Robotic manipulators in warehousing and logistics often face complex tasks that involve multiple steps, frequent task switching, and long-term dependencies. Inspired by the hierarchical structure of human motor control, this paper proposes a Hierarchical Reinforcement Learning (HRL) framework utilizing a multi-task goal-conditioned Decision Transformer (MTGC-DT). The high-level policy treats the Markov decision process as a sequence modeling task, allowing the agent to manage temporal dependencies. The low-level policy is made up of parameterized action primitives that handle physical execution. This design improves long-term reasoning and generalization. This method is evaluated on two common logistics manipulation tasks: sequential stacking and spatial sorting with sparse reward and low-quality dataset. The main contributions include introducing a HRL framework that integrates Decision Transformer (DT) with task and goal embeddings, along with a path-efficiency loss (PEL) correction and designing a parameterized, learnable primitive skill library for low-level control to enhance generalization and reusability. Experimental results demonstrate that the proposed Decision Transformer-based Hierarchical Reinforcement Learning (DT-HRL) achieves over a 10% higher success rate and over 8% average reward compared with the baseline, and a normalized score increase of over 2% in the ablation experiments.

The Segment Anything Model (SAM) provides a versatile promptable segmentation interface, but adverse weather and image degradation can obscure visual cues and substantially reduce its accuracy. Existing approaches mainly follow two directions to mitigate this issue. The first adapts SAM through fine-tuning or additional trainable modules, which requires model-side optimization. The second preprocesses degraded images before SAM inference, but such methods are generally optimized for perceptual restoration rather than downstream segmentation. We introduce WeatherAgent-SAM, an agentic framework that improves degraded-image segmentation through response-conditioned preprocessing selection while keeping SAM frozen. WeatherAgent-SAM takes SAM’s initial response and a spatial prompt as inputs and uses response-conditioned knowledge to determine which preprocessing method should be applied next. To realize this process, the framework comprises two key modules. First, Fine-Grained Response-Conditioned Knowledge Construction adopts a progressive coarse-to-fine strategy: it first categorizes SAM responses into recurring states and then explores how individual preprocessing methods affect segmentation within each state. This design enables the agent to distinguish methods that pursue similar enhancement objectives but produce different segmentation outcomes. Second, a learnable Mask Verifier estimates the quality of each resulting SAM mask without ground truth and provides feedback to the agent, allowing tool selection to proceed iteratively and stop when a satisfactory prediction is identified. Experiments demonstrate that WeatherAgent-SAM achieves results comparable to those of the evaluated baselines on the Robust-Seg dataset and outperforms SAM, RobustSAM, and GaRA-SAM-Syn on the ACDC dataset.