COLSON: Controllable Learning-Based Social Navigation via Diffusion-Based Reinforcement Learning

• URL: http://arxiv.org/abs/2503.13934v1
• Date: Tue, 18 Mar 2025 06:02:30 GMT
• Title: COLSON: Controllable Learning-Based Social Navigation via Diffusion-Based Reinforcement Learning
• Abstract summary: Mobile robot navigation in dynamic environments with pedestrian traffic is a key challenge in the development of autonomous mobile service robots.<n>Deep reinforcement learning-based methods have outperformed traditional rule-based approaches owing to their optimization capabilities.<n>In this study, we apply a diffusion-based reinforcement learning approach to social navigation and validated its effectiveness.
• Score: 0.7499722271664144
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mobile robot navigation in dynamic environments with pedestrian traffic is a key challenge in the development of autonomous mobile service robots. Recently, deep reinforcement learning-based methods have been actively studied and have outperformed traditional rule-based approaches owing to their optimization capabilities. Among these, methods that assume a continuous action space typically rely on a Gaussian distribution assumption, which limits the flexibility of generated actions. Meanwhile, the application of diffusion models to reinforcement learning has advanced, allowing for more flexible action distributions compared with Gaussian distribution-based approaches. In this study, we applied a diffusion-based reinforcement learning approach to social navigation and validated its effectiveness. Furthermore, by leveraging the characteristics of diffusion models, we propose an extension that enables post-training action smoothing and adaptation to static obstacle scenarios not considered during the training steps.

Related papers

• Distributional Active Inference [11.60734837821471]
  We present a formal abstraction of reinforcement learning algorithms that spans model-based, distributional, and model-free approaches.<n>This abstraction seamlessly integrates active inference into the distributional reinforcement learning framework, making its performance advantages accessible without transition dynamics modeling.
  arXiv  Detail & Related papers  (2026-01-28T19:36:33Z)
• On-the-Fly VLA Adaptation via Test-Time Reinforcement Learning [61.38273866396522]
  Vision-Language-Action models have emerged as a powerful paradigm for general-purpose robot learning.<n>Current methods remain unsuitable for challenging simulated- or physical-world deployments.<n>We introduce a Test-Time Reinforcement Learning for VLAs framework that enables on-the-fly policy adaptation during inference.
  arXiv  Detail & Related papers  (2026-01-11T01:51:30Z)
• Discrete Diffusion for Reflective Vision-Language-Action Models in Autonomous Driving [55.13109926181247]
  We introduce ReflectDrive, a learning-based framework that integrates a reflection mechanism for safe trajectory generation via discrete diffusion.<n>Central to our approach is a safety-aware reflection mechanism that performs iterative self-correction without gradient.<n>Our method begins with goal-conditioned trajectory generation to model multi-modal driving behaviors.
  arXiv  Detail & Related papers  (2025-09-24T13:35:15Z)
• Breaking Imitation Bottlenecks: Reinforced Diffusion Powers Diverse Trajectory Generation [20.106116218594266]
  DIVER is an end-to-end autonomous driving framework that integrates reinforcement learning and diffusion-based generation.<n>We show that DIVER significantly improves trajectory diversity, effectively addressing the mode collapse problem inherent in imitation learning.
  arXiv  Detail & Related papers  (2025-07-05T14:19:19Z)
• Prior-Guided Diffusion Planning for Offline Reinforcement Learning [4.760537994346813]
  Prior Guidance (PG) is a novel guided sampling framework that replaces the standard Gaussian prior-of-cloned diffusion model.<n>PG directly generates high-value trajectories without costly reward optimization of the diffusion model itself.<n>We present an efficient training strategy that applies behavior regularization in latent space, and empirically demonstrate that PG outperforms state-the-art diffusion policies and planners across diverse long-horizon offline RL benchmarks.
  arXiv  Detail & Related papers  (2025-05-16T05:39:02Z)
• Latent Diffusion Planning for Imitation Learning [78.56207566743154]
  Latent Diffusion Planning (LDP) is a modular approach consisting of a planner and inverse dynamics model. By separating planning from action prediction, LDP can benefit from the denser supervision signals of suboptimal and action-free data. On simulated visual robotic manipulation tasks, LDP outperforms state-of-the-art imitation learning approaches.
  arXiv  Detail & Related papers  (2025-04-23T17:53:34Z)
• Diffusion Predictive Control with Constraints [51.91057765703533]
  Diffusion predictive control with constraints (DPCC)<n>An algorithm for diffusion-based control with explicit state and action constraints that can deviate from those in the training data.<n>We show through simulations of a robot manipulator that DPCC outperforms existing methods in satisfying novel test-time constraints while maintaining performance on the learned control task.
  arXiv  Detail & Related papers  (2024-12-12T15:10:22Z)
• Towards Human-Like Driving: Active Inference in Autonomous Vehicle Control [0.5437298646956507]
  This paper presents a novel approach to Autonomous Vehicle (AV) control through the application of active inference. Active inference is a theory derived from neuroscience that conceptualizes the brain as a predictive machine. Our method integrates active inference with deep learning to manage lateral control in AVs, enabling them to perform lane following maneuvers within a simulated urban environment.
  arXiv  Detail & Related papers  (2024-07-10T14:08:27Z)
• Diffusion-Reinforcement Learning Hierarchical Motion Planning in Adversarial Multi-agent Games [6.532258098619471]
  We focus on a motion planning task for an evasive target in a partially observable multi-agent adversarial pursuit-evasion games (PEG) These pursuit-evasion problems are relevant to various applications, such as search and rescue operations and surveillance robots. We propose a hierarchical architecture that integrates a high-level diffusion model to plan global paths responsive to environment data.
  arXiv  Detail & Related papers  (2024-03-16T03:53:55Z)
• 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 a Diffusion Model Policy from Rewards via Q-Score Matching [93.0191910132874]
  We present a theoretical framework linking the structure of diffusion model policies to a learned Q-function.<n>We propose a new policy update method from this theory, which we denote Q-score matching.
  arXiv  Detail & Related papers  (2023-12-18T23:31:01Z)
• Score Regularized Policy Optimization through Diffusion Behavior [25.926641622408752]
  Recent developments in offline reinforcement learning have uncovered the immense potential of diffusion modeling. We propose to extract an efficient deterministic inference policy from critic models and pretrained diffusion behavior models. Our method boosts action sampling speed by more than 25 times compared with various leading diffusion-based methods in locomotion tasks.
  arXiv  Detail & Related papers  (2023-10-11T08:31:26Z)
• Distributionally Robust Model-based Reinforcement Learning with Large State Spaces [55.14361269378122]
  Three major challenges in reinforcement learning are the complex dynamical systems with large state spaces, the costly data acquisition processes, and the deviation of real-world dynamics from the training environment deployment. We study distributionally robust Markov decision processes with continuous state spaces under the widely used Kullback-Leibler, chi-square, and total variation uncertainty sets. We propose a model-based approach that utilizes Gaussian Processes and the maximum variance reduction algorithm to efficiently learn multi-output nominal transition dynamics.
  arXiv  Detail & Related papers  (2023-09-05T13:42:11Z)
• Crossway Diffusion: Improving Diffusion-based Visuomotor Policy via Self-supervised Learning [42.009856923352864]
  diffusion models have been adopted for behavioral cloning in a sequence modeling fashion. We propose Crossway Diffusion, a simple yet effective method to enhance diffusion-based visuomotor policy learning. Our experiments demonstrate the effectiveness of Crossway Diffusion in various simulated and real-world robot tasks.
  arXiv  Detail & Related papers  (2023-07-04T17:59:29Z)
• Learning Representative Trajectories of Dynamical Systems via Domain-Adaptive Imitation [0.0]
  We propose DATI, a deep reinforcement learning agent designed for domain-adaptive trajectory imitation. Our experiments show that DATI outperforms baseline methods for imitation learning and optimal control in this setting. Its generalization to a real-world scenario is shown through the discovery of abnormal motion patterns in maritime traffic.
  arXiv  Detail & Related papers  (2023-04-19T15:53:48Z)
• Safe Multi-agent Learning via Trapping Regions [89.24858306636816]
  We apply the concept of trapping regions, known from qualitative theory of dynamical systems, to create safety sets in the joint strategy space for decentralized learning. We propose a binary partitioning algorithm for verification that candidate sets form trapping regions in systems with known learning dynamics, and a sampling algorithm for scenarios where learning dynamics are not known.
  arXiv  Detail & Related papers  (2023-02-27T14:47:52Z)
• Generative Adversarial Reward Learning for Generalized Behavior Tendency Inference [71.11416263370823]
  We propose a generative inverse reinforcement learning for user behavioral preference modelling. Our model can automatically learn the rewards from user's actions based on discriminative actor-critic network and Wasserstein GAN.
  arXiv  Detail & Related papers  (2021-05-03T13:14:25Z)

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.