Grasp planning must consider an object's local geometry (at the finger contacts), for the range of applicable wrenches under friction, and its global geometry, for force closure and grasp quality. Most everyday objects have curved surfaces unamenable to a pure combinatorial approach but treatable with tools from differential geometry. Our idea is to “discretize” such a surface in a top-down fashion into elementary patches (e-patches), each consisting of points that would yield close enough wrenches. Preprocessing based on Gaussian curvature decomposes the surface into strictly convex, strictly concave, ruled, and saddle patches. The Gauss map guides the subdivision of any patch with a large variation in the contact force direction, with the aid of a Platonic solid. The principal component analysis (PCA) further subdivides any patch that has a large variation in torque. The final structure is called a patch tree, which stores e-patches at its leaves, and force or torque ranges at its internal nodes. Grasp synthesis and optimization operates on the patch tree with a stack to efficiently prune away non-promising finger placements. Simulation and experiment with a Shadow Hand have been conducted over everyday items. The patch tree exhibits different levels of surface granularity. It has a good promise for efficient planning of finger gaits to carry out grasping and tool manipulation.

Despite decades of steady research progress, the robotic hand is still far behind the human hand in terms of dexterity and versatility. A milestone in this quest for human-level performance will be possessing the skills of manipulating hand tools, for their non-trivial geometries and for the intricacies of controlling their contact-based interactions with objects, which are the final targets of manipulation. This paper investigates screwdriving by a robotic arm/hand pair, dealing with the chain of contacts connecting the substrate, screw, screwdriver, and fingertips. Considering rolling contacts and finger gaits, our force control scheme is derived through backward chaining to leverage the dynamics of the screwdriver and arm/hand. To maintain the fastening effort, estimations are carried out sequentially for the screwdriver’s pose via optimization under visual and kinematic constraints, and for its applied wrench on the screw via solution drawing upon dynamics. This wrench, adjusted based on position/force feedback, is mapped by the grasp matrix to the desired fingertip forces, which are then used for computing torques to be exerted by the arm and hand to close the loop. Simulation and experiments with a Shadow Hand have been conducted for validations.

Skills of cutting natural foods are important for robots looking to play a bigger role in kitchen assistance. The basic objective of cutting is to achieve material fracture via smooth movements of a kitchen knife, which in the process performs work to overcome material toughness, acts against blade-material friction, and generates shape deformation. This paper investigates how a robotic arm drives the knife to cut through an object in a sequence of three moves: pressing, touching, and slicing. To cope with evolving contacts with the material and cutting board, position, force, and impedance controls act either separately or jointly, assisted by force sensing and/or based on fracture mechanics, so the knife follows a prescribed trajectory to split the object. Force data acquired during the phase of pressing are used for estimating the object-specific values of physical parameters related to cutting. These estimated values are promptly used for control purpose to execute the phase of slicing. Experiments over several types of fruits and vegetables have exhibited natural cutting movements resembling those performed by a human hand.Note to Practitioners—Automation of kitchen skills is an important step in the development of home robots, which are expected to relieve us from daily chores and help us care for the elderly and people with disabilities. The motivation of this research is to enable a robotic arm to cut natural foods with knife movements that bear the smoothness and efficiency of those executed by a human hand. Existing methods on robotic cutting have focused on force control to ensure material separation but not on execution of natural knife movements. This paper dissects a cutting action into three phases, as inspired from the human hand execution, and realizes them via different control policies. These policies are based on sensing and modeling the forces experienced by the knife through its interactions with the material and the cutting board. Preliminary experimental results have demonstrated cutting of various food items with speed and smoothness. In future research, we will address cutting of deformable objects and explore issues including energy efficiency, cutting by the knife held in a robotic hand, and food stabilization and manipulation by a second arm/hand.

For over three decades, finger gaiting has remained largely a subject for theoretical inquiries. Successful execution of a sequence of finger gaits does not simply reduce to planning collision-free paths for the involved fingers. A major issue is how to move the gaiting finger without losing the finger contacts with the object, which will most likely undergo a motion as the contact forces need to be adapted during the gait. This paper focuses on a single finger gait executed on a tool by an anthropomorphic hand driven by an arm. To improve stability, the tool's tip is leveraged as a pivot on the supporting plane. The gait consists of three stages: removal, during which the contact force on the gaiting finger gradually decreases to zero; relocation, during which the finger follows a pre-planned path (relative to the moving object) to establish a new contact; and addition, during which the contact force on the relocated finger increases to some desired level. Hybrid position/impedance control employs reference finger forces that satisfy the friction cone constraints and are dynamically consistent with the object's motion, which in turn provides reference poses for the fingertips to maintain their contacts during the gait. Finger gaits have been demonstrated on a kitchen knife and a screwdriver with an Adept SCARA robot and a Shadow Dexterous Hand.

Despite more than three decades of grasping research, many tools in our everyday life still pose a serious challenge for a robotic hand to grip. The level of dexterity for such a maneuver is surprisingly "high" that its execution may require a combination of closed loop controls and finger gaits. This paper studies the task of an anthropomorphic hand driven by a robotic arm to pick up and firmly hold a kitchen knife initially resting on the cutting board. In the first phase, the hand grasps the knife's handle at two antipodal points and then pivots it about the knife's point in contact with the board to leverage the latter's support. Desired contact forces exerted by the two holding soft fingers are calculated and used for dynamic control of both the hand and the arm. In the second phase, a sequence of gaits for all the five fingers is performed quasi-statically to reach a power grasp on the knife's handle, which remains still during the period. Simulation has been performed using models of the Shadow Hand and the UR10 Arm.

Effectiveness of cutting is measured by the ability to achieve material fracture with smooth knife movements. The work performed by a knife overcomes the material toughness, acts against the blade-material friction, and generates shape deformation. This paper studies how to control a 2-DOF robotic arm equipped with a force/torque sensor to cut through an object in a sequence of three moves: press, push, and slice. For each move, a separate control strategy in the Cartesian space is designed to incorporate contact and/or force constraints while following some prescribed trajectory. Experiments conducted over several types of natural foods have demonstrated smooth motions like would be commanded by a human hand.

This paper studies dexterous manipulation in the plane by a two-fingered hand in the plane. The dynamics of each finger, which consists of two links with coupled joints, are derived based on Lagrangian mechanics. As an object is being manipulated, its orientation and the two independent joint angles of the hand constitute the state of the entire system. Contact kinematics, accounting for both stick and slip modes, are combined with dynamics to establish a dependence of the object's linear and angular accelerations on joint accelerations. This allows control of joint torques, under a proportional-derivative (PD) law, to move the object to a target position in a desired orientation.