Adaptive curricula in reinforcement learning (RL) have proven effective for producing policies robust to discrepancies between the train and test environment. Recently, the Unsupervised Environment Design (UED) framework generalized RL curricula to generating sequences of entire environments, leading to new methods with robust minimax regret properties. Problematically, in partially-observable or stochastic settings, optimal policies may depend on the ground-truth distribution over aleatoric parameters of the environment in the intended deployment setting, while curriculum learning necessarily shifts the training distribution. We formalize this phenomenon as curriculum-induced covariate shift (CICS), and describe how its occurrence in aleatoric parameters can lead to suboptimal policies. Directly sampling these parameters from the ground-truth distribution avoids the issue, but thwarts curriculum learning. We propose SAMPLR, a minimax regret UED method that optimizes the ground-truth utility function, even when the underlying training data is biased due to CICS. We prove, and validate on challenging domains, that our approach preserves optimality under the ground-truth distribution, while promoting robustness across the full range of environment settings.

Olis Robotics, a leader in next-generation AI-driven software for remote robotics in dynamic environments in subsea, terrestrial, and space applications, today announced that it has been selected by Maxar Technologies to provide robotic operator planning software for Maxar's Sample Acquisition, Morphology Filtering, and Probing of Lunar Regolith (SAMPLR) robotic arm. The arm will be mounted to a yet-to-be-named lander as one of 12 payloads that NASA selected as part of its Artemis program to send the first woman and the next man to the Moon by 2024 in preparation for a human mission to Mars. Olis Robotics' operator planning software will solve for the extreme latency experienced while operating robotics on the lunar surface by enabling operators to simulate and plan movements from the ground. Olis' software will provide a 3D visualization of the lunar environment and intuitive controls for operators on Earth, providing enhanced control during exploration missions. "The moon provides an excellent proving ground for our robotic operator planning software, allowing operators on Earth to successfully complete more complex missions faster and safer than ever before," explained Olis Robotics CEO Don Pickering.