Simulators are powerful tools for reasoning about a robot's interactions with its environment. However, when simulations diverge from reality, that reasoning becomes less useful. In this paper, we show how to close the loop between liquid simulation and real-time perception. We use observations of liquids to correct errors when tracking the liquid's state in a simulator. Our results show that closed-loop simulation is an effective way to prevent large divergence between the simulated and real liquid states. As a direct consequence of this, our method can enable reasoning about liquids that would otherwise be infeasible due to large divergences, such as reasoning about occluded liquid.
In robotics, there has been work using simulators to reason about liquids, although only in constrained settings, e.g., pouring tasks. Kunze and Beetz @cite_7 @cite_8 employed a simulator to reason about a robot's actions as it attempted to make pancakes, which involved reasoning about the liquid batter. Yamaguchi and Atkeson @cite_13 @cite_2 used a simulator to reason about pouring different kinds of liquids. However, these works use rather crude liquid simulations for prediction tasks that do not require accurate feedback. Schenck and Fox @cite_4 used a finite element method liquid simulator to train a deep network on the tasks of detecting and tracking liquids. They did not use the simulator to reason about perceived liquid, though.
@cite_4 @cite_7 @cite_8 @cite_2 @cite_13
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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. 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. Recent advances in AI and robotics have claimed many incredible results with deep learning, yet no work to date has applied deep learning to the problem of liquid perception and reasoning. In this paper, we apply fully-convolutional deep neural networks to the tasks of detecting and tracking liquids. We evaluate three models: a single-frame network, multi-frame network, and a LSTM recurrent network. Our results show that the best liquid detection results are achieved when aggregating data over multiple frames and that the LSTM network outperforms the other two in both tasks. This suggests that LSTM-based neural networks have the potential to be a key component for enabling robots to handle liquids using robust, closed-loop controllers. 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.