Model-based controllers using a linearized model around the system’s equilibrium point is a common approach in the control of a wheeled humanoid due to their less computational load and ease of stability analysis. However, controlling a wheeled humanoid robot while it lifts an unknown object presents significant challenges, primarily due to the lack of knowledge in object dynamics. This paper presents a framework designed for predicting the new equilibrium point explicitly to control a wheeled-legged robot with unknown dynamics. We estimated the total mass and center of mass of the system from its response to initially unknown dynamics, then calculated the new equilibrium point accordingly. To avoid using additional sensors (e.g., force torque sensor) and reduce the effort of obtaining expensive real data, a data-driven approach is utilized with a novel real-to-sim adaptation. A more accurate nonlinear dynamics model, offering a closer representation of real-world physics, is injected into a rigid-body simulation for real-to-sim adaptation. The nonlinear dynamics model parameters were optimized using Particle Swarm Optimization. The efficacy of this framework was validated on a physical wheeled inverted pendulum, a simplified model of a wheeled-legged robot. The experimental results indicate that employing a more precise analytical model with optimized parameters significantly reduces the gap between simulation and reality, thus improving the efficiency of a model-based controller in controlling a wheeled robot with unknown dynamics.

For humanoids to be deployed in demanding situations, such as search and rescue, highly intelligent decision making and proficient sensorimotor skill is expected. A promising solution is to leverage human prowess by interconnecting robot and human via teleoperation. Towards creating seamless operation, this paper presents a dynamic telelocomotion framework that synchronizes the gait of a human pilot with the walking of a bipedal robot. First, we introduce a method to generate a virtual human walking model from the stepping behavior of a human pilot which serves as a reference for the robot to walk. Second, the dynamics of the walking reference and robot walking are synchronized by applying forces to the human pilot and the robot to achieve dynamic similarity between the two systems. This enables the human pilot to continuously perceive and cancel any asynchrony between the walking reference and robot. A consistent step placement strategy for the robot is derived to maintain dynamic similarity through step transitions. Using our human-machine-interface, we demonstrate that the human pilot can achieve stable and synchronous teleoperation of a simulated robot through stepping-in-place, walking, and disturbance rejection experiments. This work provides a fundamental step towards transferring human intelligence and reflexes to humanoid robots.

Industrial manipulators do not collapse under their own weight when powered off due to the friction in their joints. Although these mechanism are effective for stiff position control of pick-and-place, they are inappropriate for legged robots that must rapidly regulate compliant interactions with the environment. However, no metric exists to quantify the robot’s performance degradation due to mechanical losses in the actuators and transmissions. This paper provides a fundamental formulation that uses the mechanical efficiency of transmissions to quantify the effect of power losses in the mechanical transmissions on the dynamics of a whole robotic system. We quantitatively demonstrate the intuitive fact that the apparent inertia of the robots increase in the presence of joint friction. We also show that robots that employ high gear ratio and low efficiency transmissions can statically sustain more substantial external loads. We expect that the framework presented here will provide the fundamental tools for designing the next generation of legged robots that can effectively interact with the world.

This paper introduces HOPPY, an open-source, low-cost, robust, and modular kit for robotics education. The robot dynamically hops around a rotating gantry with a fixed base. The kit is intended to lower the entry barrier for studying dynamic robots and legged locomotion with real systems. It bridges the theoretical content of fundamental robotic courses with real dynamic robots by facilitating and guiding the software and hardware integration. This paper describes the topics which can be studied using the kit, lists its components, discusses preferred practices for implementation, presents results from experiments with the simulator and the real system, and suggests further improvements. A simple heuristic-based controller is described to achieve velocities up to 1.7m/s, navigate small objects, and mitigate external disturbances when the robot is aided by a counterweight. HOPPY was utilized as the subject of a semester-long project for the Robot Dynamics and Control course at the University of Illinois at Urbana-Champaign. The positive feedback from the students and instructors about the hands-on activities during the course motivates us to share this kit and continue improving it in the future.

In this paper, we propose a new methodology to embed deep learning-based algorithms in both visual recognition and motion planning for general mobile robotic platforms. A framework for an asynchronous deep classification network is introduced to integrate heavy deep classification networks into a mobile robot with no loss of system bandwidth. Moreover, a gaming reinforcement learning-based motion planner, a novel and convenient embodiment of reinforcement learning, is introduced for simple implementation and high applicability. The proposed approaches are implemented and evaluated on a developed robot, TT2-bot. The evaluation was based on a mission devised for a qualitative evaluation of the general purposes and performances of a mobile robotic platform. The robot was required to recognize targets with a deep classifier and plan the path effectively using a deep motion planner. As a result, the robot verified that the proposed approaches successfully integrate deep learning technologies on the stand-alone mobile robot. The embedded neural networks for recognition and path planning were critical components for the robot.