Towards Intelligible Human-Robot Interaction: An Active Inference Approach to Occluded Pedestrian Scenarios

• URL: http://arxiv.org/abs/2602.23109v2
• Date: Fri, 27 Feb 2026 02:36:06 GMT
• Title: Towards Intelligible Human-Robot Interaction: An Active Inference Approach to Occluded Pedestrian Scenarios
• Abstract summary: sudden appearance of occluded pedestrians presents a critical safety challenge in autonomous driving.<n>Conventional rule-based or purely data-driven approaches struggle with the inherent high uncertainty of these long-tail scenarios.<n>We propose a novel framework grounded in Active Inference, which endows the agent with a human-like, belief-driven mechanism.
• Score: 17.074920390913995
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The sudden appearance of occluded pedestrians presents a critical safety challenge in autonomous driving. Conventional rule-based or purely data-driven approaches struggle with the inherent high uncertainty of these long-tail scenarios. To tackle this challenge, we propose a novel framework grounded in Active Inference, which endows the agent with a human-like, belief-driven mechanism. Our framework leverages a Rao-Blackwellized Particle Filter (RBPF) to efficiently estimate the pedestrian's hybrid state. To emulate human-like cognitive processes under uncertainty, we introduce a Conditional Belief Reset mechanism and a Hypothesis Injection technique to explicitly model beliefs about the pedestrian's multiple latent intentions. Planning is achieved via a Cross-Entropy Method (CEM) enhanced Model Predictive Path Integral (MPPI) controller, which synergizes the efficient, iterative search of CEM with the inherent robustness of MPPI. Simulation experiments demonstrate that our approach significantly reduces the collision rate compared to reactive, rule-based, and reinforcement learning (RL) baselines, while also exhibiting explainable and human-like driving behavior that reflects the agent's internal belief state.

Related papers

• Parsimonious disturbance-aware minimum-time planning with parametric uncertainty [0.0]
  This study presents and validates a minimum-lap-time planning framework for motorsport applications.<n>The methodology builds upon a prior disturbance-aware framework that propagates vehicle dynamics over a short horizon.<n>We extend the formulation to account for uncertainty in key vehicle parameters.
  arXiv  Detail & Related papers  (2026-07-14T22:32:35Z)
• Multimodal Belief-Space Covariance Steering with Active Probing and Influence for Interactive Driving [14.159772216001954]
  We introduce a hierarchical belief model that structures human behavior across coarse discrete intents and fine motion modes.<n>We develop an active probing strategy that identifies when multimodal ambiguity in human predictions may compromise safety.<n>Finally, a runtime risk-evaluation layer based on Conditional Value-at-Risk ensures that all probing actions remain within human risk tolerance during influence.
  arXiv  Detail & Related papers  (2026-02-16T08:04:16Z)
• EvoQRE: Modeling Bounded Rationality in Safety-Critical Traffic Simulation via Evolutionary Quantal Response Equilibrium [0.0]
  EvoQRE is a principled framework for modeling safety-critical traffic interactions.<n>It integrates a pre-trained generative world model with entropy-regularized replicator dynamics.<n>It achieves state-of-the-art realism, improved safety metrics, and controllable generation of diverse safety-critical scenarios.
  arXiv  Detail & Related papers  (2026-01-09T09:21:36Z)
• More Than Irrational: Modeling Belief-Biased Agents [25.274115351731325]
  We introduce a class of computational-rational (CR) user models for cognitively-bounded agents acting optimally under biased beliefs.<n>We address the challenge of identifying the latent user-specific bound and inferring biased belief states from passive observations.<n>We show that our CR model generates intuitively plausible behaviors corresponding to different levels of memory capacity.
  arXiv  Detail & Related papers  (2025-11-15T21:14:37Z)
• SPACeR: Self-Play Anchoring with Centralized Reference Models [50.55045557371374]
  Sim agent policies are realistic, human-like, fast, and scalable in multi-agent settings.<n>Recent progress in imitation learning with large diffusion-based or tokenized models has shown that behaviors can be captured directly from human driving data.<n>We propose SPACeR, a framework that leverages a pretrained tokenized autoregressive motion model as a central reference policy.
  arXiv  Detail & Related papers  (2025-10-20T19:53:02Z)
• Drift No More? Context Equilibria in Multi-Turn LLM Interactions [58.69551510148673]
  contexts drift is the gradual divergence of a model's outputs from goal-consistent behavior across turns.<n>Unlike single-turn errors, drift unfolds temporally and is poorly captured by static evaluation metrics.<n>We show that multi-turn drift can be understood as a controllable equilibrium phenomenon rather than as inevitable decay.
  arXiv  Detail & Related papers  (2025-10-09T04:48:49Z)
• STARec: An Efficient Agent Framework for Recommender Systems via Autonomous Deliberate Reasoning [54.28691219536054]
  We introduce STARec, a slow-thinking augmented agent framework that endows recommender systems with autonomous deliberative reasoning capabilities.<n>We develop anchored reinforcement training - a two-stage paradigm combining structured knowledge distillation from advanced reasoning models with preference-aligned reward shaping.<n>Experiments on MovieLens 1M and Amazon CDs benchmarks demonstrate that STARec achieves substantial performance gains compared with state-of-the-art baselines.
  arXiv  Detail & Related papers  (2025-08-26T08:47:58Z)
• Game-Theoretic Modeling of Vehicle Unprotected Left Turns Considering Drivers' Bounded Rationality [17.5324678856791]
  We propose a novel decision-making model for vehicle unprotected left-turn scenarios.<n>Our model integrates game theory with considerations for drivers' bounded rationality.<n>Our findings contribute valuable insights into the vehicle decision-making behaviors with bounded rationality.
  arXiv  Detail & Related papers  (2025-07-02T02:22:11Z)
• Beyond Patterns: Harnessing Causal Logic for Autonomous Driving Trajectory Prediction [10.21659221112514]
  We introduce a novel trajectory prediction framework that leverages causal inference to enhance predictive robustness, generalization, and accuracy.<n>Our findings highlight the potential of causal reasoning to transform trajectory prediction, paving the way for robust autonomous driving systems.
  arXiv  Detail & Related papers  (2025-05-11T05:56:07Z)
• On Minimizing Adversarial Counterfactual Error in Adversarial RL [18.044879441434432]
  adversarial noise poses significant risks in safety-critical scenarios.<n>We introduce a novel objective called Adversarial Counterfactual Error (ACoE)<n>Our method significantly outperforms current state-of-the-art approaches for addressing adversarial RL challenges.
  arXiv  Detail & Related papers  (2024-06-07T08:14:24Z)
• SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversaries [94.84458417662407]
  We introduce SAFE-SIM, a controllable closed-loop safety-critical simulation framework. Our approach yields two distinct advantages: 1) generating realistic long-tail safety-critical scenarios that closely reflect real-world conditions, and 2) providing controllable adversarial behavior for more comprehensive and interactive evaluations. We validate our framework empirically using the nuScenes and nuPlan datasets across multiple planners, demonstrating improvements in both realism and controllability.
  arXiv  Detail & Related papers  (2023-12-31T04:14:43Z)
• Reinforcement Learning with a Terminator [80.34572413850186]
  We learn the parameters of the TerMDP and leverage the structure of the estimation problem to provide state-wise confidence bounds. We use these to construct a provably-efficient algorithm, which accounts for termination, and bound its regret.
  arXiv  Detail & Related papers  (2022-05-30T18:40:28Z)
• Active Uncertainty Learning for Human-Robot Interaction: An Implicit Dual Control Approach [5.05828899601167]
  We present an algorithmic approach to enable uncertainty learning for human-in-the-loop motion planning based on the implicit dual control paradigm. Our approach relies on sampling-based approximation of dynamic programming model predictive control problem. The resulting policy is shown to preserve the dual control effect for generic human predictive models with both continuous and categorical uncertainty.
  arXiv  Detail & Related papers  (2022-02-15T20:40:06Z)
• Risk-Sensitive Sequential Action Control with Multi-Modal Human Trajectory Forecasting for Safe Crowd-Robot Interaction [55.569050872780224]
  We present an online framework for safe crowd-robot interaction based on risk-sensitive optimal control, wherein the risk is modeled by the entropic risk measure. Our modular approach decouples the crowd-robot interaction into learning-based prediction and model-based control. A simulation study and a real-world experiment show that the proposed framework can accomplish safe and efficient navigation while avoiding collisions with more than 50 humans in the scene.
  arXiv  Detail & Related papers  (2020-09-12T02:02:52Z)

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.