Quadrupedal robots with manipulators offer strong mobility and adaptability for grasping in unstructured, dynamic environments through coordinated whole-body control. However, existing research has predominantly focused on static-object grasping, neglecting the challenges posed by dynamic targets and thus limiting applicability in dynamic scenarios such as logistics sorting and human–robot collaboration. To address this, we introduce DQ-Bench, a new benchmark that systematically evaluates dynamic grasping across varying object motions, velocities, heights, object types, and terrain complexities, along with comprehensive evaluation metrics. Building upon this benchmark, we propose DQ-Net, a compact teacher–student framework designed to infer grasp configurations from limited perceptual cues. During training, the teacher network leverages privileged information to holistically model both the static geometric properties and dynamic motion characteristics of the target, and integrates a grasp fusion module to deliver robust guidance for motion planning. Concurrently, we design a lightweight student network that performs dual-viewpoint temporal modeling using only the target mask, depth map, and proprioceptive state, enabling closed-loop action outputs without reliance on privileged data. Extensive experiments on DQ-Bench demonstrate that DQ-Net achieves robust dynamic objects grasping across multiple task settings, substantially outperforming baseline methods in both success rate and responsiveness. We will release our codebase and benchmark publicly.

Dual-arm humanoid robots are poised to transform industrial manufacturing automation in human-centric environments. However, unlocking this potential requires a unified framework that can simultaneously handle coupled bimanual coordination, versatile physical interaction, and safety. We introduce Passive Model Predictive Cooperative Interaction Control (P-MPCIC), a framework that co-optimizes task performance and interaction safety under a formal passivity guarantee. P-MPCIC integrates model predictive control for the bimanual subsystem within a whole-body architecture and uses a coupling matrix to enforce synchronization objectives across relative motion and force distribution. For interaction prediction, the framework incorporates a composite robot-environment model that combines parallel and series impedance dynamics, yielding a linear state-space predictor. Passivity is enforced as a constraint on the energy balance at the interaction port, preventing destabilizing energy generation from the controller. We verify the framework’s core principles through planar simulations and demonstrate its practical effectiveness on a 7-DoF dual-arm humanoid.

Recent work has demonstrated the potential of diffusion models in robot bimanual skill learning. However, existing methods ignore the learning of posture-dependent task features, which are crucial for adapting dual-arm configurations to meet specific force and velocity requirements in dexterous bimanual manipulation. To address this limitation, we propose Manipulability-Aware Diffusion Policy (ManiDP), a novel imitation learning method that not only generates plausible bimanual trajectories, but also optimizes dual-arm configurations to better satisfy posture-dependent task requirements. ManiDP achieves this by extracting bimanual manipulability from expert demonstrations and encoding the encapsulated posture features using Riemannian-based probabilistic models. These encoded posture features are then incorporated into a conditional diffusion process to guide the generation of task-compatible bimanual motion sequences. We evaluate ManiDP on six real-world bimanual tasks, where the experimental results demonstrate a 39.33% increase in average manipulation success rate and a 0.45 improvement in task compatibility compared to baseline methods. This work highlights the importance of integrating posture-relevant robotic priors into bimanual skill diffusion to enable human-like adaptability and dexterity.

This paper proposes a novel whole-body impedance control method for the Collaborative dUal-arm Robot manIpulator (CURI) in Human-Robot Collaboration (HRC). The method enables CURI to adapt its physical behavior to human motion while following trajectories learned from human-human demonstrations. A whole-body impedance controller coordinates the robot joints to achieve desired Cartesian space impedance. Collaborative tasks are captured from human-human demonstrations and represented using a Task-parameterized Gaussian Mixture Model (TP-GMM). Electromyography (EMG) sensors record muscle activities to estimate human impedance profiles, which are then mimicked by a variable impedance controller. An adaptive parameter is introduced to adjust robot stiffness based on spatial displacement between the robot and human, ensuring safe and efficient interaction. Experimental validation through confrontational Tai Chi pulling/pushing tasks demonstrates the superiority of the proposed adaptive impedance method over the fixed impedance controller.

Dexterous Functional Grasping (DFG) is the crucial first step for humanoid robots to perform generalized manipulation tasks. However, enabling robots to learn language-guided DFG skills in real-world environments presents several challenges, including comprehending the complex relationship between task instructions and grasp functionality, generating feasible functional grasps of dexterous hands, and handling generalization for novel functional concepts. To tackle these challenges, we introduce SayFuncGrasp, a Large Language Model (LLM) based DFG framework that can synthesize versatile dexterous functional grasps from language instructions and achieve generalization on novel functional concepts. SayFuncGrasp first harnesses the open-ended manipulation knowledge from an LLM to infer grasp functionality based on language instructions. Subsequently, it employs the inferred grasp functionality to synthesize plausible DFG actions characterized by hand synergies. Simulation experiments show that SayFuncGrasp significantly outperforms the baseline method in open-set grasp functionality generalization. Real robot experiments demonstrate the effectiveness and generalizability of SayFuncGrasp for interactive humanoid manipulation tasks, achieving an overall grasp success rate of 64.66% and a manipulation success rate of 70.41%. Note to Practitioners—This research was motivated by the practical challenge of enabling humanoid robots with high-DoF dexterous hands to perform functional grasping based on verbal instructions. In industrial settings, such capabilities can significantly enhance the versatility and adaptability of humanoid assistants, allowing them to perform complex manipulations simply by being told what to do, thereby reducing programming complexity and increasing flexibility. Current dexterous functional grasping methods rely solely on visual input, without the ability to process language instructions. Furthermore, they are restricted to pre-defined functional concepts and cannot be generalized to novel object classes and manipulation tasks within natural language. Our newly proposed language-guided dexterous functional grasping system takes advantage of open-ended manipulation knowledge from LLMs to produce generalized functional grasps of dexterous robot hands according to verbal commands. Our experiment results demonstrate improved versatility and generalizability compared to the state-of-the-art.

Traditional strength and conditioning training relies on the utilization of free weights, such as weighted implements, to elicit external stimuli. However, this approach poses a significant challenge when attempting to modify or adjust the loads within a single training set. This paper introduces an innovative method for achieving adjustable loads during resistance training by leveraging physical Human-Robot Interaction (pHRI). The primary objective is to regulate targeted muscle activation through the use of Robo-Coach (robotic coach system). We first utilize a Task-Parameterized Gaussian Mixture Model (TP-GMM) to learn the motion of coach demonstration, which can be generalized for the trainees. The 3D path extracted from the generated trajectory is then projected onto a 2D plane with respect to the direction of the load. Furthermore, we propose a hybrid stiffness/position generator for online task execution. This generator determines the desired positions in the 2D plane according to the contact point displacements in the stimuli direction and, simultaneously, sets the desired stiffness based on the muscle activation feedback. Finally, the Robo-Coach is implemented with a variable impedance controller to achieve load-adjustable resistance training with the trainee. The biceps curl exercises were conducted and the results showed favorable performance, indicating the effectiveness of this approach.

Deformable objects especially large-size de-formable objects grasping is unappreciated but widespread in industrial applications (e.g., clothes recycling). While it encounters several challenges, for example, the existing methods didn’t take large-size deformable objects into account, no typical boundary of deformable objects. To solve the challenges, we proposed a grasp detection framework consisting of a self-trained object detection network, an instance segmentation module, and a grasp pose generation pipeline. The experiments were successfully conducted on the industrial laundry mock-up with an 88.9% success ratio. The experiments result indicates the effectiveness of the proposed framework on spatial-constrained large-size deformable objects grasping in clutter.

This article investigates energy efficiency (EE) maximization for an Internet of Things-based (IoT) distributed antenna system (DAS) with imperfect channel state information (CSI) subject to maximum transmit power and minimum data rate constraints. First, we derive achievable rates and corresponding EE expressions of the system for single and multiple IoT devices. Then, we formulate the EE maximization problem in the single-device IoT-DAS with imperfect CSI, and it has a special form of the difference of concave functions. To solve this problem, we develop a near-optimal power allocation (PA) scheme by using the concave–convex procedure (CCCP) and block coordinate descent (BCD) method. It can achieve an EE performance similar to that of the optimal exhaustive search scheme, but with lower complexity. Based on this result, we present a suboptimal PA scheme with a single-layer iteration to decrease the complexity and maintain a performance similar to that of the near-optimal scheme. Using the results above, the EE optimization for IoT-DAS with imperfect CSI for multiple devices is addressed. A near-optimal PA scheme based on the fractional programming (FP) theory as well as the CCCP and BCD methods for solving the optimization problem is presented. Also, a suboptimal PA scheme based on the FP theory and BCD method is developed to further decrease the complexity. Interestingly, these two schemes have the same performance for perfect CSI. Simulation results show the validity of the developed schemes and their superior performances over existing methods.

Energy efficiency (EE) optimization of joint power allocation (PA) and beamforming (BF) is studied for multiuser distributed multiple-input multiple-output (D-MIMO) systems with maximum ratio combining. Subject to power budget constraints, a non-convex constrained optimization problem is firstly formulated to maximize EE of single-user D-MIMO. We propose a near-optimal joint design scheme with the block coordinate descent method, which has an effective iterative algorithm to find optimal BF for fixed PA, and an iterative algorithm based on the Dinkelbach methods to find optimal PA for fixed BF. We also develop a low-complexity suboptimal joint scheme, which has closed-form BF and PA by maximizing an effective signal-to-noise ratio for fixed PA and a derived EE upper bound for fixed BF, respectively. It has lower complexity than the near-optimal scheme with slight performance loss. With these results, two joint design schemes are developed for multiuser D-MIMO. The first scheme can achieve the best performance, while the second one based on closed-form PA and BF has slight performance loss in comparing to that of the former, but with lower complexity. Simulation results verify the effectiveness of the developed schemes over some existing schemes.

Due to the requirement of low-power network devices for the proliferation of high data communication, enabling technologies for energy sustainable Internet of Things (IoT) are of great significance. In this article, we investigate the energy-efficiency (EE) optimization of IoT-distributed antenna (DA) system with simultaneous wireless information and power transfer (SWIPT) technique over fading channels, where the IoT device is equipped with power splitter to integrate the energy harvesting and information decoding processes via adjusting the transmit power of each DA port and power splitting (PS) ratio of IoT device. According to the analysis of EE, an optimization problem with the aim of maximizing the system EE is formulated under the constraints of maximum transmit power of each DA port as well as minimum harvested energy. Through analyzing the structure of the objective problem, it is found that the EE optimization problem, which combines transmit power allocation (PA) with PS problem, can be predigested to a PA problem. Then, the amount of valid DA ports and the corresponding PA are achieved by utilizing the Karush-Kuhn-Tucker conditions and Lambert function. Based on this, we propose an optimal resource allocation scheme without iteration to obtain the optimal PA and PS ratio, and the resulting closed-form expressions are provided. Considering that perfect channel state information (CSI) is hard to achieve, we also study the resource allocation scheme based on the imperfect CSI (i.e., statistical CSI with large-scale fading information), two suboptimal schemes are proposed. These two schemes have better robustness and lower complexity than the optimal scheme with perfect CSI because they only need partial channel information, but the performances are worse than the latter, as expected. Computer simulation indicates that proposed schemes are valid and can obtain superior EE performance with lower complexity.