleigh Lei, Jiacheng Zhu, XuanLong Nguyen and Ding Zhao
• Abstract summary: In this paper we introduce a model-free metric based on the Procrustes distance for robust representation learning of interactions. We also introduce an optimal transport based distance metric for comparing between distributions of interaction primitives. Their usefulness will be demonstrated in unsupervised learning of vehicle-to-vechicle interactions extracted from the Safety Pilot database.
• Score: 21.928675010305543
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robust representation learning of temporal dynamic interactions is an important problem in robotic learning in general and automated unsupervised learning in particular. Temporal dynamic interactions can be described by (multiple) geometric trajectories in a suitable space over which unsupervised learning techniques may be applied to extract useful features from raw and high-dimensional data measurements. Taking a geometric approach to robust representation learning for temporal dynamic interactions, it is necessary to develop suitable metrics and a systematic methodology for comparison and for assessing the stability of an unsupervised learning method with respect to its tuning parameters. Such metrics must account for the (geometric) constraints in the physical world as well as the uncertainty associated with the learned patterns. In this paper we introduce a model-free metric based on the Procrustes distance for robust representation learning of interactions, and an optimal transport based distance metric for comparing between distributions of interaction primitives. These distance metrics can serve as an objective for assessing the stability of an interaction learning algorithm. They are also used for comparing the outcomes produced by different algorithms. Moreover, they may also be adopted as an objective function to obtain clusters and representative interaction primitives. These concepts and techniques will be introduced, along with mathematical properties, while their usefulness will be demonstrated in unsupervised learning of vehicle-to-vechicle interactions extracted from the Safety Pilot database, the world's largest database for connected vehicles.

Related papers

• Visual-Geometric Collaborative Guidance for Affordance Learning [63.038406948791454]
  We propose a visual-geometric collaborative guided affordance learning network that incorporates visual and geometric cues. Our method outperforms the representative models regarding objective metrics and visual quality.
  arXiv  Detail & Related papers  (2024-10-15T07:35:51Z)
• Learning Manipulation by Predicting Interaction [85.57297574510507]
  We propose a general pre-training pipeline that learns Manipulation by Predicting the Interaction. The experimental results demonstrate that MPI exhibits remarkable improvement by 10% to 64% compared with previous state-of-the-art in real-world robot platforms.
  arXiv  Detail & Related papers  (2024-06-01T13:28:31Z)
• Multi-Agent Dynamic Relational Reasoning for Social Robot Navigation [50.01551945190676]
  Social robot navigation can be helpful in various contexts of daily life but requires safe human-robot interactions and efficient trajectory planning. We propose a systematic relational reasoning approach with explicit inference of the underlying dynamically evolving relational structures. We demonstrate its effectiveness for multi-agent trajectory prediction and social robot navigation.
  arXiv  Detail & Related papers  (2024-01-22T18:58:22Z)
• Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation [58.21683603243387]
  We propose three auxiliary tasks with relational-temporal reasoning and integrate them into the standard Deep Learning framework. These auxiliary tasks provide additional supervision signals to infer the behavior patterns other interactive agents. Our approach achieves robust and state-of-the-art performance in terms of standard evaluation metrics.
  arXiv  Detail & Related papers  (2023-11-27T18:57:42Z)
• Symphony in the Latent Space: Provably Integrating High-dimensional Techniques with Non-linear Machine Learning Models [19.824998167546298]
  This paper revisits building machine learning algorithms that involve interactions between entities. We show that it is possible to decouple the learning of high-dimensional interactions from the learning of non-linear feature interactions.
  arXiv  Detail & Related papers  (2022-12-01T20:18:26Z)
• Sample Efficient Dynamics Learning for Symmetrical Legged Robots:Leveraging Physics Invariance and Geometric Symmetries [14.848950116410231]
  This paper proposes a novel approach for learning dynamics leveraging the symmetry in the underlying robotic system. Existing frameworks that represent all data in vector space fail to consider the structured information of the robot.
  arXiv  Detail & Related papers  (2022-10-13T19:57:46Z)
• Stabilizing Q-learning with Linear Architectures for Provably Efficient Learning [53.17258888552998]
  This work proposes an exploration variant of the basic $Q$-learning protocol with linear function approximation. We show that the performance of the algorithm degrades very gracefully under a novel and more permissive notion of approximation error.
  arXiv  Detail & Related papers  (2022-06-01T23:26:51Z)
• Distributed Bayesian Online Learning for Cooperative Manipulation [9.582645137247667]
  We propose a novel distributed learning framework for the exemplary task of cooperative manipulation using Bayesian principles. Using only local state information each agent obtains an estimate of the object dynamics and grasp kinematics. Each estimate of the object dynamics and grasp kinematics is accompanied by a measure of uncertainty, which allows to guarantee a bounded prediction error with high probability.
  arXiv  Detail & Related papers  (2021-04-09T13:03:09Z)
• Structured Prediction for CRiSP Inverse Kinematics Learning with Misspecified Robot Models [39.513301957826435]
  We introduce a structured prediction algorithm that combines a data-driven strategy with a forward kinematics function. The proposed approach ensures that predicted joint configurations are well within the robot's constraints.
  arXiv  Detail & Related papers  (2021-02-25T15:39:33Z)
• Human Trajectory Forecasting in Crowds: A Deep Learning Perspective [89.4600982169]
  We present an in-depth analysis of existing deep learning-based methods for modelling social interactions. We propose two knowledge-based data-driven methods to effectively capture these social interactions. We develop a large scale interaction-centric benchmark TrajNet++, a significant yet missing component in the field of human trajectory forecasting.
  arXiv  Detail & Related papers  (2020-07-07T17:19:56Z)

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.