On-Device Robotic Planning: Eliminating Inference Redundancy for Efficient Decision-Making

• URL: http://arxiv.org/abs/2605.31460v1
• Date: Fri, 29 May 2026 15:51:42 GMT
• Title: On-Device Robotic Planning: Eliminating Inference Redundancy for Efficient Decision-Making
• Abstract summary: Reasoning-based robotic policies using large language and vision-language models achieve strong semantic planning capabilities but mostly suffer from a high inference latency.<n>We present REIS, a human cognition inspired robotic decision-making framework that minimizes unnecessary reasoning while preserving semantic adaptability.
• Score: 4.723358389541955
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reasoning-based robotic policies using large language and vision-language models achieve strong semantic planning capabilities but mostly suffer from a high inference latency that limits practical real-time deployment. In this work, we observe that robotic reasoning workloads contain substantial temporal redundancy, where consecutive observations frequently produce identical actions and subgoals. Based on this insight, we present REIS, a human cognition inspired robotic decision-making framework that minimizes unnecessary reasoning while preserving semantic adaptability. REIS combines lightweight scene gating, KV-steered affordance routing, and deliberative reasoning to accelerate robotic control under embodied constraints. Experiments on ALFRED, and real-world robotic tasks demonstrate that REIS significantly suppresses reasoning overhead while maintaining competitive task performance.

Related papers

• Occluding the Solution Space: Planner-Agnostic Adversarial Attacks on Tolerance-Aware Manipulation [57.9825059129472]
  We propose a planner-agnostic attack framework for tolerance-aware manipulation.<n>Our approach shifts the evaluation paradigm to task-level feasibility over goal regions.<n>Online, we formulate the attack as a budgeted maximum-coverage optimization.
  arXiv  Detail & Related papers  (2026-07-04T08:14:44Z)
• RoboWits: Unexpected Challenges for Robotic Creative Problem Solving [58.58727722988084]
  We introduce RoboWits, a bi-manual robotic benchmark designed to evaluate cognitive reasoning, creative tool use, and robustness to unexpected conditions.<n>Using the pipeline, we curated 30 diverse seed tasks and 208 tasks with mutations and graded difficulty across geometry, material, and assembly-based reasoning.<n>Our results reveal a significant performance gap: while pre-trained VLAs exhibit preliminary success on seed tasks after single-task fine-tuning, they struggle to perform on mutated tasks.
  arXiv  Detail & Related papers  (2026-05-28T17:57:15Z)
• Simulation-Informed Diffusion for Decentralized Multi-robot Motion Planning [56.240199425429445]
  Multi-robot motion planning requires each robot to generate collision-free trajectories from local observations.<n>Most existing planners generate trajectories from a static snapshot of the local observation.<n>This paper introduces Simulation-Informed Diffusion (SID), a decentralized framework built on constraint-aware diffusion models.
  arXiv  Detail & Related papers  (2026-05-26T21:17:53Z)
• When Should a Robot Think? Resource-Aware Reasoning via Reinforcement Learning for Embodied Robotic Decision-Making [68.12864562049957]
  Embodied robotic systems increasingly rely on large language model (LLM)-based agents to support high-level reasoning.<n>This raises a fundamental question for embodied agents: when should the agent reason, and when should it act?<n>We propose RARRL (Resource-Aware Reasoning via Reinforcement Learning), a hierarchical framework for resource-aware orchestration of embodied agents.
  arXiv  Detail & Related papers  (2026-03-17T15:38:50Z)
• Vision-based Goal-Reaching Control for Mobile Robots Using a Hierarchical Learning Framework [4.014524824655106]
  This paper decomposes the whole system into a set of tightly coupled functional modules.<n>The proposed framework guarantees uniform exponential stability of the actuation system and safety of the whole operation.
  arXiv  Detail & Related papers  (2026-01-02T08:41:47Z)
• Lite VLA: Efficient Vision-Language-Action Control on CPU-Bound Edge Robots [0.6119773373677944]
  This work demonstrates the feasibility of deploying small Vision-Language Models (VLMs) on mobile robots to achieve real-time scene understanding and reasoning under strict computational constraints.<n>Unlike prior approaches that separate perception from mobility, the proposed framework enables simultaneous movement and reasoning in dynamic environments using only on-board hardware.
  arXiv  Detail & Related papers  (2025-11-07T17:49:14Z)
• NoTVLA: Narrowing of Dense Action Trajectories for Generalizable Robot Manipulation [54.87964060934928]
  Vision-Language-Action (VLA) models confront critical barriers to real-world deployment, most notably catastrophic forgetting.<n>We propose the Narrowing of Trajectory VLA framework: a novel approach that narrows its focus to sparse trajectories.<n>NoTVLA achieves superior performance and generalization compared to pi0 while operating under two critical constraints.
  arXiv  Detail & Related papers  (2025-10-04T18:26:55Z)
• RoboPilot: Generalizable Dynamic Robotic Manipulation with Dual-thinking Modes [5.47460315248808]
  We present RoboPilot, a dual-thinking closed-loop framework for robotic manipulation.<n>RoboPilot supports adaptive reasoning for complex tasks in real-world dynamic environments.<n>We show that RoboPilot outperforms state-of-the-art baselines by 25.9% in task success rate.
  arXiv  Detail & Related papers  (2025-09-30T18:25:47Z)
• Robot-R1: Reinforcement Learning for Enhanced Embodied Reasoning in Robotics [55.05920313034645]
  We introduce Robot-R1, a novel framework that leverages reinforcement learning to enhance embodied reasoning specifically for robot control.<n>Inspired by the DeepSeek-R1 learning approach, Robot-R1 samples reasoning-based responses and reinforces those that lead to more accurate predictions.<n>Our experiments show that models trained with Robot-R1 outperform SFT methods on embodied reasoning tasks.
  arXiv  Detail & Related papers  (2025-05-29T16:41:12Z)
• REI-Bench: Can Embodied Agents Understand Vague Human Instructions in Task Planning? [12.490512012911635]
  Linguists suggest that such vagueness frequently arises from referring expressions (REs)<n>This paper studies how such vagueness in REs within human instructions affects LLM-based robot task planning.<n>We propose the first robot task planning benchmark with vague REs (REI-Bench), where we discover that the vagueness of REs can severely degrade robot planning performance.
  arXiv  Detail & Related papers  (2025-05-16T05:27:15Z)
• Prognostic Framework for Robotic Manipulators Operating Under Dynamic Task Severities [1.8485970721272895]
  We present a prognostic modeling framework that predicts a robotic manipulator's Remaining Useful Life (RUL)<n>Our findings show that robots in both fleets experience shorter RUL when handling a higher proportion of high-severity tasks.
  arXiv  Detail & Related papers  (2024-11-30T17:09:18Z)
• iCORPP: Interleaved Commonsense Reasoning and Probabilistic Planning on Robots [46.13039152809055]
  We present a novel algorithm, called iCORPP, to simultaneously estimate the current world state, reason about world dynamics, and construct task-oriented controllers. Results show significant improvements in scalability, efficiency, and adaptiveness, compared to competitive baselines.
  arXiv  Detail & Related papers  (2020-04-18T17:46:59Z)

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.