In this paper, a learning-based whole-body loco-motion controller is proposed, which enables quadruped robots to perform running in the style of real animals. We use a low-level controller based on multi-rigid body dynamics to calculate desired torques for each joint, while the high-level neural network policy planning the expected gait and foothold. The policy is trained with reinforcement learning, so that the robot can track a variety of trajectories according to the gait patterns recorded from real-world dogs. We transfer the walking and running gait style to quadrupeds in simulation, involving pace, trot, high-speed gallop and natural transitions. The performance is evaluated by the synchronization rate of contact state between the policy result and the recorded sequence. In the experiments, the robot runs steadily at a speed of 2 m/s and showcases a notable synchronization rate of about 80%. Without prior knowledge, the policy demonstrates a realistic foothold distribution that covers the central area of the torso, which is prevalent in running animals.

In this paper, nonlinear control techniques are exploited to balance an unmanned bicycle with enlarged stability domain. We consider two cases. For the first case when the autonomous bicycle is balanced by the flywheel, the steering angle is set to zero, and the torque of the flywheel is used as the control input. The controller is designed based on the Interconnection and Damping Assignment Passivity Based Control (IDA-PBC) method. For the second case when the bicycle is balanced by the handlebar, the bicycle's velocity is high, and the flywheel is turned off. The angular velocity of the handlebar is used as the control input and the balance controller is designed based on feedback linearization. In these cases, the global stability of the closed-loop unmanned bicycle is theoretically proved based on Lyapunov theory. The experiments are conducted to validate the efficacy of the proposed nonlinear balance controllers.

In this paper, the gain scheduling technique is applied to design a balance controller for an autonomous bicycle with an inertia wheel. Previously, two different balance controllers are needed depending on whether the bicycle is stationary or dynamic. The switch between the two different controllers may cause the instability of the autonomous bicycle. Our proposed gain scheduled controller can balance the autonomous bicycle in both stationary and dynamic cases. A physical system is built and experiments are carried out to demonstrate the effectiveness of the gain scheduled controller.

External wrench estimation is very helpful for aerial exploration and manipulation tasks. During the exploration, there might be unseen obstacles to cause dangerous collisions. The estimation of the external force and torque is also beneficial in aerial manipulation tasks. In this paper, we present a framework of estimating the external wrench for the aerial multilink robot based on the onboard inertial measurement unit (IMU) sensors, joints state and robot dynamic models. Compared to the conventional multirotor robot, the center of mass (CoM) is always changing when the robot transforms. The sensor could not be attached to CoM to observe the acceleration data. Consequently, we present a novel method by applying a distributed IMU system to estimate the CoM linear and angular accelerations for the external wrench estimation. With the help of the robot model, the position of the contact point could be estimated, which is useful in exploring tasks to safely interact with the physical world. We design the contact-aided navigation strategy and computationally efficient motion primitives library to help our robot react to the unexpected collision. We experimentally validate our framework with a two-dimensional multilink aerial robot to show the results of external wrench estimator and its further applications2.2Experiment video: https://youtu.be/R-WDReLnWWI

Transformable aerial robots are favorable in aerial manipulation tasks for their flexible ability to change configuration during the flight. By assuming robot keeping in the mild motion, the previous researches sacrifice aerial agility to simplify the complex non-linear system into a single rigid body with a linear controller. In this paper, we present a framework towards agile swing motion for the transformable multi-links aerial robot. We introduce a computational-efficient non-linear model predictive controller and joints motion primitive frame-work to achieve agile transforming motions and validate with a novel robot named HYRURS-X. Finally, we implement our framework under a table tennis task to validate the online and agile performance.Supplementary MaterialThis paper is accompanied by a experiment video: http://www.jsk.t.u-tokyo.ac.jp/%7eshifan/paper/iros19/video.mp4.

For a robot to manipulate an object, it has to understand the functions and the actions that can be subjected to the object. This set of information is known as affordance of the object. Affordances are generally defined by the geometrical structures and physical properties of the objects. In this paper, we present an affordance detection network (ADNet) for detecting object affordances using multimodal input i.e., RGB-D data. The method is based on the state-of-the-art fully convolutional network with two encoding streams and one decoding stream. In the presented formulation, the network learns powerful discriminative features independently from the RGB and depth images, which enables it to abstract rich photometrical and geometrical properties of the objects. The multimodal encoding is combined at multiple stages of the network using the late-fusion strategy and used is for predicting the potential affordances of the objects.

In this paper, we introduce the achievement of the flight motion to pass through small opening by the multilinked and transformable aerial robot. Previous works about such motion are based on under-actuated multirotors, indicating that aggressive maneuvering is necessary condition. This involves two crucial problems: i) enough free space for deceleration is necessary, otherwise the robot would collide with unknown obstacle after exiting opening; ii) the multirotor can not traverse the openings that are smaller than the robot body. The proposed transformable aerial robot in our work can solve these problems, since the multilinked model can not only guarantee the near-hover condition during the whole motion sequence, but also slowly traverse relative small openings by changing its form like a snake. We first propose an improved dynamics derivation and flight control method for this multilinked aerial robot based on our previous work. Then, we present the path planning method which takes the flight stability in the near-hover condition into account. Finally we demonstrate the experimental results of the motion to pass through a horizontal and small opening which also involves the borders (the floor and the ceiling).

In this paper, we introduce the achievement of the aerial manipulation by using the whole body of a transformable aerial robot, instead of attaching an additional manipulator. The aerial robot in our work is composed by two-dimensional multilinks which enable a stable aerial transformation and can be employed as an entire gripper. We propose a planning method to find the optimized grasping form for the multilinks while they are on the air, which is based on the original planar enveloping algorithm, along with the optimization of the internal force and joint torque for the force-closure. We then propose the aerial approach and grasp motion strategy, which is devoted to the determination of the form and position of the aerial robot to approach and grasp effectively the object from the air. Finally we present the experimental results of the aerial manipulation which involves grasping, carrying and dropping different types of object. These results validate the performance of aerial grasping based on our proposed whole-body grasp planning and motion control method.

In this paper, we show the achievement of a transformable aerial robot with internal communication system for multiple objects transportation. As it is not easy to make the flight endurance of an aerial robot longer, we study the problem to transport multiple objects at the same time to improve the efficiency of transportation. However, for conventional aerial robots, multiple objects transportation is difficult because the CoG position changes when the number of grasped objects changes, resulting in the instability of the flight. Therefore, to solve this problem, we focus on the multirotor with two-dimensional multilinks proposed in our previous work, which possesses the ability to modify the CoG position actively and can keep the flight stable. First, we introduce the hardware platform including the structure of link module and internal communication system to achieve the extensibility in terms of the link number. We then propose a method to find the optimal form for the multilinks based on the flight stability. Finally, we present experimental results which include aerial transformation and multiple objects transportation.

In recent years, applications of visual tracking algorithms has seen a substantial growth with deployments in intelligent robots such as drones for human tracking. The algorithms for such tasks has to be efficient in terms of computational cost while been robust, accurate and fast. Object tracking algorithms based on handcrafted heuristics and constraints are widely used in uav applications. The handcrafted heuristics are mostly implemented for task-oriented applications which limits the extensions in uav's capability beyond the predefined functions. This paper considers the challenges of tracking and landing an autonomous uav on a speed high moving target, and presents a visual tracking algorithm that integrates correlation filters with deep comparison network for real-time tracking with state-of-the-art accuracy. The method first tracks the target upto translation using an online learnt model via local search technique. The changes in scale is estimated by a deep comparison network (DCN) instead of the commonly used pyramidal approach. In a single network evaluation, DCN can estimate the changes in scale as well as compensate the drifting of the tracker by refining the object region estimated by the correlation filters. The network is end-to-end trained which attempts to learn a powerful matching function for object localization using a known template. Generally, the integrated framework can be viewed as coarse-to-fine level motion estimation. Moreover, the framework can redetect the lost target without a need for a separate detector.

To enable robotic sensing for tasks with requirements on weight, size, and cost, we develop an ultra-tiny line laser range sensor based on the Time-of-Flight (TOF) principle. With delicate circuit design and optical attachments, we create a sensor as small as 35[mm] × 27[mm] × 30[mm] and as light as 20[g]. The line sensor samples 272 pixels (256 effective pixels) uniformly distributed within the measurement field of view customizable using different laser lenses. The optimal measurement range of the sensor is 0.05[m] ~ 2[m]. Higher sampling rates can be achieved with a shorter range. The sensor can also extend its range to 3[m] with reduced accuracy. We model the overall errors of the sensor and formulate calibration methods, achieving repeatable accuracy and measurement bias both within 2[cm] with our tested ambient lighting conditions and measurement ranges. The sensor is applicable to range sensing tasks including humanoid hand-eye measurement, UAV safe landing, tiny robot range sensing, and object detection.

We present a vision cognition framework for tomato harvesting humanoid robot based on geometrical and physical reasoning. Inspired from the natural human harvesting behaviour, our goal is to build a humanoid robot to pick tomatoes autonomously or with minimal human efforts. The proposed vision approach uses fusion of calibrated observation data from two RGB-D sensors installed on the head and the hand of the humanoid. We observe the natural human harvesting behaviour and equip our robot with similar grippers to follow the same picking processes for a specific fruit. In the vision approach, we mainly focus on modelling fruits in one branch and then estimating the pedicel direction of each fruit in a branch. Through pointcloud model segmentation, the primitive shape model of each fruit can be obtained and we consider a simple fact that crops in one branch should remain stable with respect to gravity and interaction forces from neighbouring crops in the branch. According to this assumption, a probabilistic model is created and the picking order in the branch is assigned under the evaluated geometrical structure. In the experiments, we tested harvesting of real tomatoes on actual branches and evaluated the successful harvesting rate.