Tao Geng

Fast Biped Walking with a Sensor-driven Neuronal Controller and Real-time Online Learning

In this paper, we present our design and experiments on a planar biped robot under the control of a pure sensor-driven controller. This design has some special mechanical features, for example small curved feet allowing rolling action and a properly positioned center of mass, that facilitate fast walking through exploitation of the robots natural dynamics. Our sensor-driven controller is built with biologically inspired sensor- and motor-neuron models, and does not employ any kind of position or trajectory tracking control algorithm. Instead, it allows our biped robot to exploit its own natural dynamics during critical stages of its walking gait cycle. Due to the interaction between the sensor-driven neuronal controller and the properly designed mechanics of the robot, the biped robot can realize stable dynamic walking gaits in a large domain of the neuronal parameters. In addition, this structure allows the use of a policy gradient reinforcement learning algorithm to tune the parameters of the sensor-driven controller in real-time, during walking. This way RunBot can reach a relative speed of 3.5 leg lengths per second after only a few minutes of online learning, which is faster than that of any other biped robot, and is also comparable to the fastest relative speed of human walking.

Adaptive, fast walking in a biped robot under neuronal control and learning

Human walking is a dynamic, partly self-stabilizing process relying on the interaction of the biomechanical design with its neuronal control. The coordination of this process is a very difficult problem, and it has been suggested that it involves a hierarchy of levels, where the lower ones, e.g., interactions between muscles and the spinal cord, are largely autonomous, and where higher level control (e.g., cortical) arises only pointwise, as needed. This requires an architecture of several nested, sensori–motor loops where the walking process provides feedback signals to the walkers sensory systems, which can be used to coordinate its movements. To complicate the situation, at a maximal walking speed of more than four leg-lengths per second, the cycle period available to coordinate all these loops is rather short. In this study we present a planar biped robot, which uses the design principle of nested loops to combine the self-stabilizing properties of its biomechanical design with several levels of neuronal control. Specifically, we show how to adapt control by including online learning mechanisms based on simulated synaptic plasticity. This robot can walk with a high speed (>3.0 leg length/s), self-adapting to minor disturbances, and reacting in a robust way to abruptly induced gait changes. At the same time, it can learn walking on different terrains, requiring only few learning experiences. This study shows that the tight coupling of physical with neuronal control, guided by sensory feedback from the walking pattern itself, combined with synaptic learning may be a way forward to better understand and solve coordination problems in other complex motor tasks.

A Reflexive Neural Network for Dynamic Biped Walking Control

Biped walking remains a difficult problem, and robot models can greatly facilitate our understanding of the underlying biomechanical principles as well as their neuronal control. The goal of this study is to specifically demonstrate that stable biped walking can be achieved by combining the physical properties of the walking robot with a small, reflex-based neuronal network governed mainly by local sensor signals. Building on earlier work (Taga, 1995; Cruse, Kindermann, Schumm, Dean, & Schmitz, 1998), this study shows that human-like gaits emerge without specific position or trajectory control and that the walker is able to compensate small disturbances through its own dynamical properties. The reflexive controller used here has the following characteristics, which are different from earlier approaches: (1) Control is mainly local. Hence, it uses only two signals (anterior extreme angle and ground contact), which operate at the interjoint level. All other signals operate only at single joints. (2) Neither position control nor trajectory tracking control is used. Instead, the approximate nature of the local reflexes on each joint allows the robot mechanics itself (e. g., its passive dynamics) to contribute substantially to the overall gait trajectory computation. (3) The motor control scheme used in the local reflexes of our robot is more straightforward and has more biological plausibility than that of other robots, because the outputs of the motor neurons in our reflexive controller are directly driving the motors of the joints rather than working as references for position or velocity control. As a consequence, the neural controller and the robot mechanics are closely coupled as a neuromechanical system, and this study emphasizes that dynamically stable biped walking gaits emerge from the coupling between neural computation and physical computation. This is demonstrated by different walking experiments using a real robot as well as by a Poincare map analysis applied on a model of the robot in order to assess its stability.

Exploring the dynamic walking range of the biped robot "Run Bot" with an active upper-body component

In this paper, we explore the dynamic walking capability of the planar biped robot RunBot with a now added active upper-body component. Originally, the robot was designed and built to perform fast walking especially on a flat floor. Its locomotion is driven by so-called neural reflexive control. This controller does not employ any kind of position or trajectory-tracking control algorithm. Instead, it enables RunBot to exploit its own natural dynamics during critical stages of its gait cycles. The actual gait pattern is determined by the set of neural control parameters, like gain and activation thresholds. Thus, different gait patterns can be induced by changing these parameters. These walking patterns, cooperating with an added active-upper body component, allow RunBot to walk on different terrains, e.g. flat floor, up and down slopes between 0 and 7.5 degrees. The transition phase of each gait was experimentally tuned. As a result, RunBot can continuously walk on the three different terrains. During walking experiments, gait switching was manually triggered while the active body was controlled to lean either forward or backward according to the slope.

Self-stabilized biped walking under control of a novel reflexive network

Biologically inspired reflexive controllers have been implemented on various walking robots. However, due to the natural instability of biped walking, up to date, there has not existed a biped robot that depends exclusively on reflexive controllers for its dynamically stable walking control. In this paper, we present our design and experiments of a planar biped robot under control of a pure reflexive controller that includes only local extensor and flexor reflexes (no any other reflexes for explicit stability control). The reflexive controller is built with biologically inspired stretch receptors and model neurons. It requires fewer phasic feedbacks than those reflexive controllers of multilegged robots, and does not employ any kind of position or velocity control algorithm even on its low level. Instead, the approximate property of this reflexive controller has allowed our biped robot to substantially exploit its own passive dynamics in some stages of its walking gait cycle. Due to the interaction of the reflexive controller and the properly designed mechanics of the robot, the biped robot works as a closely coupled neuromechanical system, and demonstrates self-stabilizing property in the experiments of slightly perturbed walking, shallow slope walking, and various speed walking. Moreover, our biped robot can walk stably at a relatively high speed (nearly three leg-lengths per second). We know of no other biped robots that could attain such a high relative speed.

Wrist Movement Detector for ROS Based Control of the Robotic Hand

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Using Robot Operating System (ROS) and Single Board Computer to Control Bioloid Robot Motion

This paper presents a research study on the adaptation of a novel technique for placing a programmable component over the structural component of a Robotics Bioloid humanoid robot. Assimilating intelligence plays an important role in the field of robotics that enables a computer to model or replicate some of the intelligent behaviors of human beings but with minimal human intervention. As a part of this effort, this paper revises the Bioloid robot structure to be able to control the robotic movement via a single board computer BeagleBone Black (BBB) and Robot operating system (ROS). ROS as the development framework in conjunction with the main BBB controller that integrates robotic functions is an important aspect of this research, and is a first of its kind approach as far as the authors knowledge. A full ROS computation has been developed by which an API that will be usable by high level software using ROS services has also been developed. The experiments demonstrate that the human like body structure of the Bioloid robot and BeagleBone Black running ROS along with the intellectual components can make the robot walk efficiently.

Correction: Adaptive, Fast Walking in a Biped Robot under Neuronal Control and Learning

doi:10.1371/journal.pcbi.0030134 n nIn Figure 1F, the number 0.24 should be 2.4 instead. The incorrect Froude number given for human walking (0.24) corresponds to 1.5m/s, which is closer to the preferred speed of human walking. The correct number now given (2.4) corresponds to a speed of about 4.6m/s. n nThe incorrect Figure 1 is found at: doi:10.1371/journal.pcbi.0030134.g001.