385 research outputs found
A Certified-Complete Bimanual Manipulation Planner
Planning motions for two robot arms to move an object collaboratively is a
difficult problem, mainly because of the closed-chain constraint, which arises
whenever two robot hands simultaneously grasp a single rigid object. In this
paper, we propose a manipulation planning algorithm to bring an object from an
initial stable placement (position and orientation of the object on the support
surface) towards a goal stable placement. The key specificity of our algorithm
is that it is certified-complete: for a given object and a given environment,
we provide a certificate that the algorithm will find a solution to any
bimanual manipulation query in that environment whenever one exists. Moreover,
the certificate is constructive: at run-time, it can be used to quickly find a
solution to a given query. The algorithm is tested in software and hardware on
a number of large pieces of furniture.Comment: 12 pages, 7 figures, 1 tabl
Dexterous Manipulation Graphs
We propose the Dexterous Manipulation Graph as a tool to address in-hand
manipulation and reposition an object inside a robot's end-effector. This graph
is used to plan a sequence of manipulation primitives so to bring the object to
the desired end pose. This sequence of primitives is translated into motions of
the robot to move the object held by the end-effector. We use a dual arm robot
with parallel grippers to test our method on a real system and show successful
planning and execution of in-hand manipulation
The Penn Jerboa: A Platform for Exploring Parallel Composition of Templates
We have built a 12DOF, passive-compliant legged, tailed biped actuated by
four brushless DC motors. We anticipate that this machine will achieve varied
modes of quasistatic and dynamic balance, enabling a broad range of locomotion
tasks including sitting, standing, walking, hopping, running, turning, leaping,
and more. Achieving this diversity of behavior with a single under-actuated
body, requires a correspondingly diverse array of controllers, motivating our
interest in compositional techniques that promote mixing and reuse of a
relatively few base constituents to achieve a combinatorially growing array of
available choices. Here we report on the development of one important example
of such a behavioral programming method, the construction of a novel monopedal
sagittal plane hopping gait through parallel composition of four decoupled 1DOF
base controllers.
For this example behavior, the legs are locked in phase and the body is
fastened to a boom to restrict motion to the sagittal plane. The platform's
locomotion is powered by the hip motor that adjusts leg touchdown angle in
flight and balance in stance, along with a tail motor that adjusts body shape
in flight and drives energy into the passive leg shank spring during stance.
The motor control signals arise from the application in parallel of four
simple, completely decoupled 1DOF feedback laws that provably stabilize in
isolation four corresponding 1DOF abstract reference plants. Each of these
abstract 1DOF closed loop dynamics represents some simple but crucial specific
component of the locomotion task at hand. We present a partial proof of
correctness for this parallel composition of template reference systems along
with data from the physical platform suggesting these templates are anchored as
evidenced by the correspondence of their characteristic motions with a suitably
transformed image of traces from the physical platform.Comment: Technical Report to Accompany: A. De and D. Koditschek, "Parallel
composition of templates for tail-energized planar hopping," in 2015 IEEE
International Conference on Robotics and Automation (ICRA), May 2015. v2:
Used plain latex article, correct gap radius and specific force/torque
number
SAR: Generalization of Physiological Agility and Dexterity via Synergistic Action Representation
Learning effective continuous control policies in high-dimensional systems,
including musculoskeletal agents, remains a significant challenge. Over the
course of biological evolution, organisms have developed robust mechanisms for
overcoming this complexity to learn highly sophisticated strategies for motor
control. What accounts for this robust behavioral flexibility? Modular control
via muscle synergies, i.e. coordinated muscle co-contractions, is considered to
be one putative mechanism that enables organisms to learn muscle control in a
simplified and generalizable action space. Drawing inspiration from this
evolved motor control strategy, we use physiologically accurate human hand and
leg models as a testbed for determining the extent to which a Synergistic
Action Representation (SAR) acquired from simpler tasks facilitates learning
more complex tasks. We find in both cases that SAR-exploiting policies
significantly outperform end-to-end reinforcement learning. Policies trained
with SAR were able to achieve robust locomotion on a wide set of terrains with
high sample efficiency, while baseline approaches failed to learn meaningful
behaviors. Additionally, policies trained with SAR on a multiobject
manipulation task significantly outperformed (>70% success) baseline approaches
(<20% success). Both of these SAR-exploiting policies were also found to
generalize zero-shot to out-of-domain environmental conditions, while policies
that did not adopt SAR failed to generalize. Finally, we establish the
generality of SAR on broader high-dimensional control problems using a robotic
manipulation task set and a full-body humanoid locomotion task. To the best of
our knowledge, this investigation is the first of its kind to present an
end-to-end pipeline for discovering synergies and using this representation to
learn high-dimensional continuous control across a wide diversity of tasks.Comment: Accepted to RSS 202
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