160 research outputs found
Improving grasping forces during the manipulation of unknown objects
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksMany of the solutions proposed for the object manipulation problem are based on the knowledge of the object features. The approach proposed in this paper intends to provide a simple geometrical approach to securely manipulate an unknown object based only on tactile and kinematic information. The tactile and kinematic data obtained during the manipulation is used to recognize the object shape (at least the local object curvature), allowing to improve the grasping forces when this information is added to the manipulation strategy.
The approach has been fully implemented and tested using the Schunk Dexterous Hand (SDH2). Experimental results are shown to illustrate the efficiency of the approach.Peer ReviewedPostprint (author's final draft
Towards Developing Gripper to obtain Dexterous Manipulation
Artificial hands or grippers are essential elements in many robotic systems, such as, humanoid,
industry, social robot, space robot, mobile robot, surgery and so on. As humans, we use
our hands in different ways and can perform various maneuvers such as writing, altering
posture of an object in-hand without having difficulties. Most of our daily activities are
dependent on the prehensile and non-prehensile capabilities of our hand. Therefore, the
human hand is the central motivation of grasping and manipulation, and has been explicitly
studied from many perspectives such as, from the design of complex actuation, synergy, use
of soft material, sensors, etc; however to obtain the adaptability to a plurality of objects along
with the capabilities of in-hand manipulation of our hand in a grasping device is not easy,
and not fully evaluated by any developed gripper.
Industrial researchers primarily use rigid materials and heavy actuators in the design for
repeatability, reliability to meet dexterity, precision, time requirements where the required
flexibility to manipulate object in-hand is typically absent. On the other hand, anthropomorphic
hands are generally developed by soft materials. However they are not deployed
for manipulation mainly due to the presence of numerous sensors and consequent control
complexity of under-actuated mechanisms that significantly reduce speed and time requirements
of industrial demand. Hence, developing artificial hands or grippers with prehensile
capabilities and dexterity similar to human like hands is challenging, and it urges combined
contributions from multiple disciplines such as, kinematics, dynamics, control, machine
learning and so on. Therefore, capabilities of artificial hands in general have been constrained
to some specific tasks according to their target applications, such as grasping (in biomimetic
hands) or speed/precision in a pick and place (in industrial grippers).
Robotic grippers developed during last decades are mostly aimed to solve grasping
complexities of several objects as their primary objective. However, due to the increasing
demands of industries, many issues are rising and remain unsolved such as in-hand manipulation
and placing object with appropriate posture. Operations like twisting, altering
orientation of object within-hand, require significant dexterity of the gripper that must be
achieved from a compact mechanical design at the first place. Along with manipulation,
speed is also required in many robotic applications. Therefore, for the available speed and
design simplicity, nonprehensile or dynamic manipulation is widely exploited. The nonprehensile
approach however, does not focus on stable grasping in general. Also, nonprehensile
or dynamic manipulation often exceeds robot\u2019s kinematic workspace, which additionally
urges installation of high speed feedback and robust control. Hence, these approaches are
inapplicable especially when, the requirements are grasp oriented such as, precise posture
change of a payload in-hand, placing payload afterward according to a strict final configuration.
Also, addressing critical payload such as egg, contacts (between gripper and egg)
cannot be broken completely during manipulation. Moreover, theoretical analysis, such as
contact kinematics, grasp stability cannot predict the nonholonomic behaviors, and therefore,
uncertainties are always present to restrict a maneuver, even though the gripper is capable of
doing the task.
From a technical point of view, in-hand manipulation or within-hand dexterity of a gripper
significantly isolates grasping and manipulation skills from the dependencies on contact type,
a priory knowledge of object model, configurations such as initial or final postures and also
additional environmental constraints like disturbance, that may causes breaking of contacts
between object and finger. Hence, the property (in-hand manipulation) is important for a
gripper in order to obtain human hand skill.
In this research, these problems (to obtain speed, flexibility to a plurality of grasps,
within-hand dexterity in a single gripper) have been tackled in a novel way. A gripper
platform named Dexclar (DEXterous reConfigurable moduLAR) has been developed in order
to study in-hand manipulation, and a generic spherical payload has been considered at the
first place. Dexclar is mechanism-centric and it exploits modularity and reconfigurability to
the aim of achieving within-hand dexterity rather than utilizing soft materials. And hence,
precision, speed are also achievable from the platform. The platform can perform several
grasps (pinching, form closure, force closure) and address a very important issue of releasing
payload with final posture/ configuration after manipulation. By exploiting 16 degrees of
freedom (DoF), Dexclar is capable to provide 6 DoF motions to a generic spherical or
ellipsoidal payload. And since a mechanism is reliable, repeatable once it has been properly
synthesized, precision and speed are also obtainable from them. Hence Dexclar is an ideal
starting point to study within-hand dexterity from kinematic point of view.
As the final aim is to develop specific grippers (having the above capabilities) by exploiting
Dexclar, a highly dexterous but simply constructed reconfigurable platform named
VARO-fi (VARiable Orientable fingers with translation) is proposed, which can be used as
an industrial end-effector, as well as an alternative of bio-inspired gripper in many robotic
applications. The robust four fingered VARO-fi addresses grasp, in-hand manipulation and
release (payload with desired configuration) of plurality of payloads, as demonstrated in this
thesis.
Last but not the least, several tools and end-effectors have been constructed to study
prehensile and non-prehensile manipulation, thanks to Bayer Robotic challenge 2017, where
the feasibility and their potentiality to use them in an industrial environment have been
validated.
The above mentioned research will enhance a new dimension for designing grippers
with the properties of dexterity and flexibility at the same time, without explicit theoretical
analysis, algorithms, as those are difficult to implement and sometime not feasible for real
system
Grasping and Assembling with Modular Robots
A wide variety of problems, from manufacturing to disaster response and space exploration, can benefit from robotic systems that can firmly grasp objects or assemble various structures, particularly in difficult, dangerous environments. In this thesis, we study the two problems, robotic grasping and assembly, with a modular robotic approach that can facilitate the problems with versatility and robustness.
First, this thesis develops a theoretical framework for grasping objects with customized effectors that have curved contact surfaces, with applications to modular robots. We present a collection of grasps and cages that can effectively restrain the mobility of a wide range of objects including polyhedra. Each of the grasps or cages is formed by at most three effectors. A stable grasp is obtained by simple motion planning and control. Based on the theory, we create a robotic system comprised of a modular manipulator equipped with customized end-effectors and a software suite for planning and control of the manipulator.
Second, this thesis presents efficient assembly planning algorithms for constructing planar target structures collectively with a collection of homogeneous mobile modular robots. The algorithms are provably correct and address arbitrary target structures that may include internal holes. The resultant assembly plan supports parallel assembly and guarantees easy accessibility in the sense that a robot does not have to pass through a narrow gap while approaching its target position. Finally, we extend the algorithms to address various symmetric patterns formed by a collection of congruent rectangles on the plane.
The basic ideas in this thesis have broad applications to manufacturing (restraint), humanitarian missions (forming airfields on the high seas), and service robotics (grasping and manipulation)
Learning Object-level Impedance Control for Robust Grasping and Dexterous Manipulation
Object-level impedance control is of great importance for object-centric tasks, such as robust grasping and dexterous manipulation. Despite the recent progress on this topic, how to specify the desired object impedance for a given task remains an open issue. In this paper, we decompose the object’s impedance into two complementary components– the impedance for stable grasping and impedance for object manipulation. Then, we present a method to learn the desired object’s manipulation impedance (stiffness) using data obtained from human demonstration. The approach is validated in two tasks, for robust grasping of a wine glass and for inserting a bulb, using the 16 degrees of freedom Allegro Hand mounted with the SynTouch tactile sensor
Grasp Multiple Objects with One Hand
The human hand's complex kinematics allow for simultaneous grasping and
manipulation of multiple objects, essential for tasks like object transfer and
in-hand manipulation. Despite its importance, robotic multi-object grasping
remains underexplored and presents challenges in kinematics, dynamics, and
object configurations. This paper introduces MultiGrasp, a two-stage method for
multi-object grasping on a tabletop with a multi-finger dexterous hand. It
involves (i) generating pre-grasp proposals and (ii) executing the grasp and
lifting the objects. Experimental results primarily focus on dual-object
grasping and report a 44.13% success rate, showcasing adaptability to unseen
object configurations and imprecise grasps. The framework also demonstrates the
capability to grasp more than two objects, albeit at a reduced inference speed
Robust dexterous telemanipulation following object-orientation commands
This paper aims to present a procedure to change the orientation of a grasped object using dexterous manipulation. The manipulation is controlled by teleoperation in a very simple way, with the commands introduced by an operator using a keyboard. Design/methodology/approach - The paper shows a teleoperation scheme, hand kinematics and a manipulation strategy to manipulate different objects using the Schunk Dexterous Hand (SDH2). A state machine is used to model the teleoperation actions and the system states. A virtual link is used to include the contact point on the hand kinematics of the SDH2. Findings - Experiments were conducted to evaluate the proposed approach with different objects, varying the initial grasp configuration and the sequence of actions commanded by the operator. Originality/value - The proposed approach uses a shared telemanipulation schema to perform dexterous manipulation; in this schema, the operator sends high-level commands and a local system uses this information, jointly with tactile measurements and the current status of the system, to generate proper setpoints for the low-level control of the fingers, which may be a commercial close one. The main contribution of this work is the mentioned local system, simple enough for practical applications and robust enough to avoid object falls.Postprint (author's final draft
Haptic Exploration of Unknown Objects for Robust in-hand Manipulation.
Human-like robot hands provide the flexibility to manipulate a variety of objects that are found in unstructured environments. Knowledge of object properties and motion trajectory is required, but often not available in real-world manipulation tasks. Although it is possible to grasp and manipulate unknown objects, an uninformed grasp leads to inferior stability, accuracy, and repeatability of the manipulation. Therefore, a central challenge of in-hand manipulation in unstructured environments is to acquire this information safely and efficiently. We propose an in-hand manipulation framework that does not assume any prior information about the object and the motion, but instead extracts the object properties through a novel haptic exploration procedure and learns the motion from demonstration using dynamical movement primitives. We evaluate our approach by unknown object manipulation experiments using a human-like robot hand. The results show that haptic exploration improves the manipulation robustness and accuracy significantly, compared to the virtual spring framework baseline method that is widely used for grasping unknown objects
Robust dexterous telemanipulation following object-orientation commands
This paper aims to present a procedure to change the orientation of a grasped object using dexterous manipulation. The manipulation is controlled by teleoperation in a very simple way, with the commands introduced by an operator using a keyboard. Design/methodology/approach - The paper shows a teleoperation scheme, hand kinematics and a manipulation strategy to manipulate different objects using the Schunk Dexterous Hand (SDH2). A state machine is used to model the teleoperation actions and the system states. A virtual link is used to include the contact point on the hand kinematics of the SDH2. Findings - Experiments were conducted to evaluate the proposed approach with different objects, varying the initial grasp configuration and the sequence of actions commanded by the operator. Originality/value - The proposed approach uses a shared telemanipulation schema to perform dexterous manipulation; in this schema, the operator sends high-level commands and a local system uses this information, jointly with tactile measurements and the current status of the system, to generate proper setpoints for the low-level control of the fingers, which may be a commercial close one. The main contribution of this work is the mentioned local system, simple enough for practical applications and robust enough to avoid object falls.Postprint (author's final draft
Towards Transferring Tactile-based Continuous Force Control Policies from Simulation to Robot
The advent of tactile sensors in robotics has sparked many ideas on how
robots can leverage direct contact measurements of their environment
interactions to improve manipulation tasks. An important line of research in
this regard is that of grasp force control, which aims to manipulate objects
safely by limiting the amount of force exerted on the object. While prior works
have either hand-modeled their force controllers, employed model-based
approaches, or have not shown sim-to-real transfer, we propose a model-free
deep reinforcement learning approach trained in simulation and then transferred
to the robot without further fine-tuning. We therefore present a simulation
environment that produces realistic normal forces, which we use to train
continuous force control policies. An evaluation in which we compare against a
baseline and perform an ablation study shows that our approach outperforms the
hand-modeled baseline and that our proposed inductive bias and domain
randomization facilitate sim-to-real transfer. Code, models, and supplementary
videos are available on https://sites.google.com/view/rl-force-ctr
Grasp Point Optimization by Online Exploration of Unknown Object Surface
Li Q, Haschke R, Bolder B, Ritter H. Grasp Point Optimization by Online Exploration of Unknown Object Surface. Presented at the IEEE-RAS International Conference on Humanoid Robots, Osaka
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