31 research outputs found
Object Dexterous Manipulation in Hand Based on Finite State Machine
Li Q, Meier M, Haschke R, Ritter H, Bolder B. Object Dexterous Manipulation in Hand Based on Finite State Machine. In: Proc. ICMA2012. 2012: 1185-1190
Finding antipodal point grasps on irregularly shaped objects
Two-finger antipodal point grasping of arbitrarily shaped smooth 2-D and 3-D objects is considered. An object function is introduced that maps a finger contact space to the object surface. Conditions are developed to identify the feasible grasping region, F, in the finger contact space. A “grasping energy function”, E , is introduced which is proportional to the distance between two grasping points. The antipodal points correspond to critical points of E in F. Optimization and/or continuation techniques are used to find these critical points. In particular, global optimization techniques are applied to find the “maximal” or “minimal” grasp. Further, modeling techniques are introduced for representing 2-D and 3-D objects using B-spline curves and spherical product surfaces
A Developmental Organization for Robot Behavior
This paper focuses on exploring how learning and development can be structured in synthetic (robot) systems. We present a developmental assembler for constructing reusable and temporally extended actions in a sequence. The discussion adopts the traditions
of dynamic pattern theory in which behavior
is an artifact of coupled dynamical systems
with a number of controllable degrees of freedom. In our model, the events that delineate
control decisions are derived from the pattern
of (dis)equilibria on a working subset of sensorimotor policies. We show how this architecture can be used to accomplish sequential
knowledge gathering and representation tasks
and provide examples of the kind of developmental milestones that this approach has
already produced in our lab
Sampling-based Exploration for Reinforcement Learning of Dexterous Manipulation
In this paper, we present a novel method for achieving dexterous manipulation
of complex objects, while simultaneously securing the object without the use of
passive support surfaces. We posit that a key difficulty for training such
policies in a Reinforcement Learning framework is the difficulty of exploring
the problem state space, as the accessible regions of this space form a complex
structure along manifolds of a high-dimensional space. To address this
challenge, we use two versions of the non-holonomic Rapidly-Exploring Random
Trees algorithm; one version is more general, but requires explicit use of the
environment's transition function, while the second version uses
manipulation-specific kinematic constraints to attain better sample efficiency.
In both cases, we use states found via sampling-based exploration to generate
reset distributions that enable training control policies under full dynamic
constraints via model-free Reinforcement Learning. We show that these policies
are effective at manipulation problems of higher difficulty than previously
shown, and also transfer effectively to real robots. Videos of the real-hand
demonstrations can be found on the project website:
https://sbrl.cs.columbia.edu/Comment: 10 pages, 6 figures, submitted to Robotics Science & Systems 202
Real-Time Motion Planning for In-Hand Manipulation with a Multi-Fingered Hand
Dexterous manipulation of objects once held in hand remains a challenge. Such
skills are, however, necessary for robotics to move beyond gripper-based
manipulation and use all the dexterity offered by anthropomorphic robotic
hands. One major challenge when manipulating an object within the hand is that
fingers must move around the object while avoiding collision with other fingers
or the object. Such collision-free paths must be computed in real-time, as the
smallest deviation from the original plan can easily lead to collisions. We
present a real-time approach to computing collision-free paths in a
high-dimensional space. To guide the exploration, we learn an explicit
representation of the free space, retrievable in real-time. We further combine
this representation with closed-loop control via dynamical systems and
sampling-based motion planning and show that the combination increases
performance compared to alternatives, offering efficient search of feasible
paths and real-time obstacle avoidance in a multi-fingered robotic hand
Dexterous manipulation planning using probabilistic roadmaps in continuous grasp subspaces
In this paper, we propose a new method for the motion planning problem of rigid object dexterous manipulation with a robotic multi-fingered hand, under quasi-static movement assumption. This method computes both object and finger trajectories as well as the finger relocation sequence. Its specificity is to use a special structuring of the research space that allows to search for paths directly in the particular subspace GSn which is the subspace of all the grasps that can be achieved with n grasping fingers. The solving of the dexterous manipulation planning problem is based upon the exploration of this subspace. The proposed approach captures the connectivity of GSn in a graph structure. The answer of the manipulation planning query is then given by searching a path in the computed graph. Simulation experiments were conducted for different dexterous manipulation task examples to validate the proposed method
Proceedings of the NASA Conference on Space Telerobotics, volume 1
The theme of the Conference was man-machine collaboration in space. Topics addressed include: redundant manipulators; man-machine systems; telerobot architecture; remote sensing and planning; navigation; neural networks; fundamental AI research; and reasoning under uncertainty