Musculoskeletal robotic systems offer structural advantages while presenting significant control challenges. Current research on their manipulation capabilities, particularly multi-object grasping scenarios, remains insufficient. Furthermore, as robots operate in dynamic environments with evolving task requirements, developing their ability to grasp novel objects while maintaining existing manipulation skills becomes crucial. To address these challenges, we propose a novel continual learning method for generalized grasping manipulation in a musculoskeletal upper limb robot. First, we propose an end-to-end learning method for multi-object grasping that leverages object-specific latent features and multi-level states of a musculoskeletal robot. Additionally, a novel continual learning method for grasping tasks is proposed with a bio-inspired object-preference-based experience replay selector, which optimizes learning efficiency while mitigating catastrophic forgetting. Our approach successfully masters the grasping manipulation task of 10 objects in the first phase and continually learns 23 additional objects in the second phase, outperforming existing methods in both multi-object grasping and continual learning. Furthermore, our analyses of muscle synergy and methodological robustness demonstrate that the proposed approach generates biologically plausible muscle synergies and exhibits strong robustness against object observation bias and neural excitation noise. Our experiments, conducted both in simulation and on a hardware system, demonstrate the practical transferability of our method to an articulated dexterous hand grasping task. Note to Practitioners—Musculoskeletal robots, which incorporate human-like joints, muscles, and actuation mechanisms, exhibit exceptional robustness, dexterity, and compliance, offering promising applications and significant control challenges. While current research predominantly addresses lower limb locomotion and upper limb movements, studies on multi-object grasping and continual learning remain limited. This article presents a novel continual learning method for generalized grasping manipulation in a musculoskeletal upper limb, enabling end-to-end multi-object grasping without the dependency on predefined or generated grasping postures. The proposed methodology encompasses two key components: 1) the extraction and processing of object point cloud features as partial observations for the controller and 2) the implementation of a bio-inspired object-preference-based experience replay selector that balances new object learning and existing skill retention. Experimental evaluations reveal that the proposed approach exhibits effective muscle synergy patterns and demonstrates robust performance under diverse noise conditions and magnitudes, indicating significant potential for real-world deployment and cross-domain adaptation. Nevertheless, future work should address current limitations, including the extension from fixed-point to arbitrary position grasping, and the advancement from single-step grasping to complex multi-stage manipulation and assembly tasks.

Simultaneous localization and mapping (SLAM) is the basis for intelligent robots to explore the world. As a promising method for 3D reconstruction, 3D Gaussian splatting (3DGS) integrated with SLAM systems has shown significant potential. However, due to environmental uncertainties, errors in the tracking process with 3D Gaussians can negatively impact SLAM systems. This paper introduces a novel dense RGB-D SLAM system based on 3DGS that refines Gaussians through sub-Gaussians in the camera coordinate system. Additionally, we propose an algorithm to select keyframes closely related to the current frame, optimizing the scene map and pose of the current keyframe. This approach effectively enhances both the tracking and mapping performance. Experiments on high-quality synthetic scenes (Replica dataset) and low-quality real-world scenes (TUM-RGBD and ScanNet datasets) demonstrate that our system achieves competitive performance in tracking and mapping.

In this paper, based on the conformal mapping method and the perturbation theory, we develop a method to solve the statistical problem within general two-dimensional regions. We consider some examples and the numerical results and fitting results are given. We also give the thermodynamic quantities of the general two-dimensional regions, and compare the thermodynamic quantities of the different regions.

Musculoskeletal robots with anthropomorphic structures show great potential for human-like dexterous manipulation. However, their high-dimensional, coupled and nonlinear dynamics pose significant challenges to control and manipulation. Despite recent advances, most existing methods still struggle to achieve flexible multi-object manipulation without long-sequence expert demonstrations or manual parameter tuning based on expert knowledge. To tackle these limitations, a bio-inspired reinforcement learning framework named BioSynGrasp is proposed. It comprises three core components: 1) A neuromuscular controller that integrates shared perception and planning modules with distinct arm and hand synergies sub-networks; 2) A proximal policy optimization-based exploration strategy driven by hand and arm muscle synergies with correlated actuator perturbations; 3) Hand preshapes measured via motion capture provide efficient initialization for functional grasping and reaching with minimal data. BioSynGrasp is validated on a simulated musculoskeletal robot with 63 actuated muscles and on a hardware articulated dexterous hand system. Results demonstrate that it successfully completes 28 multi-object grasping and reaching tasks with an average success rate of 85.9%, outperforming baseline methods in success rate, energy efficiency, position error, and redundant actuator recruitment. These results highlight that integrating bio-inspired motor control with hand preshapes enhances human-like dexterous manipulation and provides insights into human neuromechanics for more adaptable robotic systems.