Robotic manipulation is challenging when both the objects being manipulated and the tactile sensors are deformable. In this article, we addressed the interplay between the manipulation of deformable objects, tactile sensing, and model-free reinforcement learning on a real robot. We showed how a real robot can learn to manipulate a deformable, thin-shell object via feedback from deformable, multimodal tactile sensors. We addressed the learning of a page flipping task using a two-stage approach. For the first stage, we learned nominal page flipping trajectories for two page sizes by constructing a reward function that quantifies functional task performance from the perspective of tactile sensing. For the second stage, we learned adapted trajectories using tactile-driven perceptual coupling, with an intuitive assumption that, while the page flipping trajectories for different task contexts (page sizes) might differ, similar tactile feedback should be expected from functional trajectories for each context. We also investigated the quality of information encoded by two different representations of tactile sensing data: one based on the artificial apical tuft of bio-inspired tactile sensors, and another based on principal component analysis eigenvalues. The results and effectiveness of our learning framework were demonstrated on a real seven-degree of freedom robot arm and gripper outfitted with tactile sensors.

High resolution tactile sensors are often bulky and have shape profiles that make them awkward for use in manipulation. This becomes important when using such sensors as fingertips for dexterous multi-fingered hands, where boxy or planar fingertips limit the available set of smooth manipulation strategies. High resolution optical based sensors such as GelSight have until now been constrained to relatively flat geometries due to constraints on illumination geometry. Here, we show how to construct a rounded fingertip that utilizes a form of light piping for directional illumination. Our sensors can replace the standard rounded fingertips of the Allegro hand. They can capture high resolution maps of the contact surfaces, and can be used to support various dexterous manipulation tasks.

Several robot manipulation tasks are extremely sensitive to variations of the physical properties of the manipulated objects. One such task is manipulating objects by using gravity or arm accelerations, increasing the importance of mass, center of mass, and friction information. We present SwingBot, a robot that is able to learn the physical features of an held object through tactile exploration. Two exploration actions (tilting and shaking) provide the tactile information used to create a physical feature embedding space. With this embedding, SwingBot is able to predict the swing angle achieved by a robot performing dynamic swing-up manipulations on a previously unseen object. Using these predictions, it is able to search for the optimal control parameters for a desired swing-up angle. We show that with the learned physical features our end-to-end self-supervised learning pipeline is able to substantially improve the accuracy of swinging up unseen objects. We also show that objects with similar dynamics are closer to each other on the embedding space and that the embedding can be disentangled into values of specific physical properties.

Tactile sensing is a key sensor modality for robots interacting with their surroundings. These sensors provide a rich and diverse set of data signals that contain detailed information collected from contacts between the robot and its environment. The data are however not limited to individual contacts and can be used to extract a wide range of information about the objects in the environment as well as the actions of the robot during the interactions. In this article, we provide an overview of tactile information and its applications in robotics. We present a hierarchy consisting of raw, contact, object, and action levels to structure the tactile information, with higher-level information often building upon lower-level information. We discuss different types of information that can be extracted at each level of the hierarchy. The article also includes an overview of different types of robot applications and the types of tactile information that they employ. Finally we end the article with a discussion for future tactile applications which are still beyond the current capabilities of robots.

Movement Primitives are a well studied and widely applied concept in modern robotics. Composing primitives out of an existing library, however, has shown to be a challenging problem. We propose the use of probabilistic context-free grammars to sequence a series of primitives to generate complex robot policies from a given library of primitives. The rule-based nature of formal grammars allows an intuitive encoding of hierarchically and recursively structured tasks. This hierarchical concept strongly connects with the way robot policies can be learned, organized, and re-used. However, the induction of context-free grammars has proven to be a complicated and yet unsolved challenge. In this work, we exploit the physical nature of robot movement primitives to restrict and efficiently search the grammar space. The grammar is learned applying a Markov Chain Monte Carlo optimization over the posteriors of the grammars given the observations. The proposal distribution is defined as a mixture over the probabilities of the operators connecting the search space. Restrictions to these operators guarantee continuous sequences while reducing the grammar space. We validate our method on a redundant 7 degree-of-freedom lightweight robotic arm on tasks that require the generation of complex sequences consisting of simple movement primitives.

State abstraction in a discrete reinforcement learning setting clusters states sharing a similar optimal action to yield an easier to solve decision process. In this paper, we generalize the concept of state abstraction to continuous action reinforcement learning by defining an abstract state as a state cluster over which a near-optimal policy of simple shape exists. We propose a hierarchical reinforcement learning algorithm that is able to simultaneously find the state space clustering and the optimal sub-policies in each cluster. The main advantage of the proposed framework is to provide a straightforward way of regularizing reinforcement learning by controlling the behavioral complexity of the learned policy. We apply our algorithm on several benchmark tasks and a robot tactile manipulation task and show that we can match state-of-the-art deep reinforcement learning performance by combining a small number of linear policies.

Accurate object shape knowledge provides important information for performing stable grasping and dexterous manipulation. When modeling an object using tactile sensors, touching the object surface at a fixed grid of points can be sample inefficient. In this paper, we present an active touch strategy to efficiently reduce the surface geometry uncertainty by leveraging a probabilistic representation of object surface. In particular, we model the object surface using a Gaussian process and use the associated uncertainty information to efficiently determine the next point to explore. We validate the resulting method for tactile object surface modeling using a real robot to reconstruct multiple, complex object surfaces.

During grasping and other in-hand manipulation tasks maintaining a stable grip on the object is crucial for the task's outcome. Inherently connected to grip stability is the concept of slip. Slip occurs when the contact between the fingertip and the object is partially lost, resulting in sudden undesired changes to the objects state. While several approaches for slip detection have been proposed in the literature, they frequently rely on previous knowledge of the manipulated object. This previous knowledge may be unavailable, seeing that robots operating in real-world scenarios often must interact with previously unseen objects. In our work we explore the generalization capabilities of well known supervised learning methods, using random forest classifiers to create generalizable slip predictors. We utilize these classifiers in the feedback loop of an object stabilization controller. We show that the controller can successfully stabilize previously unknown objects by predicting and counteracting slip events.