CPG-ACTOR: reinforcement learning for central pattern generators

CPG-ACTOR: reinforcement learning for central pattern generators

Central Pattern Generators (CPGs) have several properties desirable for locomotion: they generate smooth trajectories, are robust to perturbations and are simple to implement. However, they are notoriously difficult to tune and commonly operate in an open-loop manner. This paper proposes a new metho...

Bibliografski detalji
Glavni autori:	Campanaro, L, Gangapurwala, S, De Martini, D, Merkt, W, Havoutis, I
Format:	Conference item
Jezik:	English
Izdano:	Springer 2021

Slični predmeti

Learning and deploying robust locomotion policies with minimal dynamics randomization
od: Campanaro, L, i dr.
Izdano: (2024)

Roll-Drop: accounting for observation noise with a single parameter
od: Campanaro, L, i dr.
Izdano: (2023)

Learning low-frequency motion control for robust and dynamic robot locomotion
od: Gangapurwala, S, i dr.
Izdano: (2023)

Real-time trajectory adaptation for quadrupedal locomotion using deep reinforcement learning
od: Gangapurwala, S, i dr.
Izdano: (2021)

RLOC: terrain-aware legged locomotion using reinforcement learning and optimal control
od: Gangapurwala, S, i dr.
Izdano: (2022)

Tesseract: tensorised actors for multi−agent reinforcement learning
od: Mahajan, A, i dr.
Izdano: (2021)

Streamlined sim-to-real transfer for deep-reinforcement learning in robotics locomotion
od: Campanaro, L
Izdano: (2023)

VAE-Loco: versatile quadruped locomotion by learning a disentangled gait representation
od: Mitchell, AL, i dr.
Izdano: (2023)

Next steps: learning a disentangled gait representation for versatile quadruped locomotion
od: Mitchell, AL, i dr.
Izdano: (2022)

Guided constrained policy optimization for dynamic quadrupedal robot locomotion
od: Gangapurwala, S, i dr.
Izdano: (2020)

ASEAN as pivotal actor : balancing centrality and the Indo-Pacific
od: Ng, Joel
Izdano: (2020)

Memory clustering using persistent homology for multimodality- and discontinuity-sensitive learning of optimal control warm-starts
od: Merkt, WX, i dr.
Izdano: (2021)

Simultaneous scene reconstruction and whole-body motion planning for safe operation in dynamic environments
od: Finean, MN, i dr.
Izdano: (2021)

Where should I look? Optimized gaze control for whole-body collision avoidance in dynamic environments
od: Finean, MN, i dr.
Izdano: (2021)

Predicted composite signed-distance fields for real-time motion planning in dynamic environments
od: Finean, MN, i dr.
Izdano: (2021)

Learning system adaptive legged robotic locomotion policies
od: Gangapurwala, S
Izdano: (2022)

Association of the CpG methylation pattern of the proximal insulin gene promoter with type 1 diabetes.
od: Fradin, D, i dr.
Izdano: (2012)

Actor-Critic Policy Learning in Cooperative Planning
od: Redding, Joshua, i dr.
Izdano: (2013)

A soft actor-critic deep reinforcement learning method for multi-timescale coordinated operation of microgrids
od: Hu, Chunchao, i dr.
Izdano: (2023)

Actor Network Procedures
od: Pavlovic, D, i dr.
Izdano: (2012)

The Social Cognitive Actor: a multi−actor simulation of organisations
od: Helmhout, J
Izdano: (2006)

FUSION SPARSE AND SHAPING REWARD FUNCTION IN SOFT ACTOR-CRITIC DEEP REINFORCEMENT LEARNING FOR MOBILE ROBOT NAVIGATION
od: Abu Bakar, Mohamad Hafiz, i dr.
Izdano: (2024)

Computer animation with scripts and actors /
od: 216277 Reynolds, Craig W.

DAC: The Double Actor-Critic Architecture for Learning Options
od: Zhang, S, i dr.
Izdano: (2019)

Understanding the role of CFP1 at CpG islands
od: Brown, D
Izdano: (2014)

Actor machine architecture.
od: Steiger, Richard John.
Izdano: (2024)

Actor-critic algorithms
od: Konda, Vijaymohan (Vijaymohan Gao), 1973-
Izdano: (2005)

Actors and Continuous Functionals
od: Hewitt, Carl, i dr.
Izdano: (2023)

Actors and Continuous Functionals
od: Hewitt, Carl, i dr.
Izdano: (2004)

Actor-Network Theory
od: Lezaun, J
Izdano: (2017)

Foundations of Actor Semantics
od: Clinger, William Douglas
Izdano: (2004)

Exploration of network centrality in goal conditioned reinforcement learning
od: Sharma Divyansh
Izdano: (2024)

Patterns of preference and practice: bridging actors in wildfire response networks in the American Northwest
od: Faas, A, i dr.
Izdano: (2016)

Sparsity-inducing optimal control via differential dynamic programming
od: Dinev, T, i dr.
Izdano: (2021)

Asymptotically optimized multi-surface coverage path planning for loco-manipulation in inspection and monitoring
od: Ly, KT, i dr.
Izdano: (2023)

Momentum-aware trajectory optimisation using full-centroidal dynamics and implicit inverse kinematics
od: Papatheodorou, A, i dr.
Izdano: (2024)

Motion planning in dynamic environments using context-aware human trajectory prediction
od: Finean, MN, i dr.
Izdano: (2023)

Generalized Off-Policy Actor-Critic
od: Zhang, S, i dr.
Izdano: (2019)

Procedural Material Generation with Reinforcement Learning
od: Li, Beichen, i dr.
Izdano: (2024)

Optimal Production Planning Strategies for Global CPG Company
od: Sakr, Omar Mahmoud
Izdano: (2021)