Articulated continuum robots (ACRs) are characterized by flexibility, controllability, and adaptability and perform excellently in complex and constrained environments. However, the large number of motor drives limit the ACRs' portability and make them cumbersome to control. This paper presents a novel tendon-driven ACR composed of stabilized self-locking joints (SLJs) connected in series. After triggering the mechanical constraints with shape memory alloy coils, each joint can be maintained in either a self-locking or release state with zero power consumption. Consequently, even with a single set of drive units, the ACR can operate in multiple modes, enabling variable motion performance and workspace adaptability, effectively reducing the number of motors. The ACR's stiffness also varies with the locking state of its SLJs, and no motor drive is required to maintain its shape when all SLJs are self-locking. The performance and reliability of the SLJ prototype were validated. The workspace of the ACR prototype model was analyzed, and its partial motion performance, motion error, and variable stiffness were verified.

In this work, we focus on addressing the long-horizon packing tasks in densely cluttered scenes. Such tasks require policies to effectively manage severe occlusions among objects and continually produce precise actions based on visual observations. We propose a vision-based Hierarchical policy for Cluttered-scene Long-horizon Manipulation (HCLM). It employs a high-level policy and three options to select and instantiate three parameterized action primitives: push, pick, and place. We first train the two-stream pick and place options by behavior cloning (BC). Subsequently, we use hierarchical reinforcement learning (HRL) to train the high-level policy and push option. During HRL, we propose a Spatially Extended Q-update (SEQ) to augment the updates for the push option and a Two-Stage Update Scheme (TSUS) to alleviate the non-stationary transition problem in updating the high-level policy. We demonstrate that HCLM significantly outperforms baseline methods in terms of success rate and efficiency in diverse tasks both in simulation and real world. The ablation studies also validate the key roles of SEQ and TSUS in HRL.

The true implementation of the Anticipatory Steam Detection for Steamer-Filling(ASDSF) process in baijiu intelligent distillation systems, which involves predicting and precisely spreading distillers’ grains before steam emerges, remains a critical unresolved challenge. In this study, we introduce the SLU model, which utilizes SwinLSTM as the core feature extraction module and adopts a U-shaped structure. This model achieves spatiotemporal feature extraction and dynamic change prediction. It is further enhanced by integrating a U-Net module for multi-scale feature fusion and optimized through a Deep Q-Network (DQN)-based decision-making process. The SLU-DQN model, specifically designed for anticipatory material spreading planning in the baijiu Steamer-Filling(SF) distillation system, predicts future steam emission areas. Finally, both quantitative and qualitative experimental results demonstrate the excellent performance of the SLU-DQN model in solving the ASDSF problem. The model achieved 91.1% reward accuracy, an F1-Score of 91% for material spreading point prediction, an MSE of 19.02, and an SSIM of 95.8%. These results not only highlight the model’s superior accuracy in predicting future steam emission areas but also provide a significant technical breakthrough for intelligent baijiu distillation systems, filling a crucial gap in the field.