Search Results for author:
Found 13 papers, 8 papers with code
Reinforcement learning for freeform robot design
Inspired by the necessity of morphological adaptation in animals, a growing body of work has attempted to expand robot training to encompass physical aspects of a robot's design.
Glamour muscles: why having a body is not what it means to be embodied
Embodiment has recently enjoyed renewed consideration as a means to amplify the faculties of smart machines.
Efficient automatic design of robots
Robots are notoriously difficult to design because of complex interdependencies between their physical structure, sensory and motor layouts, and behavior.
Scale invariant robot behavior with fractals
Finally, we show that biobots can spontaneously exhibit self similar attachment geometries, thereby suggesting that self similar behavior via self similar structure may be realizable across a wide range of robot platforms in future.
Scalable sim-to-real transfer of soft robot designs
The manual design of soft robots and their controllers is notoriously challenging, but it could be augmented---or, in some cases, entirely replaced---by automated design tools.
Embodiment dictates learnability in neural controllers
Catastrophic forgetting continues to severely restrict the learnability of controllers suitable for multiple task environments.
Word2vec to behavior: morphology facilitates the grounding of language in machines
Enabling machines to respond appropriately to natural language commands could greatly expand the number of people to whom they could be of service.
Automated shapeshifting for function recovery in damaged robots
We found that, especially in the case of "deep insult", such as removal of all four of the robot's legs, the damaged machine evolves shape changes that not only recover the original level of function (locomotion) as before, but can in fact surpass the original level of performance (speed).
Combating catastrophic forgetting with developmental compression
Generally intelligent agents exhibit successful behavior across problems in several settings.
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.
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 Spatially Aggregated Features from Satellite Imagery for Regional Modeling
Satellite imagery and remote sensing provide explanatory variables at relatively high resolutions for modeling geospatial phenomena, yet regional summaries are often desirable for analysis and actionable insight.
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).
