Humanoid robots require both robust lower-body locomotion and precise upper-body manipulation. While recent Reinforcement Learning (RL) approaches provide whole-body loco-manipulation policies, they lack precise manipulation with high DoF arms. In this paper, we propose decoupling upper-body control from locomotion, using inverse kinematics (IK) and motion retargeting for precise manipulation, while RL focuses on robust lower-body locomotion. We introduce PMP (Predictive Motion Priors), trained with Conditional Variational Autoencoder (CVAE) to effectively represent upper-body motions. The locomotion policy is trained conditioned on this upper-body motion representation, ensuring that the system re-mains robust with both manipulation and locomotion. We show that CVAE features are crucial for stability and robustness, and significantly outperforms RL-based whole-body control in precise manipulation. With precise upper-body motion and robust lower-body locomotion control, operators can remotely control the humanoid to walk around and explore different environments, while performing diverse manipulation tasks.

In this paper, we present a heterogeneous robot swarm system that can physically couple with each other to form functional structures and dynamically decouple to perform individual tasks. The connection between robots can be formed with a passive coupling mechanism, ensuring minimum energy consumption during coupling and decoupling behavior. The heterogeneity of the system enables the robots to perform structural enhancement configurations based on specific environmental requirements. We propose a connection-pair oriented configuration control algorithm to form different assemblies. We show experiments of up to nine robots performing the coupling, gap-crossing, and decoupling behaviors.

Nowadays, precise train parking has become a key technology of automatic train operation. Due to the high nonlinearities and various uncertainties existing in the dynamics of urban rail trains (URTs), it is challenging to develop an effective parking controller to achieve high-precision parking. To address this issue, a novel robust adaptive backstepping controller is proposed in the paper, where a robust term is employed to compensate for the system uncertainties caused by the brake shoe, and meanwhile several parametric adaption laws are equipped to estimate the uncertain system parameters as well as the external disturbances. The closed-loop stability of the controlled system is analyzed rigorously by virtue of the Lyapunov theory, and the effectiveness of the proposed control scheme in precise parking of URTs is demonstrated through numerical simulations.

Multi-robot systems can perform task-related collaborative behaviors while maintaining connectivity within the system. However, some robots may fail to execute tasks or converge relatively slowly due to connectivity constraints. We consider the case that some robots may not have tasks assigned at a certain time frame, and they may help the task robots to achieve their goals by forming a connectivity graph with flexible topology. Therefore, we introduce a topology correction controller to provide flexibility for the task robots to perform task behaviors by modifying the topology of the connectivity graph for a faster convergence rate. We propose a distributed approach of blending weighted rendezvous and weighted flocking to form the correction controller. We prove that this scheme can guarantee a faster convergence rate and provide flexible connectivity graph topology. We then present our result of a system of up to thirty robots in various cluttered environments and show that our approach of behavior combination is robust and scalable.

Robot swarms have been shown to improve the ability of individual robots by inter-robot collaboration. In this paper, we present the PuzzleBots - a low-cost robotic swarm system where robots can physically couple with each other to form functional structures with minimum energy consumption while maintaining individual mobility to navigate within the environment. Each robot has knobs and holes along the sides of its body so that the robots can couple by inserting the knobs into the holes. We present the characterization of knob design and the result of gap-crossing behavior with up to nine robots. We show with hardware experiments that the robots are able to couple with each other to cross gaps and decouple to perform individual tasks. We anticipate the PuzzleBots will be useful in unstructured environments as individuals and coupled systems in real-world applications.

In many cases the multi-robot systems are desired to execute simultaneously multiple behaviors with different controllers, and sequences of behaviors in real time, which we call behavior mixing. Behavior mixing is accomplished when different subgroups of the overall robot team change their controllers to collectively achieve given tasks while maintaining connectivity within and across subgroups in one connected communication graph. In this paper, we present a provably minimum connectivity maintenance framework to ensure the subgroups and overall robot team stay connected at all times while providing the highest freedom for behavior mixing. In particular, we propose a real-time distributed Minimum Connectivity Constraint Spanning Tree (MCCST) algorithm to select the minimum inter-robot connectivity constraints preserving subgroup and global connectivity that are least likely to be violated by the original controllers. With the employed safety and connectivity barrier certificates for the activated connectivity constraints and collision avoidance, the behavior mixing controllers are thus minimally modified from the original controllers. We demonstrate the effectiveness and scalability of our approach via simulations of up to 100 robots with multiple behaviors.

Multi-robot systems are widely used in environmental exploration and modeling, especially in hazardous environments. However, different types of robots are limited by different mobility, battery life, sensor type, etc. Heterogeneous robot systems are able to utilize various types of robots and provide solutions where robots are able to compensate each other with their different capabilities. In this paper, we consider the problem of sampling and modeling environmental characteristics with a heterogeneous team of robots. To utilize heterogeneity of the system while remaining computationally tractable, we propose an environmental partitioning approach that leverages various robot capabilities by forming a uniformly defined heterogeneity cost space. We combine with the mixture of Gaussian Processes model-learning framework to adaptively sample and model the environment in an efficient and scalable manner. We demonstrate our algorithm in field experiments with ground and aerial vehicles.