tilmannb commited on
Commit
353cb06
·
verified ·
1 Parent(s): d288030

fix typo in line 55

Browse files
app/src/content/chapters/02_classic_robotics.mdx CHANGED
@@ -52,7 +52,7 @@ Effects such as (1) are typically achieved *through* the robot, i.e. generating
52
  Motions like (2) may result in changes in the robot's physical location within its environment.
53
  Generally, modifications to a robot's location within its environment may be considered instances of the general *locomotion* problem, further specified as *wheeled* or *legged* locomotion based on whenever a robot makes use of wheels or leg(s) to move in the environment.
54
  Lastly, an increased level of dynamism in the robot-environment interactions can be obtained combining (1) and (2), thus designing systems capable to interact with *and* move within their environment.
55
- This category is problems is typically termed *mobile manipulation*, and is characterized by a typically much larger set of control variables compared to either locomotion or manipulation alone.
56
 
57
  The traditional body of work developed since the very inception of robotics is increasingly complemented by learning-based approaches.
58
  ML has indeed proven particularly transformative across the entire robotics stack, first empowering planning-based techniques with improved state estimation used for traditional planning [@tangPerceptionNavigationAutonomous2023] and then end-to-end replacing controllers, effectively yielding perception-to-action methods [@koberReinforcementLearningRobotics].
 
52
  Motions like (2) may result in changes in the robot's physical location within its environment.
53
  Generally, modifications to a robot's location within its environment may be considered instances of the general *locomotion* problem, further specified as *wheeled* or *legged* locomotion based on whenever a robot makes use of wheels or leg(s) to move in the environment.
54
  Lastly, an increased level of dynamism in the robot-environment interactions can be obtained combining (1) and (2), thus designing systems capable to interact with *and* move within their environment.
55
+ This category of problems is typically termed *mobile manipulation*, and is characterized by a typically much larger set of control variables compared to either locomotion or manipulation alone.
56
 
57
  The traditional body of work developed since the very inception of robotics is increasingly complemented by learning-based approaches.
58
  ML has indeed proven particularly transformative across the entire robotics stack, first empowering planning-based techniques with improved state estimation used for traditional planning [@tangPerceptionNavigationAutonomous2023] and then end-to-end replacing controllers, effectively yielding perception-to-action methods [@koberReinforcementLearningRobotics].