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Found 4 papers, 3 papers with code
Fundamental Limits for Sensor-Based Robot Control
Our goal is to develop theory and algorithms for establishing fundamental limits on performance imposed by a robot's sensors for a given task.
Invariant Policy Optimization: Towards Stronger Generalization in Reinforcement Learning
A fundamental challenge in reinforcement learning is to learn policies that generalize beyond the operating domains experienced during training.
Learning Task-Driven Control Policies via Information Bottlenecks
Standard reinforcement learning algorithms typically produce policies that tightly couple control actions to the entirety of the system's state and rich sensor observations.
Task-Driven Estimation and Control via Information Bottlenecks
We propose novel iterative algorithms for automatically synthesizing (offline) a task-driven representation (given in terms of a set of task-relevant variables (TRVs)) and a performant control policy that is a function of the TRVs.
Optimization and Control Robotics Systems and Control
