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Found 8 papers, 2 papers with code
Identifying Policy Gradient Subspaces
Policy gradient methods hold great potential for solving complex continuous control tasks.
Investigating the Impact of Action Representations in Policy Gradient Algorithms
Reinforcement learning~(RL) is a versatile framework for learning to solve complex real-world tasks.
Hindsight States: Blending Sim and Real Task Elements for Efficient Reinforcement Learning
Reinforcement learning has shown great potential in solving complex tasks when large amounts of data can be generated with little effort.
Learning with Muscles: Benefits for Data-Efficiency and Robustness in Anthropomorphic Tasks
Humans are able to outperform robots in terms of robustness, versatility, and learning of new tasks in a wide variety of movements.
DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal Systems
Muscle-actuated organisms are capable of learning an unparalleled diversity of dexterous movements despite their vast amount of muscles.
Hierarchical Reinforcement Learning with Timed Subgoals
Hierarchical reinforcement learning (HRL) holds great potential for sample-efficient learning on challenging long-horizon tasks.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Learning to Play Table Tennis From Scratch using Muscular Robots
This work is the first to (a) fail-safe learn of a safety-critical dynamic task using anthropomorphic robot arms, (b) learn a precision-demanding problem with a PAM-driven system despite the control challenges and (c) train robots to play table tennis without real balls.
Learning to Control Highly Accelerated Ballistic Movements on Muscular Robots
High-speed and high-acceleration movements are inherently hard to control.
