- Added JointMaskingActionSpace wrapper in gym_manipulator in order to select which joints will be controlled. For example, we can disable the gripper actions for some tasks.

- Added Nan detection mechanisms in the actor, learner and gym_manipulator for the case where we encounter nans in the loop.
- changed the non-blocking in the `.to(device)` functions to only work for the case of cuda because they were causing nans when running the policy on mps
- Added some joint clipping and limits in the env, robot and policy configs. TODO clean this part and make the limits in one config file only.

Co-authored-by: Adil Zouitine <adilzouitinegm@gmail.com>

lerobot/common/policies/hilserl/classifier/modeling_classifier.py

@@ -145,8 +145,8 @@ class Classifier(
 
         return ClassifierOutput(logits=logits, probabilities=probabilities, hidden_states=encoder_outputs)
 
-    def predict_reward(self, x):
+    def predict_reward(self, x, threshold=0.6):
         if self.config.num_classes == 2:
-            return (self.forward(x).probabilities > 0.6).float()
+            return (self.forward(x).probabilities > threshold).float()
         else:
             return torch.argmax(self.forward(x).probabilities, dim=1)