ALAS: Adaptive Long-Horizon Action Synthesis via Async-pathway Stream Disentanglement

• URL: http://arxiv.org/abs/2604.20721v1
• Date: Wed, 22 Apr 2026 16:07:24 GMT
• Title: ALAS: Adaptive Long-Horizon Action Synthesis via Async-pathway Stream Disentanglement
• Abstract summary: ALAS is a cross-domain learning framework for LH tasks via biologically inspired dual-stream disentanglement.<n>ALAS can achieve an average subtasks success rate improvement of 23% and average execution efficiency improvement of 29%.
• Score: 6.15749307717446
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Long-Horizon (LH) tasks in Human-Scene Interaction (HSI) are complex multi-step tasks that require continuous planning, sequential decision-making, and extended execution across domains to achieve the final goal. However, existing methods heavily rely on skill chaining by concatenating pre-trained subtasks, with environment observations and self-state tightly coupled, lacking the ability to generalize to new combinations of environments and skills, failing to complete various LH tasks across domains. To solve this problem, this paper presents ALAS, a cross-domain learning framework for LH tasks via biologically inspired dual-stream disentanglement. Inspired by the brain's "where-what" dual pathway mechanism, ALAS comprises two core modules: i) an environment learning module for spatial understanding, which captures object functions, spatial relationships, and scene semantics, achieving cross-domain transfer through complete environment-self disentanglement; ii) a skill learning module for task execution, which processes self-state information including joint degrees of freedom and motor patterns, enabling cross-skill transfer through independent motor pattern encoding. We conducted extensive experiments on various LH tasks in HSI scenes. Compared with existing methods, ALAS can achieve an average subtasks success rate improvement of 23\% and average execution efficiency improvement of 29\%.

Related papers

• Connect the Dots: Training LLMs for Long-Lifecycle Agents with Cross-Domain Generalization Via Reinforcement Learning [82.3676770818744]
  We present a framework for training large language models (LLMs) to "Connect the Dots" (CoD)<n>As an LLM-based AI agent gets deployed in an environment, it solves a long sequence of tasks while continuously exploring the environment.<n>We present proof-of-concept implementations of the CoD framework, including a GRPO-style RL algorithm with fine-grained credit assignment.
  arXiv  Detail & Related papers  (2026-06-18T09:38:45Z)
• Essential Subspace Merging for Multi-Task Learning [78.5390284258307]
  We analyze the output shifts induced by task updates and observe that their energy is concentrated in a small number of principal directions.<n>Most remaining directions carry little task-relevant energy, but their accumulation across multiple task updates can cause severe interference during merging.<n>Based on ESD, we introduce Essential Subspace Merging (ESM), a training-free static merging method.<n>We extend ESM to ESM++, a training-free dynamic merging method that decomposes task-specific residuals into low-rank experts.
  arXiv  Detail & Related papers  (2026-06-17T15:07:11Z)
• ProgVLA: Progress-Aware Robot Manipulation Skill Learning [13.001629637153583]
  We present ProgVLA, a compact vision-language-action (VLA) model for reliable robot manipulation under tight compute and memory budgets.<n>A multi-modal encoder with a two-stage Perceiver resampling scheme compresses variable-length visual, language, and proprioceptive streams into a fixed set of control-ready context tokens.<n>An auxiliary set of progress heads is trained with offline reinforcement learning objectives to jointly learn critics over normalized remaining-horizon targets.
  arXiv  Detail & Related papers  (2026-05-27T09:44:46Z)
• PALM: Progress-Aware Policy Learning via Affordance Reasoning for Long-Horizon Robotic Manipulation [27.791908160098625]
  PALM builds policy learning around interaction-centric affordance reasoning and subtask progress cues.<n>Palm consistently outperforms baselines in simulation and real-world experiments.
  arXiv  Detail & Related papers  (2026-01-11T21:00:58Z)
• CrossEarth-Gate: Fisher-Guided Adaptive Tuning Engine for Efficient Adaptation of Cross-Domain Remote Sensing Semantic Segmentation [32.405967784469304]
  CrossEarth-Gate addresses multifaceted domain gaps in Remote Sensing (RS) data.<n>We develop a Fisher-guided adaptive selection mechanism that operates on this toolbox.<n>Our method achieves state-of-the-art performance across 16 cross-domain benchmarks for RS semantic segmentation.
  arXiv  Detail & Related papers  (2025-11-25T13:41:59Z)
• DETACH: Cross-domain Learning for Long-Horizon Tasks via Mixture of Disentangled Experts [6.15749307717446]
  DETACH is a cross-domain learning framework for LH tasks via biologically inspired dual-stream disentanglement.<n>It can achieve an average subtasks success rate improvement of 23% and average execution efficiency improvement of 29%.
  arXiv  Detail & Related papers  (2025-08-11T10:54:28Z)
• Enhancing Cross-task Transfer of Large Language Models via Activation Steering [75.41750053623298]
  Cross-task in-context learning offers a direct solution for transferring knowledge across tasks.<n>We investigate whether cross-task transfer can be achieved via latent space steering without parameter updates or input expansion.<n>We propose a novel Cross-task Activation Steering Transfer framework that enables effective transfer by manipulating the model's internal activation states.
  arXiv  Detail & Related papers  (2025-07-17T15:47:22Z)
• CKAA: Cross-subspace Knowledge Alignment and Aggregation for Robust Continual Learning [80.18781219542016]
  Continual Learning (CL) empowers AI models to continuously learn from sequential task streams.<n>Recent parameter-efficient fine-tuning (PEFT)-based CL methods have garnered increasing attention due to their superior performance.<n>We propose Cross-subspace Knowledge Alignment and Aggregation (CKAA) to enhance robustness against misleading task-ids.
  arXiv  Detail & Related papers  (2025-07-13T03:11:35Z)
• 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)
• Solving Continual Offline RL through Selective Weights Activation on Aligned Spaces [52.649077293256795]
  Continual offline reinforcement learning (CORL) has shown impressive ability in diffusion-based lifelong learning systems. We propose Vector-Quantized Continual diffuser, named VQ-CD, to break the barrier of different spaces between various tasks.
  arXiv  Detail & Related papers  (2024-10-21T07:13:45Z)
• Learning to Relate Depth and Semantics for Unsupervised Domain Adaptation [87.1188556802942]
  We present an approach for encoding visual task relationships to improve model performance in an Unsupervised Domain Adaptation (UDA) setting. We propose a novel Cross-Task Relation Layer (CTRL), which encodes task dependencies between the semantic and depth predictions. Furthermore, we propose an Iterative Self-Learning (ISL) training scheme, which exploits semantic pseudo-labels to provide extra supervision on the target domain.
  arXiv  Detail & Related papers  (2021-05-17T13:42:09Z)
• Meta Reinforcement Learning with Autonomous Inference of Subtask Dependencies [57.27944046925876]
  We propose and address a novel few-shot RL problem, where a task is characterized by a subtask graph. Instead of directly learning a meta-policy, we develop a Meta-learner with Subtask Graph Inference. Our experiment results on two grid-world domains and StarCraft II environments show that the proposed method is able to accurately infer the latent task parameter.
  arXiv  Detail & Related papers  (2020-01-01T17:34:00Z)

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.