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Decoding the brain: From neural representations to mechanistic models
A central principle in neuroscience is that neurons within the brain act in concert to produce
perception, cognition, and adaptive behavior. Neurons are organized into specialized brain …
perception, cognition, and adaptive behavior. Neurons are organized into specialized brain …
Pink noise is all you need: Colored noise exploration in deep reinforcement learning
In off-policy deep reinforcement learning with continuous action spaces, exploration is often
implemented by injecting action noise into the action selection process. Popular algorithms …
implemented by injecting action noise into the action selection process. Popular algorithms …
Latent exploration for reinforcement learning
AS Chiappa, A Marin Vargas… - Advances in Neural …, 2023 - proceedings.neurips.cc
Abstract In Reinforcement Learning, agents learn policies by exploring and interacting with
the environment. Due to the curse of dimensionality, learning policies that map high …
the environment. Due to the curse of dimensionality, learning policies that map high …
Sar: Generalization of physiological agility and dexterity via synergistic action representation
C Berg, V Caggiano, V Kumar - Autonomous Robots, 2024 - Springer
Learning effective continuous control policies in high-dimensional systems, including
musculoskeletal agents, remains a significant challenge. Over the course of biological …
musculoskeletal agents, remains a significant challenge. Over the course of biological …
[HTML][HTML] Acquiring musculoskeletal skills with curriculum-based reinforcement learning
Efficient musculoskeletal simulators and powerful learning algorithms provide computational
tools to tackle the grand challenge of understanding biological motor control. Our winning …
tools to tackle the grand challenge of understanding biological motor control. Our winning …
Musclevae: Model-based controllers of muscle-actuated characters
In this paper, we present a simulation and control framework for generating biomechanically
plausible motion for muscle-actuated characters. We incorporate a fatigue dynamics model …
plausible motion for muscle-actuated characters. We incorporate a fatigue dynamics model …
Myodex: a generalizable prior for dexterous manipulation
Human dexterity is a hallmark of motor control behaviors. Our hands can rapidly synthesize
new behaviors despite the complexity (multi-articular and multi-joints, with 23 joints …
new behaviors despite the complexity (multi-articular and multi-joints, with 23 joints …
Natural and robust walking using reinforcement learning without demonstrations in high-dimensional musculoskeletal models
Humans excel at robust bipedal walking in complex natural environments. In each step, they
adequately tune the interaction of biomechanical muscle dynamics and neuronal signals to …
adequately tune the interaction of biomechanical muscle dynamics and neuronal signals to …
Myochallenge 2022: Learning contact-rich manipulation using a musculoskeletal hand
Manual dexterity has been considered one of the critical components for human evolution.
The ability to perform movements as simple as holding and rotating an object in the hand …
The ability to perform movements as simple as holding and rotating an object in the hand …
Open the black box: Step-based policy updates for temporally-correlated episodic reinforcement learning
Current advancements in reinforcement learning (RL) have predominantly focused on
learning step-based policies that generate actions for each perceived state. While these …
learning step-based policies that generate actions for each perceived state. While these …