In this paper, we examine the problem of robotic manipulation of granular media. We evaluate multiple predictive models used to infer the dynamics of scooping and dumping actions. These models are evaluated on a task that involves manipulating the media in order to deform it into a desired shape. Our best performing model is based on a highly-tailored convolutional network architecture with domain-specific optimizations, which we show accurately models the physical interaction of the robotic scoop with the underlying media. We empirically demonstrate that explicitly predicting physical mechanics results in a policy that out-performs both a hand-crafted dynamics baseline, and a "value-network", which must otherwise implicitly predict the same mechanics in order to produce accurate value estimates.
In recent years, there has been some work in robotics in areas related to interaction and manipulation of granular media. For example, there has been a significant amount of work on legged locomotion over granular media @cite_22 @cite_1 @cite_0 . There has also been work on automated operation of construction equipment for scooping @cite_9 @cite_19 @cite_32 @cite_14 . Additionally, much of the work related to robotic pouring has utilized granular media rather than liquids @cite_24 @cite_27 @cite_12 @cite_4 @cite_25 . Recent work by Xu and Cakmak @cite_20 explored the ability of robots to clean, among other things, granular media from surfaces using a low-cost tool. In contrast to this prior work, here we directly tackle the problem of manipulating granular media in a robust, flexible manner.
@cite_14 @cite_4 @cite_22 @cite_9 @cite_1 @cite_32 @cite_0 @cite_19 @cite_24 @cite_27 @cite_20 @cite_25 @cite_12
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As a part of research on autonomous loading system by wheel loader, a method for appropriate arrangement of bucket trajectory for scooping motion will be proposed. In this paper, relation between resistance force and advancing direction of the bucket is analyzed theoretically. Advancing direction is dominant factor for resistance force on the bucket during scooping. Based on this analysis, simple rules for bucket trajectory arrangement are proposed. In scooping procedure, scooped volume is estimated using 3D model obtained with stereo-vision system. The shape of the pile is measured and converted into pile model prior to scooping. The developed method and system are installed on experimental scale model of wheel loader. The results show good performance for different condition of pile. We explore differential dynamic programming for dynamical systems that form a directed graph structure. This planning method is applicable to complicated tasks where sub-tasks are sequentially connected and different skills are selected according to the situation. A pouring task is an example: it involves grasping and moving a container, and selection of skills, e.g. tipping and shaking. Our method can handle these situations; we plan the continuous parameters of each subtask and skill, as well as select skills. Our method is based on stochastic differential dynamic programming. We use stochastic neural networks to learn dynamical systems when they are unknown. Our method is a form of reinforcement learning. On the other hand, we use ideas from artificial intelligence, such as graph-structured dynamical systems, and frame-and-slots to represent a large state-action vector. This work is a partial unification of these different fields. We demonstrate our method in a simulated pouring task, where we show that our method generalizes over material property and container shape. Accompanying video: https: youtu.be _ECmnG2BLE8. Legged locomotion on flowing ground (e.g., granular media) is unlike locomotion on hard ground because feet experience both solid- and fluid-like forces during surface penetration. Recent bioinspired legged robots display speed relative to body size on hard ground comparable with high-performing organisms like cockroaches but suffer significant performance loss on flowing materials like sand. In laboratory experiments, we study the performance (speed) of a small (2.3 kg) 6-legged robot, SandBot, as it runs on a bed of granular media (1-mm poppy seeds). For an alternating tripod gait on the granular bed, standard gait control parameters achieve speeds at best 2 orders of magnitude smaller than the 2 body lengths s (≈60 cm s) for motion on hard ground. However, empirical adjustment of these control parameters away from the hard ground settings restores good performance, yielding top speeds of 30 cm s. Robot speed depends sensitively on the packing fraction φ and the limb frequency ω, and a dramatic transition from rotary walking to slow swimming occurs when φ becomes small enough and or ω large enough. We propose a kinematic model of the rotary walking mode based on generic features of penetration and slip of a curved limb in granular media. The model captures the dependence of robot speed on limb frequency and the transition between walking and swimming modes but highlights the need for a deeper understanding of the physics of granular media. Mining operations have great potential for automation, from a worker safety viewpoint as well as productivity and efficiency. Among the operations that can be automated are loading, guiding, and unloading of LHD loaders. This paper concerns automatic loading of these units, based on modeling a loader as a robot manipulator. For this purpose, analysis of the forces concerned in the process of scooping, and information about the kinematics of motion become essential. This work is about the trajectory of motion during scooping. Based an the results of a preliminary study on the nature of the forces involved, and bearing in mind the preference for simplicity in the control action, an easy to follow trajectory is determined for the cutting edge of the bucket of a loader, which is regarded as the end-effector in a robot manipulator. The way to find a minimum energy consuming trajectory for each individual bucket and for a particular material to be loaded is discussed. Since following this trajectory is more efficient it can be used for faster execution of bucket loading, if desired. > Achieving effective locomotion on diverse terrestrial substrates can require subtle changes of limb kinematics. Biologically inspired legged robots (physical models of organisms) have shown impressive mobility on hard ground but suffer performance loss on unconsolidated granular materials like sand. Because comprehensive limb–ground interaction models are lacking, optimal gaits on complex yielding terrain have been determined empirically. To develop predictive models for legged devices and to provide hypotheses for biological locomotors, we systematically study the performance of SandBot, a small legged robot, on granular media as a function of gait parameters. High performance occurs only in a small region of parameter space. A previously introduced kinematic model of the robot combined with a new anisotropic granular penetration force law predicts the speed. Performance on granular media is maximized when gait parameters utilize solidification features of the granular medium and minimize limb interference. A controller for a miniature wheel loader is developed to scoop rock piles autonomously. During a scooping task operation, the load of the bucket varies momentarily according to the phase of scooping. Before the insertion of the bucket into the rock pile, the load is just the bucket weight, but after the insertion the reaction force from the rock piles is applied to the bucket. The values of the reaction forces changes significantly and they can not be identified in advance. To achieve autonomous loading, it is required for the bucket controller to work stably in both cases with the same algorithm. Therefore, a disturbance observer mechanism is installed into a miniature wheel loader "Yamazumi 3" to adapt various amounts of loads. Its effectiveness is verified by fundamental experiments. The theories of aero- and hydrodynamics predict animal movement and device design in air and water through the computation of lift, drag, and thrust forces. Although models of terrestrial legged locomotion have focused on interactions with solid ground, many animals move on substrates that flow in response to intrusion. However, locomotor-ground interaction models on such flowable ground are often unavailable. We developed a force model for arbitrarily-shaped legs and bodies moving freely in granular media, and used this “terradynamics” to predict a small legged robot’s locomotion on granular media using various leg shapes and stride frequencies. Our study reveals a complex but generic dependence of stresses in granular media on intruder depth, orientation, and movement direction and gives insight into the effects of leg morphology and kinematics on movement. There is a demand of mining automation by heavy equipment in an open mine. Authors aim to automization of motion for wheel loader in scooping and loading operation. In this paper, we applied the optimization method by Genetic Algorithm to the path planning for the wheel loader on scooping and loading operation. We showed the quasi-optimized path and demonstrated by the miniature wheel loader robot. Robot learning from demonstration faces new challenges when applied to tasks in which forces play a key role. Pouring liquid from a bottle into a glass is one such task, where not just a motion with a certain force profile needs to be learned, but the motion is subtly conditioned by the amount of liquid in the bottle. In this paper, the pouring skill is taught to a robot as follows. In a training phase, the human teleoperates the robot using a haptic device, and data from the demonstrations are statistically encoded by a parametric hidden Markov model, which compactly encapsulates the relation between the task parameter (dependent on the bottle weight) and the force-torque traces. Gaussian mixture regression is then used at the reproduction stage for retrieving the suitable robot actions based on the force perceptions. Computational and experimental results show that the robot is able to learn to pour drinks using the proposed framework, outperforming other approaches such as the classical hidden Markov models in that it requires less training, yields more compact encodings and shows better generalization capabilities. Programming new skills on a robot should take minimal time and effort. One approach to achieve this goal is to allow the robot to ask questions. This idea, called Active Learning, has recently caught a lot of attention in the robotics community. However, it has not been explored from a human-robot interaction perspective. In this paper, we identify three types of questions (label, demonstration and feature queries) and discuss how a robot can use these while learning new skills. Then, we present an experiment on human question asking which characterizes the extent to which humans use these question types. Finally, we evaluate the three question types within a human-robot teaching interaction. We investigate the ease with which different types of questions are answered and whether or not there is a general preference of one type of question over another. Based on our findings from both experiments we provide guidelines for designing question asking behaviors on a robot learner. Robots that can reliably manipulate human tools can do a diverse range of useful tasks in human environments. However, these tools are often difficult to manipulate, particularly given force requirements for applying the tool. This is often due to the mismatch between the robot's gripper and the tool handle designed for human hands. In this paper, we present the design of a low-cost universal tool attachment that makes the tool gripper-friendly. We demonstrate the performance gain provided by the attachment on 10 different tools in the three stages of tool use: grasping the tool, applying the tool, and placing the tool. Our experiments demonstrate that the attachment performs significantly better in all three stages of tool use. We explore a model-based approach to reinforcement learning where partially or totally unknown dynamics are learned and explicit planning is performed. We learn dynamics with neural networks, and plan behaviors with differential dynamic programming (DDP). In order to handle complicated dynamics, such as manipulating liquids (pouring), we consider temporally decomposed dynamics. We start from our recent work [1] where we used locally weighted regression (LWR) to model dynamics. The major contribution of this paper is making use of deep learning in the form of neural networks with stochastic DDP, and showing the advantages of neural networks over LWR. For this purpose, we extend neural networks for: (1) modeling prediction error and output noise, (2) computing an output probability distribution for a given input distribution, and (3) computing gradients of output expectation with respect to an input. Since neural networks have nonlinear activation functions, these extensions were not easy. We provide an analytic solution for these extensions using some simplifying assumptions. We verified this method in pouring simulation experiments. The learning performance with neural networks was better than that of LWR. The amount of spilled materials was reduced. We also present early results of robot experiments using a PR2. Accompanying video: https: youtu.be aM3hE1J5W98 We explore a temporal decomposition of dynamics in order to enhance policy learning with unknown dynamics. There are model-free methods and model-based methods for policy learning with unknown dynamics, but both approaches have problems: in general, model-free methods have less generalization ability, while model-based methods are often limited by the assumed model structure or need to gather many samples to make models. We consider a temporal decomposition of dynamics to make learning models easier. To obtain a policy, we apply differential dynamic programming (DDP). A feature of our method is that we consider decomposed dynamics even when there is no action to be taken, which allows us to decompose dynamics more flexibly. Consequently learned dynamics become more accurate. Our DDP is a first-order gradient descent algorithm with a stochastic evaluation function. In DDP with learned models, typically there are many local maxima. In order to avoid them, we consider multiple criteria evaluation functions. In addition to the stochastic evaluation function, we use a reference value function. This method was verified with pouring simulation experiments where we created complicated dynamics. The results show that we can optimize actions with DDP while learning dynamics models.