Fetching the paper…
Reading the bibliography…
An agent's functionality is largely determined by its design, i.e., skeletal structure and joint attributes (e.g., length, size, strength).
Evolving virtual creatures
Karl Sims · 1994
Earlier work this paper cites.
The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
Earlier work this paper cites.
Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
Earlier work this paper cites.
Spectral networks and locally connected networks on graphs
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun · 2013
Earlier work this paper cites.
Unshackling evolution: evolving soft robots with multiple materials and a powerful generative encoding
Nick Cheney, Robert MacCurdy, Jeff Clune, and Hod Lipson · 2014
Earlier work this paper cites.
High-dimensional continuous control using generalized advantage estimation
John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel · 2015
Earlier work this paper cites.
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
Earlier work this paper cites.
Asymptotically optimal design of piecewise cylindrical robots using motion planning
Cenk Baykal and Ron Alterovitz · 2017
Earlier work this paper cites.
Computational abstractions for interactive design of robotic devices
Ruta Desai, Ye Yuan, and Stelian Coros · 2017
Earlier work this paper cites.
Joint optimization of robot design and motion parameters using the implicit function theorem
Sehoon Ha, Stelian Coros, Alexander Alspach, Joohyung Kim, and Katsu Yamane · 2017
Earlier work this paper cites.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Cited alongside, same era.
Scalable co-optimization of morphology and control in embodied machines
Nick Cheney, Josh Bongard, Vytas SunSpiral, and Hod Lipson · 2018
Cited alongside, same era.
Interactive co-design of form and function for legged robots using the adjoint method
Ruta Desai, Beichen Li, Ye Yuan, and Stelian Coros · 2018
Cited alongside, same era.
Computational co-optimization of design parameters and motion trajectories for robotic systems
Sehoon Ha, Stelian Coros, Alexander Alspach, Joohyung Kim, and Katsu Yamane · 2018
Cited alongside, same era.
Nervenet: Learning structured policy with graph neural networks
Tingwu Wang, Renjie Liao, Jimmy Ba, and Sanja Fidler · 2018
Cited alongside, same era.
Jointly learning to construct and control agents using deep reinforcement learning
Charles Schaff, David Yunis, Ayan Chakrabarti, and Matthew R Walter · 2019
Later among the works it cites.
Contrasting exploration in parameter and action space: A zeroth-order optimization perspective
Anirudh Vemula, Wen Sun, and J Bagnell · 2019
Later among the works it cites.
Neural graph evolution: Towards efficient automatic robot design
Tingwu Wang, Yuhao Zhou, Sanja Fidler, and Jimmy Ba · 2019
Later among the works it cites.
Hardware as policy: Mechanical and computational co-optimization using deep reinforcement learning
Tianjian Chen, Zhanpeng He, and Matei Ciocarlie · 2020
Later among the works it cites.
Policy transfer via kinematic domain randomization and adaptation
Ioannis Exarchos, Yifeng Jiang, Wenhao Yu, and C Karen Liu · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Policy transfer with strategy optimization
Wenhao Yu, C Karen Liu, and Greg Turk · 2018
Cited alongside, same era.
Fast graph representation learning with pytorch geometric
Matthias Fey and Jan Eric Lenssen · 2019
Cited alongside, same era.
Reinforcement learning for improving agent design
David Ha · 2019
Cited alongside, same era.
Weisfeiler and leman go neural: Higher-order graph neural networks
Christopher Morris, Martin Ritzert, Matthias Fey, William L Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Cited alongside, same era.
Learning to control self-assembling morphologies: a study of generalization via modularity
Deepak Pathak, Chris Lu, Trevor Darrell, Phillip Isola, and Alexei A Efros · 2019
Cited alongside, same era.
Later among the works it cites.
One policy to control them all: Shared modular policies for agent-agnostic control
Wenlong Huang, Igor Mordatch, and Deepak Pathak · 2020
Later among the works it cites.
Data-efficient co-adaptation of morphology and behaviour with deep reinforcement learning
Kevin Sebastian Luck, Heni Ben Amor, and Roberto Calandra · 2020
Later among the works it cites.
A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip · 2020
Later among the works it cites.
Task-agnostic morphology evolution
Donald Hejna, Pieter Abbeel, and Lerrel Pinto · 2021
Closest in time.
Simgan: Hybrid simulator identification for domain adaptation via adversarial reinforcement learning
Yifeng Jiang, Tingnan Zhang, Daniel Ho, Yunfei Bai, C Karen Liu, Sergey Levine, and Jie Tan · 2021
Closest in time.
My body is a cage: the role of morphology in graph-based incompatible control
Vitaly Kurin, Maximilian Igl, Tim Rocktäschel, Wendelin Boehmer, and Shimon Whiteson · 2021
Closest in time.