This work introduces a model-free reinforcement learning framework that enables various modes of motion (quadruped, tripod, or biped) and diverse tasks for legged robot locomotion. We employ a motion-style reward based on a relaxed logarithmic barrier function as a soft constraint, to bias the learning process toward the desired motion style, such as gait, foot clearance, joint position, or body height. The predefined gait cycle is encoded in a flexible manner, facilitating gait adjustments throughout the learning process. Extensive experiments demonstrate that KAIST HOUND, a 45 kg robotic system, can achieve biped, tripod, and quadruped locomotion using the proposed framework; quadrupedal capabilities include traversing uneven terrain, galloping at 4.67 m/s, and overcoming obstacles up to 58 cm (67 cm for HOUND2); bipedal capabilities include running at 3.6 m/s, carrying a 7.5 kg object, and ascending stairs-all performed without exteroceptive input.

This paper introduces a design method for an efficient and agile quadruped robot. A mixed-integer optimization formulation including the number of gear teeth is derived to obtain the optimal gear ratio that minimizes cost for a running-trot with the target speed of 3 m/s. With the inclusion of integer constraints related to the number of gear teeth, detailed design considerations of gear trains can be included in the optimization process. Thermal dissipation of the motor controller is also taken into account in the optimization to consider heat generation during high-speed running. KAIST Hound, a 45 kg robot, designed with the obtained design parameters has successfully demonstrated a 3 m/s running-trot using a nonlinear model predictive controller (NMPC). Furthermore, the robot has proved its robustness by the demonstration of additional experiments such as 22° slope climbing, 3.2 km walking, and traversing a 35 cm obstacle.

In this work, we propose a novel differential dynamic programming (DDP) framework for systems involving contact with the ground. The approach converts a general constrained differential dynamic programming into contact-implicit one by incorporating contact dynamics in a linear complementarity problem (LCP) formulation. Analytical gradients of the contact dynamics are obtained through a relaxed complementarity condition in the LCP formulation that helps the search directions of optimization avoid stalling in bad local minima or saddle points. Incorporation of contact dynamics and its analytical gradients into DDP enables an online discovery of not only dynamically-feasible trajectories of states, control inputs, and contact forces but also contact mode sequences. We demonstrate that our Contact-Implicit Differential Dynamic Programming framework successfully finds totally new dynamic motions with contact mode sequences in a variety of robotic systems including an one-legged hopping robot and planar quadrupedal robot in simulation environment.

Mobile cloud service is the most important traffic generator today, and high data rate services in this area are increasing. The Virtual Desktop Infrastructure (VDI) is a circuit-like service that is widely deployed owing to its security merits and the resource constraints of mobile devices. However, VDI requires a high and guaranteed virtually constant wireless data rate. Thus, we focus on the problems that have to be resolved in order for IEEE802.21 Media Independent Handover (MIH) to support a guaranteed wireless data rate and also enable it to offer optimized and maximized data rate support for mobile cloud services. We also consider a virtual machine (VM) migration scheme that is tightly coupled to mobile networks and can minimize the user's service delay and reduce the bandwidth requirements of the wired network. We then present a consistent control framework by extending the Global Environment for Network Innovations (GENI) control framework to provide a single integrated control framework for cloud computing and Future Internet compatibility. Finally, we develop an experimental testbed using a mobile cloud computing environment and evaluate the performance of the proposed scheme. We show that the proposed method can enhance the Quality of Experience (QoE) of mobile cloud computing services with increased data rate support and a single consistent control framework that is compatible with the GENI-based Future Internet architecture. The results of evaluations conducted indicate that our proposed method provides 24%–41% better data rate compared to conventional methods.