In this paper, we focus on the challenging perception problem in robotic pouring. Most of the existing approaches either leverage visual or haptic information. However, these techniques may suffer from poor generalization performances on opaque containers or concerning measuring precision. To tackle these drawbacks, we propose to make use of audio vibration sensing and design a deep neural network PouringNet to predict the liquid height from the audio fragment during the robotic pouring task. PouringNet is trained on our collected real-world pouring dataset with multimodal sensing data, which contains more than 3000 recordings of audio, force feedback, video and trajectory data of the human hand that performs the pouring task. Each record represents a complete pouring procedure. We conduct several evaluations on PouringNet with our dataset and robotic hardware. The results demonstrate that our PouringNet generalizes well across different liquid containers, positions of the audio receiver, initial liquid heights and types of liquid, and facilitates a more robust and accurate audio-based perception for robotic pouring.
Besides, haptic sensing, especially force and torque sensing, are also popular in the perception of robotic pouring. Specifically, force data is exerted to generate pouring trajectories by predicting the angular velocity of the pouring container in simulation @cite_14 . Rozo al @cite_17 used a parametric hidden Markov model to retrieve joint-level commands given the force-torque inputs from the human demonstration. Hannes al @cite_11 examined the viscosity estimation of the various liquids from tactile sensory data. Although force from the pouring container could explicitly represent the volume of the pour-out liquid, force cannot measure the liquid height in an unseen target container.
@cite_14 @cite_11 @cite_17
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Pouring is a simple task people perform daily. It is the second most frequently executed motion in cooking scenarios, after pick-and-place. We present a pouring trajectory generation approach, which uses force feedback from the cup to determine the future velocity of pouring. The approach uses recurrent neural networks as its building blocks. We collected the pouring demonstrations which we used for training. To test our approach in simulation, we also created and trained a force estimation system. The simulated experiments show that the system is able to generalize to single unseen element of the pouring characteristics. The estimation of parameters that affect the dynamics of objects—such as viscosity or internal degree of freedom—is an important step in autonomous and dexterous robotic manipulation of objects. However, accurate and efficient estimation of these object parameters may be challenging due to complex, highly nonlinear underlying physical processes. To improve on the quality of otherwise hand-crafted solutions, automatic generation of control strategies can be helpful. We present a framework that uses active learning to help with sequential gathering of data samples,using information-theoretic ciriteria to find the optimal actions to perform at each time step. We demonstrate the usefulness of our approach on a robotic hand-arm setup, where the task involves shaking bottles of different liquids in order to determine the liquid's viscosity from only tactile feedback. We optimize the shaking frequency and the rotation angle of shaking in an online manner in order to speed up convergence of estimates. 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.