Self-Supervised Learning-Based Multimodal Prediction on Prosocial Behavior Intentions

• URL: http://arxiv.org/abs/2507.08238v1
• Date: Fri, 11 Jul 2025 00:49:46 GMT
• Title: Self-Supervised Learning-Based Multimodal Prediction on Prosocial Behavior Intentions
• Abstract summary: There are no large, labeled datasets available for prosocial behavior.<n>Small-scale datasets make it difficult to train deep-learning models effectively.<n>We propose a self-supervised learning approach that harnesses multi-modal data.
• Score: 6.782784535456252
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Human state detection and behavior prediction have seen significant advancements with the rise of machine learning and multimodal sensing technologies. However, predicting prosocial behavior intentions in mobility scenarios, such as helping others on the road, is an underexplored area. Current research faces a major limitation. There are no large, labeled datasets available for prosocial behavior, and small-scale datasets make it difficult to train deep-learning models effectively. To overcome this, we propose a self-supervised learning approach that harnesses multi-modal data from existing physiological and behavioral datasets. By pre-training our model on diverse tasks and fine-tuning it with a smaller, manually labeled prosocial behavior dataset, we significantly enhance its performance. This method addresses the data scarcity issue, providing a more effective benchmark for prosocial behavior prediction, and offering valuable insights for improving intelligent vehicle systems and human-machine interaction.

Related papers

• Trajectory Learning with Graph Representations for Social Robot Navigation [1.0732935873226022]
  We propose an imitation learning framework that leverages social dynamics for socially compliant navigation.<n>We present a navigation module that captures temporal dynamics and mitigates error accumulations.<n>Our framework outperforms established data-driven baselines on simulation and a real-world dataset across diverse social metrics.
  arXiv  Detail & Related papers  (2026-06-21T23:57:54Z)
• HumanLLM: Towards Personalized Understanding and Simulation of Human Nature [72.55730315685837]
  HumanLLM is a foundation model designed for personalized understanding and simulation of individuals.<n>We first construct the Cognitive Genome, a large-scale corpus curated from real-world user data on platforms like Reddit, Twitter, Blogger, and Amazon.<n>We then formulate diverse learning tasks and perform supervised fine-tuning to empower the model to predict a wide range of individualized human behaviors, thoughts, and experiences.
  arXiv  Detail & Related papers  (2026-01-22T09:27:27Z)
• Few-Shot Inference of Human Perceptions of Robot Performance in Social Navigation Scenarios [1.5415050466360671]
  We propose leveraging the few-shot learning capabilities of Large Language Models to improve how well a robot can predict a user's perception of its performance.<n>This work paves the path to improving robot behavior in a scalable manner through user-centered feedback.
  arXiv  Detail & Related papers  (2025-12-17T23:06:36Z)
• MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving [7.41761409055788]
  MPCFormer is an explainable socially-aware autonomous driving approach with physics-informed and data-driven coupled social interaction dynamics.<n>We show that MPCFormer achieves the highest planning success rate of 94.67%, improves driving efficiency by 15.75%, and reduces the collision rate from 21.25% to 0.5%.
  arXiv  Detail & Related papers  (2025-12-03T13:43:33Z)
• Human Behavior Atlas: Benchmarking Unified Psychological and Social Behavior Understanding [50.34821397821815]
  Human Behavior Atlas comprises over 100,000 samples spanning text, audio, and visual modalities.<n>Our unification efforts can reduce redundancy and cost, enable training to scale efficiently across tasks, and enhance generalization of behavioral features across domains.
  arXiv  Detail & Related papers  (2025-10-06T15:16:45Z)
• Multi-Transmotion: Pre-trained Model for Human Motion Prediction [68.87010221355223]
  Multi-Transmotion is an innovative transformer-based model designed for cross-modality pre-training. Our methodology demonstrates competitive performance across various datasets on several downstream tasks.
  arXiv  Detail & Related papers  (2024-11-04T23:15:21Z)
• SmartPretrain: Model-Agnostic and Dataset-Agnostic Representation Learning for Motion Prediction [37.461695201579914]
  We propose SmartPretrain, a general and scalable framework for motion prediction.<n>Our approach integrates contrastive and reconstructive SSL, leveraging the strengths of both generative and discriminative paradigms.<n>SmartPretrain consistently improves the performance of state-of-the-art prediction models across datasets, data splits and main metrics.
  arXiv  Detail & Related papers  (2024-10-11T09:52:26Z)
• 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)
• Learning Human Action Recognition Representations Without Real Humans [66.61527869763819]
  We present a benchmark that leverages real-world videos with humans removed and synthetic data containing virtual humans to pre-train a model. We then evaluate the transferability of the representation learned on this data to a diverse set of downstream action recognition benchmarks. Our approach outperforms previous baselines by up to 5%.
  arXiv  Detail & Related papers  (2023-11-10T18:38:14Z)
• SACSoN: Scalable Autonomous Control for Social Navigation [62.59274275261392]
  We develop methods for training policies for socially unobtrusive navigation. By minimizing this counterfactual perturbation, we can induce robots to behave in ways that do not alter the natural behavior of humans in the shared space. We collect a large dataset where an indoor mobile robot interacts with human bystanders.
  arXiv  Detail & Related papers  (2023-06-02T19:07:52Z)
• Incorporating Heterogeneous User Behaviors and Social Influences for Predictive Analysis [32.31161268928372]
  We aim to incorporate heterogeneous user behaviors and social influences for behavior predictions. This paper proposes a variant of Long-Short Term Memory (LSTM) which can consider context while a behavior sequence. A residual learning-based decoder is designed to automatically construct multiple high-order cross features based on social behavior representation.
  arXiv  Detail & Related papers  (2022-07-24T17:05:37Z)
• What Matters in Learning from Offline Human Demonstrations for Robot Manipulation [64.43440450794495]
  We conduct an extensive study of six offline learning algorithms for robot manipulation. Our study analyzes the most critical challenges when learning from offline human data. We highlight opportunities for learning from human datasets.
  arXiv  Detail & Related papers  (2021-08-06T20:48:30Z)
• 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)
• Knowledge Distillation for Action Anticipation via Label Smoothing [21.457069042129138]
  Human capability to anticipate near future from visual observations and non-verbal cues is essential for developing intelligent systems. We implement a multi-modal framework based on long short-term memory (LSTM) networks to summarize past observations and make predictions at different time steps. Experiments show that label smoothing systematically improves performance of state-of-the-art models for action anticipation.
  arXiv  Detail & Related papers  (2020-04-16T15:38:53Z)
• Learning Predictive Models From Observation and Interaction [137.77887825854768]
  Learning predictive models from interaction with the world allows an agent, such as a robot, to learn about how the world works. However, learning a model that captures the dynamics of complex skills represents a major challenge. We propose a method to augment the training set with observational data of other agents, such as humans.
  arXiv  Detail & Related papers  (2019-12-30T01:10:41Z)

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.