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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We explore how to represent, plan and learn robot pouring. This is a case study of a complex task that has many variations and involves manipulating non-rigid materials such as liquids and granular substances. Variations of pouring we consider are the type of pouring (such as pouring into a glass or spreading a sauce on an object), material, container shapes, initial poses of containers and target amounts. The robot learns to select appropriate behaviors from a library of skills, such as tipping, shaking and tapping, to pour a range of materials from a variety of containers. The robot also learns to select behavioral parameters. Planning methods are used to adapt skills for some variations such as initial poses of containers. We show using simulation and experiments on a PR2 robot that our pouring behavior model is able to plan and learn to handle a wide variety of pouring tasks. This case study is a step towards enabling humanoid robots to perform tasks of daily living. This paper focuses on improving performance with practice for tasks that are difficult to model or plan, such as pouring (manipulating a liquid or granular material such as sugar). We are also interested in tasks that involve the possible use of many skills, such as pouring by tipping, shaking, and tapping. Although our ultimate goal is to learn and optimize skills automatically from demonstration and practice, in this paper, we explore manually obtaining skills from human demonstration, and automatically selecting skills and optimizing continuous parameters for these skills. Behaviors such as pouring, shaking, and tapping are modeled with finite state machines. We unify the pouring and the two shaking skills as a general pouring model. The constructed models are verified by implementing them on a PR2 robot. The robot experiments demonstrate that our approach is able to appropriately generalize knowledge about different pouring skills and optimize behavior parameters. Robotic assistants have the potential to greatly improve our quality of life by supporting us in our daily activities. A service robot acting autonomously in an indoor environment is faced with very complex tasks. Consider the problem of pouring a liquid into a cup, the robot should first determine if the cup is empty or partially filled. RGB-D cameras provide noisy depth measurements which depend on the opaqueness and refraction index of the liquid. In this paper, we present a novel probabilistic approach for estimating the fill-level of a liquid in a cup using an RGB-D camera. Our approach does not make any assumptions about the properties of the liquid like its opaqueness or its refraction index. We develop a probabilistic model using features extracted from RGB and depth data. Our experiments demonstrate the robustness of our method and an improvement over the state of the art. 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.