Heterogeneous Multi-Expert Reinforcement Learning for Long-Horizon Multi-Goal Tasks in Autonomous Forklifts

• URL: http://arxiv.org/abs/2601.07304v1
• Date: Mon, 12 Jan 2026 08:27:24 GMT
• Title: Heterogeneous Multi-Expert Reinforcement Learning for Long-Horizon Multi-Goal Tasks in Autonomous Forklifts
• Authors: Yun Chen, Bowei Huang, Fan Guo, Kang Song,
• Abstract summary: We propose a Heterogeneous Multi-Expert Reinforcement Learning (HMER) framework tailored for autonomous forklifts.<n>HMER decomposes long-horizon tasks into specialized sub-policies controlled by a Semantic Task Planner.<n>Our method achieves a task success rate of 94.2% (compared to 62.5% for baselines), reduces operation time by 21.4%, and maintains placement error within 1.5 cm.
• Score: 5.215925647203835
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Autonomous mobile manipulation in unstructured warehouses requires a balance between efficient large-scale navigation and high-precision object interaction. Traditional end-to-end learning approaches often struggle to handle the conflicting demands of these distinct phases. Navigation relies on robust decision-making over large spaces, while manipulation needs high sensitivity to fine local details. Forcing a single network to learn these different objectives simultaneously often causes optimization interference, where improving one task degrades the other. To address these limitations, we propose a Heterogeneous Multi-Expert Reinforcement Learning (HMER) framework tailored for autonomous forklifts. HMER decomposes long-horizon tasks into specialized sub-policies controlled by a Semantic Task Planner. This structure separates macro-level navigation from micro-level manipulation, allowing each expert to focus on its specific action space without interference. The planner coordinates the sequential execution of these experts, bridging the gap between task planning and continuous control. Furthermore, to solve the problem of sparse exploration, we introduce a Hybrid Imitation-Reinforcement Training Strategy. This method uses expert demonstrations to initialize the policy and Reinforcement Learning for fine-tuning. Experiments in Gazebo simulations show that HMER significantly outperforms sequential and end-to-end baselines. Our method achieves a task success rate of 94.2\% (compared to 62.5\% for baselines), reduces operation time by 21.4\%, and maintains placement error within 1.5 cm, validating its efficacy for precise material handling.

Related papers

• OmniVL-Guard: Towards Unified Vision-Language Forgery Detection and Grounding via Balanced RL [63.388513841293616]
  Existing forgery detection methods fail to handle the interleaved text, images, and videos prevalent in real-world misinformation.<n>To bridge this gap, this paper targets to develop a unified framework for omnibus vision-language forgery detection and grounding.<n>We propose textbf OmniVL-Guard, a balanced reinforcement learning framework for omnibus vision-language forgery detection and grounding.
  arXiv  Detail & Related papers  (2026-02-11T09:41:36Z)
• SMES: Towards Scalable Multi-Task Recommendation via Expert Sparsity [47.79376327982703]
  Industrial recommender systems rely on multi-task learning to estimate diverse user feedback signals and aggregate them for ranking.<n>Recent advances in model scaling have shown promising gains in recommendation.<n>This mismatch between uniform parameter scaling and heterogeneous task capacity demands poses a fundamental challenge for scalable multi-task recommendation.
  arXiv  Detail & Related papers  (2026-02-10T03:56:12Z)
• SAME: Stabilized Mixture-of-Experts for Multimodal Continual Instruction Tuning [83.66308307152808]
  We propose StAbilized Mixture-of-Experts (SAME) for Multimodal Continual Instruction Tuning (MCIT)<n>SAME stabilizes expert selection by decomposing routing dynamics into subspaces and updating only task-relevant directions.<n>It also introduces adaptive expert activation to freeze selected experts during training, reducing redundant and cross-task interference.
  arXiv  Detail & Related papers  (2026-02-02T11:47:06Z)
• ThanoRA: Task Heterogeneity-Aware Multi-Task Low-Rank Adaptation [96.86211867758652]
  Low-Rank Adaptation (LoRA) is widely adopted for downstream fine-tuning of foundation models.<n>We propose ThanoRA, a Task Heterogeneity-Aware Multi-Task Low-Rank Adaptation framework.
  arXiv  Detail & Related papers  (2025-05-24T11:01:45Z)
• LLaVA-CMoE: Towards Continual Mixture of Experts for Large Vision-Language Models [21.888139819188105]
  LLaVA-CMoE is a continual learning framework for large language models.<n> Probe-Guided Knowledge Extension mechanism determines when and where new experts should be added.<n>Probabilistic Task Locator assigns each task a dedicated, lightweight router.
  arXiv  Detail & Related papers  (2025-03-27T07:36:11Z)
• Investigating the Impact of Choice on Deep Reinforcement Learning for Space Controls [0.3441021278275805]
  This paper analyzes using discrete action spaces, where the agent must choose from a predefined list of actions. Experiments are conducted for an inspection task, where the agent must circumnavigate an object to inspect points on its surface, and a docking task, where the agent must move into proximity of another spacecraft and "dock" A common objective of both tasks, and most space tasks in general, is to minimize fuel usage, which motivates the agent to regularly choose an action that uses no fuel.
  arXiv  Detail & Related papers  (2024-05-20T20:06:54Z)
• Latent Exploration for Reinforcement Learning [87.42776741119653]
  In Reinforcement Learning, agents learn policies by exploring and interacting with the environment. We propose LATent TIme-Correlated Exploration (Lattice), a method to inject temporally-correlated noise into the latent state of the policy network.
  arXiv  Detail & Related papers  (2023-05-31T17:40:43Z)
• ReLMoGen: Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation [99.2543521972137]
  ReLMoGen is a framework that combines a learned policy to predict subgoals and a motion generator to plan and execute the motion needed to reach these subgoals. Our method is benchmarked on a diverse set of seven robotics tasks in photo-realistic simulation environments. ReLMoGen shows outstanding transferability between different motion generators at test time, indicating a great potential to transfer to real robots.
  arXiv  Detail & Related papers  (2020-08-18T08:05:15Z)
• Using Deep Reinforcement Learning Methods for Autonomous Vessels in 2D Environments [11.657524999491029]
  In this work, we used deep reinforcement learning combining Q-learning with a neural representation to avoid instability. Our methodology uses deep q-learning and combines it with a rolling wave planning approach on agile methodology. Experimental results show that the proposed method enhanced the performance of VVN by 55.31 on average for long-distance missions.
  arXiv  Detail & Related papers  (2020-03-23T12:58:58Z)
• Gradient Surgery for Multi-Task Learning [119.675492088251]
  Multi-task learning has emerged as a promising approach for sharing structure across multiple tasks. The reasons why multi-task learning is so challenging compared to single-task learning are not fully understood. We propose a form of gradient surgery that projects a task's gradient onto the normal plane of the gradient of any other task that has a conflicting gradient.
  arXiv  Detail & Related papers  (2020-01-19T06:33:47Z)

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.