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Found 5 papers, 3 papers with code
Deriving Rewards for Reinforcement Learning from Symbolic Behaviour Descriptions of Bipedal Walking
Generating physical movement behaviours from their symbolic description is a long-standing challenge in artificial intelligence (AI) and robotics, requiring insights into numerical optimization methods as well as into formalizations from symbolic AI and reasoning.
End-to-End Reinforcement Learning for Torque Based Variable Height Hopping
Legged locomotion is arguably the most suited and versatile mode to deal with natural or unstructured terrains.
A Development Cycle for Automated Self-Exploration of Robot Behaviors
In this paper we introduce Q-Rock, a development cycle for the automated self-exploration and qualification of robot behaviors.
Combinatorics of a Discrete Trajectory Space for Robot Motion Planning
Motion planning is a difficult problem in robot control.
Comparison of Distal Teacher Learning with Numerical and Analytical Methods to Solve Inverse Kinematics for Rigid-Body Mechanisms
We argue that for rigid-body kinematics one of the first proposed machine learning (ML) solutions to inverse kinematics -- distal teaching (DT) -- is actually good enough when combined with differentiable programming libraries and we provide an extensive evaluation and comparison to analytical and numerical solutions.
