On Grasping a Tumbling Debris Object with a Free-Flying Robot

The grasping and stabilization of a tumbling, non-cooperative target satellite by means of a free-flying robot is a challenging control problem, which has been addressed in increasing degree of complexity since 20 years. A novel method for computing robot trajectories for grasping a tumbling target is presented. The problem is solved as a motion planning problem with nonlinear optimization. The resulting solution includes a first maneuver of the Servicer satellite which carries the robot arm, taking account of typical satellite control inputs. An analysis of the characteristics of the motion of a grasping point on a tumbling body is used to motivate this grasping method, which is argued to be useful for grasping targets of larger size

Global Search with Bernoulli Alternation Kernel for Task-oriented Grasping Informed by Simulation

We develop an approach that benefits from large simulated datasets and takes full advantage of the limited online data that is most relevant. We propose a variant of Bayesian optimization that alternates between using informed and uninformed kernels. With this Bernoulli Alternation Kernel we ensure that discrepancies between simulation and reality do not hinder adapting robot control policies online. The proposed approach is applied to a challenging real-world problem of task-oriented grasping with novel objects. Our further contribution is a neural network architecture and training pipeline that use experience from grasping objects in simulation to learn grasp stability scores. We learn task scores from a labeled dataset with a convolutional network, which is used to construct an informed kernel for our variant of Bayesian optimization. Experiments on an ABB Yumi robot with real sensor data demonstrate success of our approach, despite the challenge of fulfilling task requirements and high uncertainty over physical properties of objects.Comment: To appear in 2nd Conference on Robot Learning (CoRL) 201

Simultaneous Tactile Exploration and Grasp Refinement for Unknown Objects

This paper addresses the problem of simultaneously exploring an unknown object to model its shape, using tactile sensors on robotic fingers, while also improving finger placement to optimise grasp stability. In many situations, a robot will have only a partial camera view of the near side of an observed object, for which the far side remains occluded. We show how an initial grasp attempt, based on an initial guess of the overall object shape, yields tactile glances of the far side of the object which enable the shape estimate and consequently the successive grasps to be improved. We propose a grasp exploration approach using a probabilistic representation of shape, based on Gaussian Process Implicit Surfaces. This representation enables initial partial vision data to be augmented with additional data from successive tactile glances. This is combined with a probabilistic estimate of grasp quality to refine grasp configurations. When choosing the next set of finger placements, a bi-objective optimisation method is used to mutually maximise grasp quality and improve shape representation during successive grasp attempts. Experimental results show that the proposed approach yields stable grasp configurations more efficiently than a baseline method, while also yielding improved shape estimate of the grasped object.Comment: IEEE Robotics and Automation Letters. Preprint Version. Accepted February, 202

Introverted Metaphysics: How We Get Our Grip on the Ultimate Nature of Objects, Properties, and Causation

This paper pulls together three debates fundamental in metaphysics and proposes a novel unified approach to them. The three debates are (i) between bundle theory and substrate theory about the nature of objects, (ii) dispositionalism and categoricalism about the nature of properties, and (iii) regularity theory and production theory about the nature of causation. The first part of the paper (§§2-4) suggests that although these debates are metaphysical, the considerations motivating the competing approaches in each debate tend to be epistemological. The second part (§§5-6) argues that the two underlying epistemological pictures supporting competing views lead to highly unsatisfying conceptions of the world. The final part (§§7-10) proposes an alternative epistemological picture, which I call ‘introverted empiricism,’ and presents the way it provides for a more satisfying grasp of the ultimate nature of objects, properties, and causation. It is a consequence of this alternative picture that there is a kind of intimate self-understanding that underlies our understanding of the deep nature of reality

Aesthetic Comprehension of Abstract and Emotion Concepts: Kant’s Aesthetics Renewed

In § 49 of the Critique of the Power of Judgment Kant puts forward a view that the feeling of pleasure in the experience of the beautiful can be stimulated not merely by perceptual properties, but by ideas and thoughts as well. The aim of this paper is to argue that aesthetic ideas fill in the emptiness that abstract and emotion concepts on their own would have without empirical intuitions. That is, aesthetic ideas make these concepts more accessible to us, by creating image schemas that allow us to think about these abstract concepts in a way linked to sensory experience, thereby imbuing them with a more substantive meaning and understanding