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Found 17 papers, 8 papers with code
Towards Multi-Morphology Controllers with Diversity and Knowledge Distillation
Finding controllers that perform well across multiple morphologies is an important milestone for large-scale robotics, in line with recent advances via foundation models in other areas of machine learning.
Investigating Premature Convergence in Co-optimization of Morphology and Control in Evolved Virtual Soft Robots
We hope the insights we share with this work attract more attention to the problem and help us to enable efficient brain-body co-optimization.
Reset It and Forget It: Relearning Last-Layer Weights Improves Continual and Transfer Learning
We show that our zapping procedure results in improved transfer accuracy and/or more rapid adaptation in both standard fine-tuning and continual learning settings, while being simple to implement and computationally efficient.
Many-objective Optimization via Voting for Elites
Current approaches to many-objective optimization often require challenging assumptions, like knowledge of the importance/difficulty of objectives in a weighted-sum single-objective paradigm, or enormous populations to overcome the curse of dimensionality in multi-objective Pareto optimization.
Coping with seasons: evolutionary dynamics of gene networks in a changing environment
In contrast, conservative bet-hedgers have a set of offspring that all have an in-between phenotype compared to the specialists.
Modular Controllers Facilitate the Co-Optimization of Morphology and Control in Soft Robots
Soft robotics is a rapidly growing area of robotics research that would benefit greatly from design automation, given the challenges of manually engineering complex, compliant, and generally non-intuitive robot body plans and behaviors.
Picking up the pieces: separately evaluating supernet training and architecture selection
We frame supernet NAS as a two-stage search, decoupling the training of the supernet from the extraction of a final design from the supernet.
Continual learning under domain transfer with sparse synaptic bursting
Past efforts to engineer such systems have sought to build or regulate artificial neural networks using disjoint sets of weights that are uniquely sensitive to specific tasks or inputs.
Learning to Continually Learn
Continual lifelong learning requires an agent or model to learn many sequentially ordered tasks, building on previous knowledge without catastrophically forgetting it.
Embodiment dictates learnability in neural controllers
Catastrophic forgetting continues to severely restrict the learnability of controllers suitable for multiple task environments.
Interoceptive robustness through environment-mediated morphological development
Typically, AI researchers and roboticists try to realize intelligent behavior in machines by tuning parameters of a predefined structure (body plan and/or neural network architecture) using evolutionary or learning algorithms.
The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities
Biological evolution provides a creative fount of complex and subtle adaptations, often surprising the scientists who discover them.
How morphological development can guide evolution
Here, we report on a previously unknown phenomenon when embodied agents are allowed to develop and evolve: Evolution discovers body plans robust to control changes, these body plans become genetically assimilated, yet controllers for these agents are not assimilated.
Evolving soft locomotion in aquatic and terrestrial environments: effects of material properties and environmental transitions
Designing soft robots poses considerable challenges: automated design approaches may be particularly appealing in this field, as they promise to optimize complex multi-material machines with very little or no human intervention.
A Minimal Developmental Model Can Increase Evolvability in Soft Robots
Different subsystems of organisms adapt over many time scales, such as rapid changes in the nervous system (learning), slower morphological and neurological change over the lifetime of the organism (postnatal development), and change over many generations (evolution).
Scalable Co-Optimization of Morphology and Control in Embodied Machines
In psychology, the theory of embodied cognition posits that behavior arises from a close coupling between body plan and sensorimotor control, which suggests why co-optimizing these two subsystems is so difficult: most evolutionary changes to morphology tend to adversely impact sensorimotor control, leading to an overall decrease in behavioral performance.
Hands-free Evolution of 3D-printable Objects via Eye Tracking
Interactive evolution has shown the potential to create amazing and complex forms in both 2-D and 3-D settings.
