124 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
MOTION CONTROL SIMULATION OF A HEXAPOD ROBOT
This thesis addresses hexapod robot motion control. Insect morphology and locomotion patterns inform the design of a robotic model, and motion control is achieved via trajectory planning and bio-inspired principles. Additionally, deep learning and multi-agent reinforcement learning are employed to train the robot motion control strategy with leg coordination achieves using a multi-agent deep reinforcement learning framework. The thesis makes the following contributions:
First, research on legged robots is synthesized, with a focus on hexapod robot motion control. Insect anatomy analysis informs the hexagonal robot body and three-joint single robotic leg design, which is assembled using SolidWorks. Different gaits are studied and compared, and robot leg kinematics are derived and experimentally verified, culminating in a three-legged gait for motion control.
Second, an animal-inspired approach employs a central pattern generator (CPG) control unit based on the Hopf oscillator, facilitating robot motion control in complex environments such as stable walking and climbing. The robot\u27s motion process is quantitatively evaluated in terms of displacement change and body pitch angle.
Third, a value function decomposition algorithm, QPLEX, is applied to hexapod robot motion control. The QPLEX architecture treats each leg as a separate agent with local control modules, that are trained using reinforcement learning. QPLEX outperforms decentralized approaches, achieving coordinated rhythmic gaits and increased robustness on uneven terrain. The significant of terrain curriculum learning is assessed, with QPLEX demonstrating superior stability and faster consequence.
The foot-end trajectory planning method enables robot motion control through inverse kinematic solutions but has limited generalization capabilities for diverse terrains. The animal-inspired CPG-based method offers a versatile control strategy but is constrained to core aspects. In contrast, the multi-agent deep reinforcement learning-based approach affords adaptable motion strategy adjustments, rendering it a superior control policy. These methods can be combined to develop a customized robot motion control policy for specific scenarios
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