update chair description

Author: erachelson

reload.md

@@ -15,7 +15,7 @@ The chair investigates topics in reinforcement learning, including (but not limi
 
 The endeavor to develop frugal (both in terms of computation and data) algorithms for life-long RL stems from a number of inspiration problems, such as:
 - onboard policy adjustment (in satellites, robots, and more generally devices with limited hardware),
-- robust control of mobile robots or more abstract systems, 
+- robustness to dynamics uncertainty or dynamics change in mobile robots or more abstract systems, 
 - mixing data from multi-fidelity simulations for fluid control, 
 - rapid response to various operation research problems, 
 - learning dynamic treatment regimes for patients with little data,