Jun-Ho Oh

This paper presents LIGHTDOG, a torque-controlled, hydraulically-actuated quadrupedal robot designed for a high power-to-weight ratio and substantial payload capabilities. Hydraulic systems present complexity, weight, and thermal management challenges, which are addressed by embedding all the oil channels inside the robot, inspired by biological vascular structures. This embedding is facilitated by a distinctive robot body design featuring integrated oil channels and the inclusion of a double-vane rotary actuator design, allowing for the internalization of oil channels within the joints. These oil channels not only contribute to the robot’s compactness and lightness, but also allow heat spread throughout the body to dissipate through the robot’s frame, managing heat during robot motion without the need for external radiators. The rotary actuator is equipped with a structure designed to reduce internal leakage and friction torque, which can reduce energy losses in the hydraulic system. Optimization methods were applied to the slider-crank mechanism and hydraulic actuator sizing to reduce lateral and axial forces, as well as the energy consumed by the hydraulic actuators. Our work has validated the feasibility and payload capacity of LIGHTDOG through several experiments. LIGHTDOG, weighing 45 kg, demonstrates a payload capacity of 130 kg during a squatting motion, significantly exceeding its own weight.

Hydraulic actuating system exhibits potential for legged robots to achieve highly agile dynamic movement because of their high power-to-weight ratio. However, the low energy efficiency of the hydraulic system can reduce the operating time and cause heat dissipation. In this article, an optimal control framework based on the model predictive control (MPC) is proposed to improve energy efficiency and provide robust supply of pressure required for robot tasks. The MPC includes the power loss function and supply pressure regularization term as the cost function and limitation on the pump speed and acceleration as the constraints. When implemented to the hydraulic biped robot, LIGHT, the proposed method allows legged robots to achieve the commanded motion without losing balance while minimizing energy consumption. The energy saving performance of the proposed method with MPC is also validated via simulation and experiment.

This letter presents a state estimation algorithm for the legged robot by defining the problem as a Maximum A Posteriori (MAP) estimation problem and solving the problem with the Gauss-Newton algorithm. Moreover, marginalization by the Schur Complement method is adopted to make a fixed size problem. Each component of the cost function and its Jacobian are derived utilizing the SO(3) manifold structure, while we reparameterize the state with nominal state and variation to make linear algebra and vector calculus applied properly. Furthermore, a slip rejection method is proposed to reduce the erroneous effect of fault modeling of kinematics models. The proposed algorithm is verified by comparison with the Invariant Extended Kalman Filter (IEKF) in real robot experiments on various environments.

To achieve human-level object manipulation capability, a robot must be able to handle objects not only with prehensile manipulation, such as pick-and-place, but also with nonprehensile manipulation. To study nonprehensile manipulation, we studied robotic batting, a primitive form of nonprehensile manipulation. Batting is a challenging research area because it requires sophisticated and fast manipulation of moving objects and requires considerable improvement. In this paper, we designed a batting system for dynamic manipulation of a moving ball and proposed several algorithms to improve the task performance of batting. To improve the recognition accuracy of the ball, we proposed a circle-fitting method that complements color segmentation. This method enabled robust ball recognition against illumination. To accurately estimate the trajectory of the recognized ball, weighted least-squares regression considering the accuracy according to the distance of a stereo vision sensor was used for trajectory estimation, which enabled more accurate and faster trajectory estimation of the ball. Further, we analyzed the factors influencing the success rate of ball direction control and applied a constant posture control method to improve the success rate. Through the proposed methods, the ball direction control performance is improved.

In this study, a learning-based force controller for a hydraulic actuator is presented. We propose a control method with an inverse model composed of a deep neural network, which accurately tracks a force trajectory. This learning-based controller can be trained offline using force and position data sets from the hydraulic actuator. The methodology for training the controller network and the experimental setup for data collection are proposed. The learning-based controller was implemented on a hydraulic actuator hardware platform. The proposed learning-based controller demonstrates improved tracking performance compared to that of conventional model-based adaptive control methods.

This letter proposes a design method of a compact embedded hydraulic power unit (HPU) for a bipedal robot and a controller to regulate the supply pressure. The HPU consists of an integrated pump-motor unit. The unit is immersed in hydraulic oil for efficient space utilization and heat dissipation from the motor. This HPU design is analyzed to establish a relationship between the thermal variables and the motor design parameters such as gap radius and wire radius via its thermal and electrical modeling. Through this analysis, the design parameters of suitable pump-driving motor are chosen. This letter also proposes a control method for the HPU to regulate the supply pressure while minimizing the energy loss caused by the bypass through a pressure-regulating valve. The HPU is mounted on top of the bipedal robot platform, LIGHT, with twelve degrees of freedom actuated by the proposed HPU. Finally, the durability of the designed HPU is demonstrated through a long-term driving test at a high pressure. Furthermore, air-walking and squat motion experiments are conducted with the bipedal robot to demonstrate the capabilities of the HPU and its controller.

We propose a delayed reference generation method for a humanoid to stably imitate human walking. An admissible condition for a robust imitation is first defined from the state of the robot and the target support polygon. In addition, a motion buffer is proposed and implemented to apply a time delay to the human motion according to the abovementioned condition for use as a reference to the robot. A data acquisition device and the HUBO2+ humanoid platform were used in teleoperation experiments to verify the effectiveness of the proposed method. We confirmed that the robot could stably imitate human footsteps under either static or dynamic walking when applying the proposed method.

This paper discusses the development of robot motion generation interface between a real-time software architecture and a non-real-time robot operating system. In order for robots to execute intelligent manipulation or navigation, close integration of high-level perception and low-level control is required. However, many available open-source perception modules are developed in ROS, which operates on Linux OS that don't guarantee RT performance. This can lead to non-deterministic responses and stability problems that can adversely affect robot control. As a result, many robotic systems devote RTOS for low-level motion control. Similarly, the humanoid robot platform developed at KAIST, Hubo, utilizes a custom real-time software framework called PODO. Although PODO provides easy interface for motion generation, it lacks interface to high-level frameworks such as ROS. As such, we present a new motion generation interface between ROS and PODO that enables users to generate motion trajectories through standard ROS messages while leveraging a real-time motion controller. With the proposed communication interface, we demonstrate series of manipulator tasks on the actual wheeled humanoid platform, M-Hubo. The overall communication interface responsiveness was at most 27 milliseconds.