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Automatic Control and System Dynamics Laboratory, Technische Universität Chemnitz, Chemnitz, Germany
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Explicitly leveraging Lyapunov functions within ADP simultaneously optimizes control policies and guarantees closed-loop stability, overcoming a fundamental limitation of prior methods.
Training efficiency skyrockets as RL policies leverage existing suboptimal baselines, achieving superior performance without starting from scratch.
Finally, a rigorous RL benchmark: generate environments with *provably* optimal policies, enabling controlled algorithm evaluation against ground truth.
Swap that pricey VR headset for a smartphone: this open-source teleoperation framework delivers intuitive whole-body control of mobile manipulators using readily available commodity hardware.