214 research outputs found
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
Robot Composite Learning and the Nunchaku Flipping Challenge
Advanced motor skills are essential for robots to physically coexist with
humans. Much research on robot dynamics and control has achieved success on
hyper robot motor capabilities, but mostly through heavily case-specific
engineering. Meanwhile, in terms of robot acquiring skills in a ubiquitous
manner, robot learning from human demonstration (LfD) has achieved great
progress, but still has limitations handling dynamic skills and compound
actions. In this paper, we present a composite learning scheme which goes
beyond LfD and integrates robot learning from human definition, demonstration,
and evaluation. The method tackles advanced motor skills that require dynamic
time-critical maneuver, complex contact control, and handling partly soft
partly rigid objects. We also introduce the "nunchaku flipping challenge", an
extreme test that puts hard requirements to all these three aspects. Continued
from our previous presentations, this paper introduces the latest update of the
composite learning scheme and the physical success of the nunchaku flipping
challenge
Planning hand-arm grasping motions with human-like appearance
© 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 worksFinalista de l’IROS Best Application Paper Award a la 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, ICROS.This paper addresses the problem of obtaining human-like motions on hand-arm robotic systems performing pick-and-place actions. The focus is set on the coordinated movements of the robotic arm and the anthropomorphic mechanical hand, with which the arm is equipped. For this, human movements performing different grasps are captured and mapped to the robot in order to compute the human hand synergies. These synergies are used to reduce the complexity of the planning phase by reducing the dimension of the search space. In addition, the paper proposes a sampling-based planner, which guides the motion planning ollowing the synergies. The introduced approach is tested in an application example and thoroughly compared with other state-of-the-art planning algorithms, obtaining better results.Peer ReviewedAward-winningPostprint (author's final draft
FRoGGeR: Fast Robust Grasp Generation via the Min-Weight Metric
Many approaches to grasp synthesis optimize analytic quality metrics that
measure grasp robustness based on finger placements and local surface geometry.
However, generating feasible dexterous grasps by optimizing these metrics is
slow, often taking minutes. To address this issue, this paper presents FRoGGeR:
a method that quickly generates robust precision grasps using the min-weight
metric, a novel, almost-everywhere differentiable approximation of the
classical epsilon grasp metric. The min-weight metric is simple and
interpretable, provides a reasonable measure of grasp robustness, and admits
numerically efficient gradients for smooth optimization. We leverage these
properties to rapidly synthesize collision-free robust grasps - typically in
less than a second. FRoGGeR can refine the candidate grasps generated by other
methods (heuristic, data-driven, etc.) and is compatible with many object
representations (SDFs, meshes, etc.). We study FRoGGeR's performance on over 40
objects drawn from the YCB dataset, outperforming a competitive baseline in
computation time, feasibility rate of grasp synthesis, and picking success in
simulation. We conclude that FRoGGeR is fast: it has a median synthesis time of
0.834s over hundreds of experiments.Comment: Accepted at IROS 2023. The arXiv version contains the appendix, which
does not appear in the conference versio
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