210 research outputs found
Evolvability signatures of generative encodings: beyond standard performance benchmarks
Evolutionary robotics is a promising approach to autonomously synthesize
machines with abilities that resemble those of animals, but the field suffers
from a lack of strong foundations. In particular, evolutionary systems are
currently assessed solely by the fitness score their evolved artifacts can
achieve for a specific task, whereas such fitness-based comparisons provide
limited insights about how the same system would evaluate on different tasks,
and its adaptive capabilities to respond to changes in fitness (e.g., from
damages to the machine, or in new situations). To counter these limitations, we
introduce the concept of "evolvability signatures", which picture the
post-mutation statistical distribution of both behavior diversity (how
different are the robot behaviors after a mutation?) and fitness values (how
different is the fitness after a mutation?). We tested the relevance of this
concept by evolving controllers for hexapod robot locomotion using five
different genotype-to-phenotype mappings (direct encoding, generative encoding
of open-loop and closed-loop central pattern generators, generative encoding of
neural networks, and single-unit pattern generators (SUPG)). We observed a
predictive relationship between the evolvability signature of each encoding and
the number of generations required by hexapods to adapt from incurred damages.
Our study also reveals that, across the five investigated encodings, the SUPG
scheme achieved the best evolvability signature, and was always foremost in
recovering an effective gait following robot damages. Overall, our evolvability
signatures neatly complement existing task-performance benchmarks, and pave the
way for stronger foundations for research in evolutionary robotics.Comment: 24 pages with 12 figures in the main text, and 4 supplementary
figures. Accepted at Information Sciences journal (in press). Supplemental
videos are available online at, see http://goo.gl/uyY1R
In silico case studies of compliant robots: AMARSI deliverable 3.3
In the deliverable 3.2 we presented how the morphological computing ap-
proach can significantly facilitate the control strategy in several scenarios,
e.g. quadruped locomotion, bipedal locomotion and reaching. In particular,
the Kitty experimental platform is an example of the use of morphological
computation to allow quadruped locomotion. In this deliverable we continue
with the simulation studies on the application of the different morphological
computation strategies to control a robotic system
CPG-RL: Learning Central Pattern Generators for Quadruped Locomotion
In this letter, we present a method for integrating central pattern
generators (CPGs), i.e. systems of coupled oscillators, into the deep
reinforcement learning (DRL) framework to produce robust and omnidirectional
quadruped locomotion. The agent learns to directly modulate the intrinsic
oscillator setpoints (amplitude and frequency) and coordinate rhythmic behavior
among different oscillators. This approach also allows the use of DRL to
explore questions related to neuroscience, namely the role of descending
pathways, interoscillator couplings, and sensory feedback in gait generation.
We train our policies in simulation and perform a sim-to-real transfer to the
Unitree A1 quadruped, where we observe robust behavior to disturbances unseen
during training, most notably to a dynamically added 13.75 kg load representing
115% of the nominal quadruped mass. We test several different observation
spaces based on proprioceptive sensing and show that our framework is
deployable with no domain randomization and very little feedback, where along
with the oscillator states, it is possible to provide only contact booleans in
the observation space. Video results can be found at
https://youtu.be/xqXHLzLsEV4.Comment: Accepted for IEEE Robotics and Automation Letters, September 202
Gait transition and modulation in a quadruped robot : a brainstem-like modulation approach
In this article, we propose a bio-inspired architecture for a quadruped robot that is able to initiate/stop
locomotion; generate different gaits, and to easily select and switch between the different gaits according
to the speed and/or the behavioral context. This improves the robot stability and smoothness while
locomoting.
We apply nonlinear oscillators to model Central Pattern Generators (CPGs). These generate the
rhythmic locomotor movements for a quadruped robot. The generated trajectories are modulated by a
tonic signal, that encodes the required activity and/or modulation. This drive signal strength is mapped
onto sets of CPG parameters. By increasing the drive signal, locomotion can be elicited and velocity
increased while switching to the appropriate gaits. This drive signal can be specified according to sensory
information or set a priori.
The system is implemented in a simulated and real AIBO robot. Results demonstrate the adequacy of
the architecture to generate and modulate the required coordinated trajectories according to a velocity
increase; and to smoothly and easily switch among the different motor behaviors.The authors gratefully acknowledge Keir Pearson for all the discussions and help. This work is funded by FEDER Funding supported by the Operational Program Competitive Factors COMPETE and National Funding supported by the FCT - Foundation for Science and Technology through project PTDC/EEACRO/100655/2008
Kinematic primitives for walking and trotting gaits of a quadruped robot with compliant legs
In this work we research the role of body dynamics in the complexity of kinematic patterns in a quadruped robot with compliant legs. Two gait patterns, lateral sequence walk and trot, along with leg length control patterns of different complexity were implemented in a modular, feed-forward locomotion controller. The controller was tested on a small, quadruped robot with compliant, segmented leg design, and led to self-stable and self-stabilizing robot locomotion. In-air stepping and on-ground locomotion leg kinematics were recorded, and the number and shapes of motion primitives accounting for 95% of the variance of kinematic leg data were extracted. This revealed that kinematic patterns resulting from feed-forward control had a lower complexity (in-air stepping, 2 to 3 primitives) than kinematic patterns from on-ground locomotion (4 primitives), although both experiments applied identical motor patterns. The complexity of on-ground kinematic patterns had increased, through ground contact and mechanical entrainment. The complexity of observed kinematic on-ground data matches those reported from level-ground locomotion data of legged animals. Results indicate that a very low complexity of modular, rhythmic, feed-forward motor control is sufficient for level-ground locomotion in combination with passive compliant legged hardware
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