Manuel Bonilla

Grasping with Soft Hands

Despite some prematurely optimistic claims, the ability of robots to grasp general objects in unstructured environments still remains far behind that of humans. This is not solely caused by differences in the mechanics of hands: indeed, we show that human use of a simple robot hand (the Pisa/IIT SoftHand) can afford capabilities that are comparable to natural grasping. It is through the observation of such human-directed robot hand operations that we realized how fundamental in everyday grasping and manipulation is the role of hand compliance, which is used to adapt to the shape of surrounding objects. Objects and environmental constraints are in turn used to functionally shape the hand, going beyond its nominal kinematic limits by exploiting structural softness. In this paper, we set out to study grasp planning for hands that are simple - in the sense of low number of actuated degrees of freedom (one for the Pisa/IIT SoftHand) - but are soft, i.e. continuously deformable in an infinity of possible shapes through interaction with objects. After general considerations on the change of paradigm in grasp planning that this setting brings about with respect to classical rigid multi-dof grasp planning, we present a procedure to extract grasp affordances for the Pisa/IIT SoftHand through physically accurate numerical simulations. The selected grasps are then successfully tested in an experimental scenario.

No More Heavy Lifting: Robotic Solutions to the Container Unloading Problem

This article discusses the scientifically and industrially important problem of automating the process of unloading goods from standard shipping containers. We outline some of the challenges barring further adoption of robotic solutions to this problem, ranging from handling a vast variety of shapes, sizes, weights, appearances, and packing arrangements of the goods, through hard demands on unloading speed and reliability, to ensuring that fragile goods are not damaged. We propose a modular and reconfigurable software framework in an attempt to efficiently address some of these challenges. We also outline the general framework design and the basic functionality of the core modules developed. We present two instantiations of the software system on two different fully integrated demonstrators: (1) coping with an industrial scenario, i.e., the automated unloading of coffee sacks with an already economically interesting performance; and (2) a scenario used to demonstrate the capabilities of our scientific and technological developments in the context of medium- to long-term prospects of automation in logistics. We performed evaluations that allowed us to summarize several important lessons learned and to identify future directions of research on autonomous robots for the handling of goods in logistics applications.

The Quest for Natural Machine Motion: An Open Platform to Fast-Prototyping Articulated Soft Robots

Soft robots are one of the most significant recent evolutions in robotics. They rely on compliant physical structures purposefully designed to embody desired characteristics. Since their introduction, they have shown remarkable applicability in overcoming their rigid counterparts in such areas as interaction with humans, adaptability, energy efficiency, and maximization of peak performance. Nonetheless, we believe that research on novel soft robot applications is still slowed by the difficulty in obtaining or developing a working soft robot structure to explore novel applications.

Grasp compliance regulation in synergistically controlled robotic hands with VSA

In this paper, we propose a general method to achieve a desired grasp compliance acting both on the joint stiffness values and on the hand configuration, also in the presence of restrictions caused by synergistic underactuation. The approach is based on the iterative exploration of the equilibrium manifold of the system and the quasi-static analysis of the governing equations. As a result, the method can cope with large commanded variations of the grasp stiffness with respect to an initial configuration. Two numerical examples are illustrated. In the first one, a simple 2D hand is analyzed so that the obtained results can be easily verified and discussed. In the second one, to show the method at work in a more realistic scenario, we model grasp compliance regulation for a DLR/HIT hand II grasping a ball.

Object recognition and localization for robust grasping with a dexterous gripper in the context of container unloading

The work presented here is embedded in research on an industrial application scenario, namely autonomous shipping-container unloading, which has several challenging constraints: the scene is very cluttered, objects can be much larger than in common table-top scenarios; the perception must be highly robust, while being as fast as possible. These contradicting goals force a compromise between speed and accuracy. In this work, we investigate a state of the art perception system integrated with a dexterous gripper. In particular, we are interested in pose estimation errors from the recognition module and whether these errors can be handled by the abilities of the gripper.

Grasp planning with soft hands using Bounding Box object decomposition

In this paper, we present a method to plan grasps for soft hands. Considering that soft hands can easily conform to the shape an the object, with preference to certain types of basic geometries and dimensions, we decompose the object into one type of these geometries, particularly into Minimal Volume Bounding Boxes (MVBBs), which are proved to be efficiently graspable by the hand we use. A set of hand poses are then generated using geometric information extracted from such MVBBs. All hand postures are used in a dynamic simulator of the PISA/IIT Soft Hand and put on a test to evaluate if a proposed hand posture leads to a successful grasp. We show, through a set of numerical simulations, that the probability of success of the hand poses generated with the proposed algorithm is very good and represents an evident improvement with respect to our previous results published in [1].

Noninteracting constrained motion planning and control for robot manipulators

In this paper we present a novel geometric approach to motion planning for constrained robot systems. This problem is notoriously hard, as classical sampling-based methods do not easily apply when motion is constrained in a zero-measure submanifold of the configuration space. Based on results on the functional controllability theory of dynamical systems, we obtain a description of the complementary spaces where rigid body motions can occur, and where interaction forces can be generated, respectively. Once this geometric setting is established, the motion planning problem can be greatly simplified. Indeed, we can relax the geometric constraint, i.e., replace the lower-dimensional constraint manifold with a full-dimensional boundary layer. This in turn allows us to plan motion using state-of-the-art methods, such as RRT∗, on points within the boundary layer, which can be efficiently sampled. On the other hand, the same geometric approach enables the design of a completely decoupled control scheme for interaction forces, so that they can be regulated to zero (or any other desired value) without interacting with the motion plan execution. A distinguishing feature of our method is that it does not use projection of sampled points on the constraint manifold, thus largely saving in computational time, and guaranteeing accurate execution of the motion plan. An explanatory example is presented, along with an experimental implementation of the method on a bimanual manipulation workstation.

Sample-based motion planning for robot manipulators with closed kinematic chains

Random sampling-based methods for motion planning of constrained robot manipulators have been widely studied in recent years. The main problem to deal with is the lack of an explicit parametrization of the non linear submanifold in the Configuration Space (CS) imposed by the constraints in the system. Most of the proposed planning methods use projections to generate valid configurations of the system slowing the planning process. Recently, new robot mechanism includes compliance either in the structure or in the controllers. In this kind of robot most of the times the planned trajectories are not executed exactly due to uncertainties and interactions with the environment. Indeed, controller references are generated such that the constraint is violated to indirectly generate forces during interactions. With the purpose of avoiding projections, in this paper we take advantage of the compliance of systems to relax the geometric constraints imposed by closed kinematic chains. The relaxed constraint is then used in a state-of-the-art suboptimal random sampling based technique to generate paths for constrained robot manipulators. As a consequence of relaxation, arising contact forces acting on the constraint change from configuration to configuration during the planned path. Those forces can be regulated using a proper controller that takes advantage of the geometric decoupling of the subspaces describing constrained rigid-body motions of the mechanism and the controllable forces.

Controlling the active surfaces of the Velvet Fingers: Sticky to slippy fingers

Industrial grippers are often used for grasping, while in-hand re-orientation and positioning are dealt with by other means. Contact surface engineering has been recently proposed as a possible mean to introduce dexterity in simple grippers, as in the Velvet Fingers smart gripper, a novel concept of end-effector combining simple under-actuated mechanics and high manipulation possibilities, thanks to conveyors which are built in the finger pads. This paper undergoes the modeling and control of the active conveyors of the Velvet Fingers gripper which are rendered able to emulate different levels of friction and to apply tangential thrusts to the contacted objects. Through the paper particular attention is dedicated to the mechanical implementation, sense drive and control electronics of the device. The capabilities of the prototype are showed in some grasping and manipulation experiments.

From Soft to Adaptive Synergies: The Pisa/IIT SoftHand

Taking inspiration from the neuroscientific findings on hand synergies discussed in the first part of the book, in this chapter we present the Pisa/IIT SoftHand, a novel robot hand prototype. The design moves under the guidelines of making an hardware robust and easy to control, preserving an high level of grasping capabilities and an aspect as similar as possible to the human counterpart. First, the main theoretical tools used to enable such simplification are presented, as for example the notion of soft synergies. A discussion of some possible actuation schemes shows that a straightforward implementation of the soft synergy idea in an effective design is not trivial. The proposed approach, called adaptive synergy, rests on ideas coming from underactuated hand design, offering a design method to implement the desired set of soft synergies as demonstrated both with simulations and experiments. As a particular instance of application of the synthesis method of adaptive synergies, the Pisa/IIT SoftHand is described in detail. The hand has 19 joints, but only uses one actuator to activate its adaptive synergy. Of particular relevance in its design is the very soft and safe, yet powerful and extremely robust structure, obtained through the use of innovative articulations and ligaments replacing conventional joint design. Moreover, in this work, summarizing results presented in previous papers, a discussion is presented about how a new set of possibilities is open from paradigm shift in manipulation approaches, moving from manipulation with rigid to soft hands.