Dual Process Motion Planning

• URL: http://arxiv.org/abs/2609.01260v1
• Date: Tue, 01 Sep 2026 13:52:12 GMT
• Title: Dual Process Motion Planning
• Abstract summary: We introduce a dual-process architecture that combines the strengths of robust reasoning and learning.<n>Our framework integrates state-of-the-art symbolic solvers as a System-2'' component with experience-driven System-1'' modules.
• Score: 53.570848178455776
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Robotic systems are deeply embedded in both industry and everyday life, where they are expected to act with speed, precision, and reliability. Classical control and planning methods have long delivered strong guarantees, but often at the cost of computational efficiency and adaptability. More recently, learning-based approaches have shown promise in overcoming these limitations, enabling agents to leverage experience to accelerate decision-making and address previously intractable problems. In this work, we bridge these two approaches through a neuro-symbolic perspective on nonlinear motion planning. Inspired by the Thinking Fast and Slow paradigm, we introduce a dual-process architecture that combines the strengths of robust reasoning and learning. Our framework integrates state-of-the-art symbolic solvers as a ``System-2'' component with experience-driven ``System-1'' modules. A metacognitive controller dynamically orchestrates their interaction, selecting when to rely on fast intuition versus slower, more precise reasoning. By evaluating the framework across diverse nonlinear benchmark environments, we demonstrate that this architecture yields consistent gains in planning efficiency, accuracy, and generalization, while promoting reuse across tasks. The results suggest that tightly coupling learning with structured reasoning offers a scalable path toward more capable and adaptive robotic systems.

Related papers

• Fast and Accurate: An Adaptive VLA Inference Framework through Environment-aware Model Selection [35.17327515353227]
  Environment-aware Model Selection (EMS) is an adaptive VLA inference framework that switches between two fully decoupled systems.<n>EMS achieves success rates comparable to the large-scale baseline while increasing the effective action frequency to 93.4 Hz.
  arXiv  Detail & Related papers  (2026-08-06T06:32:27Z)
• MARS: Optimizing Dual-System Deep Research via Multi-Agent Reinforcement Learning [82.14973479594367]
  Large Language Models (LLMs) for complex reasoning tasks require innovative approaches that bridge intuitive and deliberate cognitive processes.<n>This paper introduces a Multi-Agent System for Deep ReSearch (MARS) enabling seamless integration of System 1's fast, intuitive thinking with System 2's deliberate reasoning.
  arXiv  Detail & Related papers  (2025-10-06T15:42:55Z)
• Pangu Embedded: An Efficient Dual-system LLM Reasoner with Metacognition [95.54406667705999]
  Pangu Embedded is an efficient Large Language Model (LLM) reasoner developed on Ascend Neural Processing Units (NPUs)<n>It addresses the significant computational costs and inference latency challenges prevalent in existing reasoning-optimized LLMs.<n>It delivers rapid responses and state-of-the-art reasoning quality within a single, unified model architecture.
  arXiv  Detail & Related papers  (2025-05-28T14:03:02Z)
• SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving [51.47621083057114]
  SOLVE is an innovative framework that synergizes Vision-Language Models with end-to-end (E2E) models to enhance autonomous vehicle planning.<n>Our approach emphasizes knowledge sharing at the feature level through a shared visual encoder, enabling comprehensive interaction between VLM and E2E components.
  arXiv  Detail & Related papers  (2025-05-22T15:44:30Z)
• Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning [75.04643265875072]
  Large reasoning models (LRMs) have demonstrated strong performance on complex reasoning tasks, but often suffer from overthinking.<n>Inspired by the dual process theory in cognitive science, we propose Adaptive Cognition Policy Optimization.<n>ACPO enables LRMs to achieve efficient reasoning through adaptive cognitive allocation and dynamic system switch.
  arXiv  Detail & Related papers  (2025-05-22T07:15:08Z)
• DSADF: Thinking Fast and Slow for Decision Making [18.563568719536033]
  We propose a Dual-System Adaptive Decision Framework (DSADF) to integrate two complementary modules: System 1, comprising an RL agent and a memory space for fast and intuitive decision making, and System 2, driven by a VLM for deep and analytical reasoning.
  arXiv  Detail & Related papers  (2025-05-13T02:58:04Z)
• FASIONAD++ : Integrating High-Level Instruction and Information Bottleneck in FAt-Slow fusION Systems for Enhanced Safety in Autonomous Driving with Adaptive Feedback [15.55944950850973]
  FASIONAD is a novel dual-system framework that synergizes a fast end-to-end planner with a VLM-based reasoning module.<n>In open-loop experiments, FASIONAD achieves a $6.7%$ reduction in average $L2$ trajectory error and $28.1%$ lower collision rate.
  arXiv  Detail & Related papers  (2025-03-11T08:27:01Z)
• DriveTransformer: Unified Transformer for Scalable End-to-End Autonomous Driving [62.62464518137153]
  DriveTransformer is a simplified E2E-AD framework for the ease of scaling up.<n>It is composed of three unified operations: task self-attention, sensor cross-attention, temporal cross-attention.<n>It achieves state-of-the-art performance in both simulated closed-loop benchmark Bench2Drive and real world open-loop benchmark nuScenes with high FPS.
  arXiv  Detail & Related papers  (2025-03-07T11:41:18Z)
• FASIONAD : FAst and Slow FusION Thinking Systems for Human-Like Autonomous Driving with Adaptive Feedback [15.805379735361862]
  This paper presents FASIONAD, a novel dual-system framework inspired by the cognitive model "Thinking, Fast and Slow"<n>The fast system handles routine navigation tasks using rapid, data-driven path planning, while the slow system focuses on complex reasoning and decision-making in challenging or unfamiliar situations.<n>Visual prompts generated by the fast system enable human-like reasoning in the slow system, which provides high-quality feedback to enhance the fast system's decision-making.
  arXiv  Detail & Related papers  (2024-11-27T03:14:16Z)
• Synergising Human-like Responses and Machine Intelligence for Planning in Disaster Response [10.294618771570985]
  We propose an attention-based cognitive architecture inspired by Dual Process Theory (DPT) This framework integrates, in an online fashion, rapid yet (human-like) responses with the slow but optimized planning capabilities of machine intelligence.
  arXiv  Detail & Related papers  (2024-04-15T15:47:08Z)

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.