Applying Sums-of-Squares Optimization for Better Control in Robotics
Addressing Challenges in Reinforcement Learning
Reinforcement learning has shown success in dynamic controller generation, but limitations due to suboptimality and finite sampling exist.
Practical Solutions and Value
MIT CSAIL researchers developed a method for tight value function approximations using convex optimization, enhancing controller quality for robotic systems.
Advantages Over Existing Approaches
This approach focuses on local approximations, improving the quality of the approximation, and expanding the stabilizing regions for controllers.
Emphasizing Optimality and Stability
The SOS-based controllers not only stabilize the system but also emphasize optimality, enabling stabilization over larger state space regions.
Application in Robotics
The research showcases tight under and over-estimates of the value function, validating the framework on continuous robotic systems and hybrid planar-pusher systems.
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