8 citations · 17 across the 5 of their papers we have counts for
5 papers
Arnold: A multi-task, multi-embodiment muscle transformer policy
Boshi An, Alberto Silvio Chiappa, Merkourios Simos +2
Controlling high-dimensional and nonlinear musculoskeletal models of the human body is a foundational scientific challenge. Recent machine learning breakthroughs have heralded in-s…
KINESIS: Motion Imitation for Human Musculoskeletal Locomotion
Merkourios Simos, Alberto Silvio Chiappa, Alexander Mathis
How do humans move? Advances in reinforcement learning (RL) have produced impressive results in capturing human motion using physics-based humanoid control. However, torque-control…
Reducing the Drag of a Bluff Body by Deep Reinforcement Learning
Enrico Ballini, Alberto Silvio Chiappa, Stefano Micheletti
We present a deep reinforcement learning approach to a classical problem in fluid dynamics, i.e., the reduction of the drag of a bluff body. We cast the problem as a discrete-time…
Latent Exploration for Reinforcement Learning
Alberto Silvio Chiappa, Alessandro Marin Vargas, Ann Zixiang Huang +1
In Reinforcement Learning, agents learn policies by exploring and interacting with the environment. Due to the curse of dimensionality, learning policies that map high-dimensional…
DMAP: a Distributed Morphological Attention Policy for Learning to Locomote with a Changing Body
Alberto Silvio Chiappa, Alessandro Marin Vargas, Alexander Mathis
Biological and artificial agents need to deal with constant changes in the real world. We study this problem in four classical continuous control environments, augmented with morph…