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Chapter 40 — Mobility and Manipulation

Oliver Brock, Jaeheung Park and Marc Toussaint

Mobile manipulation requires the integration of methodologies from all aspects of robotics. Instead of tackling each aspect in isolation,mobilemanipulation research exploits their interdependence to solve challenging problems. As a result, novel views of long-standing problems emerge. In this chapter, we present these emerging views in the areas of grasping, control, motion generation, learning, and perception. All of these areas must address the shared challenges of high-dimensionality, uncertainty, and task variability. The section on grasping and manipulation describes a trend towards actively leveraging contact and physical and dynamic interactions between hand, object, and environment. Research in control addresses the challenges of appropriately coupling mobility and manipulation. The field of motion generation increasingly blurs the boundaries between control and planning, leading to task-consistent motion in high-dimensional configuration spaces, even in dynamic and partially unknown environments. A key challenge of learning formobilemanipulation consists of identifying the appropriate priors, and we survey recent learning approaches to perception, grasping, motion, and manipulation. Finally, a discussion of promising methods in perception shows how concepts and methods from navigation and active perception are applied.

Learning dexterous grasps that generalize to novel objects by combining hand and contact models

Author  Marek Kopicki, Renaud Detry, Florian Schmidt, Christoph Borst, Rustam Stolkin, Jeremy Wyatt

Video ID : 650

We show how a robot learns grasps for high-DOF hands that generalize 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.

Atlas whole-body grasping

Author  DRC Team MIT

Video ID : 651

A simple demonstration of automated perception, whole-body motion planning, and dynamic stabilization using Atlas and software developed at MIT.

Handle localization and grasping

Author  Robert Platt

Video ID : 652

The robot localizes and grasps appropriate handles on novel objects in real time.

Catching objects in flight

Author  Seungsu Kim, Ashwini Shukla, Aude Billard

Video ID : 653

We target the difficult problem of catching in-flight objects with uneven shapes. This requires the solution of three complex problems: predicting accurately the trajectory of fast-moving objects, predicting the feasible catching configuration, and planning the arm motion, all within milliseconds. We follow a programming-by-demonstration approach in order to learn models of the object and the arm dynamics from throwing examples. We propose a new methodology for finding a feasible catching configuration in a probabilistic manner. We leverage the strength of dynamical systems for encoding motion from several demonstrations. This enables fast and online adaptation of the arm motion in the presence of sensor uncertainty. We validate the approach in simulation with the iCub humanoid robot and in real-world experiment with the KUKA LWR 4+ (7-DOF arm robot) for catching a hammer, a tennis racket, an empty bottle, a partially filled bottle and a cardboard box.

Avian-inspired grasping for quadrotor micro UAVs

Author  Justin Thomas, Joe Polin, Koushil Sreenath, Vijay Kumar

Video ID : 654

Drawing inspiration from aerial hunting by birds of prey, we design and equip a quadrotor MAV with an actuated appendage enabling grasping and object retrieval at high speeds. We develop a nonlinear dynamic model of the system, demonstrate that the system is differentially flat, plan dynamic trajectories using the flatness property, and present experimental results with pick-up velocities at 2m/s (six body lengths/s) and 3m/s (nine body lengths/s).

A compliant underactuated hand for robust manipulation

Author  Lael U. Odhner, Leif P. Jentoft, Mark R. Claffee, Nicholas Corson, Yaroslav Tenzer, Raymond R. Ma, Martin Buehler, Robert Kohout, Robert Howe, Aaron M. Dollar

Video ID : 655

This video introduces the iRobot-Harvard-Yale (iHY) Hand, an underactuated hand driven by five actuators which is capable of performing a wide range of grasping and in-hand repositioning tasks. This hand was designed to address the need for a durable, inexpensive, moderately dexterous hand suitable for use on mobile robots. Particular emphasis is placed on the development of underactuated fingers that are capable of both firm power grasps and low-stiffness fingertip grasps, using only the compliant mechanics of the fingers.

Yale Aerial Manipulator - Dollar Grasp Lab

Author  Paul E. I. Pounds, Daniel R. Bersak, Aaron M. Dollar

Video ID : 656

Aaron Dollar's Aerial Manipulator integrates a gripper that is able to directly grasp and transport objects.

Exploitation of environmental constraints in human and robotic grasping

Author  Clemens Eppner, Raphael Deimel, Jose Alvarez-Ruiz, Marianne Maertens, Oliver Brock

Video ID : 657

We investigate the premise that robust grasping performance is enabled by exploiting constraints present in the environment. Given this premise, grasping becomes a process of successive exploitation of environmental constraints, until a successful grasp has been established. We present evidence for this view by showing robust robotic grasping based on constraint-exploiting grasp strategies, and we show that it is possible to design robotic hands with inherent capabilities for the exploitation of environmental constraints.

Adaptive synergies for a humanoid robot hand

Author  Centro di Ricerca Enrico Piaggio

Video ID : 658

We present the first implementation of the UNIPI-hand, a highly integrated prototype of an anthropomorphic hand that conciliates the idea of adaptive synergies with a human-form factor. The video validates the hand's versatility by showing grasp and manipulation actions on a variety of objects.

Universal gripper

Author  Cornel Creative Machines Lab

Video ID : 660

Universal robotic gripper based on the jamming of granular material.