This paper presents a data-efficient approach to learning transferable forward models for robotic push manipulation. Our approach extends our previous work on contact-based predictors by leveraging information on the pushed object's local surface features. We test the hypothesis that, by conditioning predictions on local surface features, we can achieve generalisation across objects of different shapes. In doing so, we do not require a CAD model of the object but rather rely on a point cloud object model (PCOM). Our approach involves learning motion models that are specific to contact models. Contact models encode the contacts seen during training time and allow generating similar contacts at prediction time. Predicting on familiar ground reduces the motion models' sample complexity while using local contact information for prediction increases their transferability. In extensive experiments in simulation, our approach is capable of transfer learning for various test objects, outperforming a baseline predictor. We support those results with a proof of concept on a real robot.

How should a robot direct active vision so as to ensure reliable grasping? We answer this question for the case of dexterous grasping of unfamiliar objects. By dexterous grasping we simply mean grasping by any hand with more than two fingers, such that the robot has some choice about where to place each finger. Such grasps typically fail in one of two ways, either unmodeled objects in the scene cause collisions or object reconstruction is insufficient to ensure that the grasp points provide a stable force closure. These problems can be solved more easily if active sensing is guided by the anticipated actions. Our approach has three stages. First, we take a single view and generate candidate grasps from the resulting partial object reconstruction. Second, we drive the active vision approach to maximise surface reconstruction quality around the planned contact points. During this phase, the anticipated grasp is continually refined. Third, we direct gaze to improve the safety of the planned reach to grasp trajectory. We show, on a dexterous manipulator with a camera on the wrist, that our approach (80.4% success rate) outperforms a randomised algorithm (64.3% success rate).

This paper addresses the problem of jointly planning both grasps and subsequent manipulative actions. Previously, these two problems have typically been studied in isolation, however joint reasoning is essential to enable robots to complete real manipulative tasks. In this paper, the two problems are addressed jointly and a solution that takes both into consideration is proposed. To do so, a manipulation capability index is defined, which is a function of both the task execution waypoints and the object grasping contact points. We build on recent state-of-the-art grasp-learning methods, to show how this index can be combined with a likelihood function computed by a probabilistic model of grasp selection, enabling the planning of grasps which have a high likelihood of being stable, but which also maximise the robot's capability to deliver a desired post-grasp task trajectory. We also show how this paradigm can be extended, from a single arm and hand, to enable efficient grasping and manipulation with a bi-manual robot. We demonstrate the effectiveness of the approach using experiments on a simulated as well as a real robot.

Generalising dexterous grasps to novel objects is an open problem. We show how to learn grasps for high DoF hands that generalise to novel objects, given as little as one demonstrated grasp. During grasp learning two types of probability density are learned that model the demonstrated grasp. The first density type (the contact model) models the relationship of an individual finger part to local surface features at its contact point. The second density type (the hand configuration model) models the whole hand configuration during the approach to grasp. When presented with a new object, many candidate grasps are generated, and a kinematically feasible grasp is selected that maximises the product of these densities. We demonstrate 31 successful grasps on novel objects (an 86% success rate), transferred from 16 training grasps. The method enables: transfer of dexterous grasps within object categories; across object categories; to and from objects where there is no complete model of the object available; and using two different dexterous hands.

Predicting the motions of rigid objects under contacts is a necessary precursor to planning of robot manipulation of objects. On the one hand physics based rigid body simulations are used, and on the other learning approaches are being developed. The advantage of physics simulations is that because they explicitly perform collision checking they respect kinematic constraints, producing physically plausible predictions. The advantage of learning approaches is that they can capture the effects on motion of unobservable parameters such as mass distribution, and frictional coefficients, thus producing more accurate predicted trajectories. This paper shows how to bring together the advantages of both approaches to achieve learned simulators of specific objects that outperform previous learning approaches. Our approach employs a fast simplified collision checker and a learning method. The learner predicts trajectories for the object. These are optimised post prediction to minimise interpenetrations according to the collision checker. In addition we show that cleaning the training data prior to learning can also improve performance. Combining both approaches results in consistently strong prediction performance. The new simulator outperforms previous learning based approaches on a single contact push manipulation prediction task. We also present results showing that the method works for multi-contact manipulation, for which rigid body simulators are notoriously unstable.

Dexterous grasping of objects with uncertain pose is a hard unsolved problem in robotics. This paper solves this problem using information gain re-planning. First we show how tactile information, acquired during a failed attempt to grasp an object can be used to refine the estimate of that object's pose. Second, we show how this information can be used to replan new reach to grasp trajectories for successive grasp attempts. Finally we show how reach-to-grasp trajectories can be modified, so that they maximise the expected tactile information gain, while simultaneously delivering the hand to the grasp configuration that is most likely to succeed. Our main novel outcome is thus to enable tactile information gain planning for Dexterous, high degree of freedom (DoFs) manipulators. We achieve this using a combination of information gain planning, hierarchical probabilistic roadmap planning, and belief updating from tactile sensors for objects with non-Gaussian pose uncertainty in 6 dimensions. The method is demonstrated in trials with simulated robots. Sequential replanning is shown to achieve a greater success rate than single grasp attempts, and trajectories that maximise information gain require fewer re-planning iterations than conventional planning methods before a grasp is achieved.

This paper presents an algorithm for planning sequences of pushes, by which a robotic arm equipped with a single rigid finger can move a manipulated object (or manipulandum) towards a desired goal pose. Pushing is perhaps the most basic kind of manipulation, however it presents difficult challenges for planning, because of the complex relationship between manipulative pushing actions and resulting manipulandum motions. The motion planning literature has well developed paradigms for solving e.g. the piano-mover's problem, where the search occurs directly in the configuration space of the manipulandum object being moved. In contrast, in pushing manipulation, a plan must be built in the action space of the robot, which is only indirectly linked to the motion space of the manipulandum through a complex interaction for which inverse models may not be known. In this paper, we present a two stage approach to planning pushing operations. A global RRT path planner is used to explore the space of possible manipulandum configurations, while a local push planner makes use of predictive models of pushing interactions, to plan sequences of pushes to move the manipulandum from one RRT node to the next. The effectiveness of the algorithm is demonstrated in simulation experiments in which a robot must move a rigid body through complex 3D transformations by applying only a sequence of simple single finger pushes.

An important problem in robotic manipulation is the ability to predict how objects behave under manipulative actions. This ability is necessary to allow planning of object manipulations. Physics simulators can be used to do this, but they model many kinds of object interaction poorly. An alternative is to learn a motion model for objects by interacting with them. In this paper we address the problem of learning to predict the interactions of rigid bodies in a probabilistic framework, and demonstrate the results in the domain of robotic push manipulation. A robot arm applies random pushes to various objects and observes the resulting motion with a vision system. The relationship between push actions and object motions is learned, and enables the robot to predict the motions that will result from new pushes. The learning does not make explicit use of physics knowledge, or any pre-coded physical constraints, nor is it even restricted to domains which obey any particular rules of physics. We use regression to learn efficiently how to predict the gross motion of a particular object. We further show how different density functions can encode different kinds of information about the behaviour of interacting objects. By combining these as a product of densities, we show how learned predictors can cope with a degree of generalisation to previously unencountered object shapes, subjected to previously unencountered push directions. Performance is evaluated through a combination of virtual experiments in a physics simulator, and real experiments with a 5-axis arm equipped with a simple, rigid finger.

Visual tracking of an object can provide a powerful source of feedback information during complex robotic manipulation operations, especially those in which there may be uncertainty about which new object pose may result from a planned manipulative action. At the same time, robotic manipulation can provide a challenging environment for visual tracking, with occlusions of the object by other objects or by the robot itself, and sudden changes in object pose that may be accompanied by motion blur. Recursive filtering techniques use motion models for predictor-corrector tracking, but the simple models typically used often fail to adequately predict the complex motions of manipulated objects. We show how statistical machine learning techniques can be used to train sophisticated motion predictors, which incorporate additional information by being conditioned on the planned manipulative action being executed. We then show how these learned predictors can be used to propagate the particles of a particle filter from one predictor-corrector step to the next, enabling a visual tracking algorithm to maintain plausible hypotheses about the location of an object, even during severe occlusion and other difficult conditions. We demonstrate the approach in the context of robotic push manipulation, where a 5-axis robot arm equipped with a rigid finger applies a series of pushes to an object, while it is tracked by a vision algorithm using a single camera.