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.

This paper introduces an upper limb postural optimization method for enhancing physical ergonomics and force manipulability during bimanual human-robot co-carrying tasks. Existing research typically emphasizes human safety or manipulative efficiency, whereas our proposed method uniquely integrates both aspects to strengthen collaboration across diverse conditions (e.g., different grasping postures of humans, and different shapes of objects). Specifically, the joint angles of a simplified human skeleton model are optimized by minimizing the cost function to prioritize safety and manipulative capability. To guide humans towards the optimized posture, the reference end-effector poses of the robot are generated through a transformation module. A bimanual model predictive impedance controller (MPIC) is proposed for our human-like robot, CURI, to recalibrate the end effector poses through planned trajectories. The proposed method has been validated through various subjects and objects during human-human collaboration (HHC) and human-robot collaboration (HRC). The experimental results demonstrate significant improvement in muscle conditions by comparing the activation of target muscles before and after optimization.

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.

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.

A vision-based cooperative formation control method is proposed in this paper for a heterogeneous unmanned system including an UAV (Unmanned Aerial Vehicle) and multiple UGVs (Unmanned Ground Vehicles). Considering the supervisory role of the UAV and the time-varying relative localization between UAV and UGVs, we aim at controlling the multi-UGVs to a desired formation relying only on the visual information obtained by a camera mounted on the UAV. Meanwhile, the UGV group is driven to track the flying UAV using a feedback control algorithm. A gradient descent-like control scheme which considers the visual sensing range constraint of the camera is thus adopted based on a designed cost function. Finally, the proposed method has been successfully validated through simulations.

We propose a biomimetic approach for steering motion control of a snake robot. Inspired by a vertebrate biological motor system paradigm, a hierarchical control scheme is adopted. In the control scheme, an artificial central pattern generator (CPG) is employed to generate serpentine locomotion in the robot. This generator outputs the coordinated desired joint angle commands to each lower-level effector controller, while the locomotion can be controlled through CPG modulation by a higher-level motion controller. The motion controller consists of a cerebellar model articulation controller (CMAC) and a proportional-derivative (PD) controller. Because of the fast learning ability of the CMAC, the proposed motion controller can drive the robot to track the desired orientation and adapt to unexpected perturbations. The PD controller is employed to expedite the convergence speed of the motion controller. Finally, both numerical studies and experiments proved that the proposed approach can help the snake robot achieve good tracking performance and adaptability in a varying environment.