Your choice of observations looks good to me. Adding robot end-effector orientation could be helpful as well. I also found that adding past actions to the observation helped learning in a lot of cases.
A choice of the observations often depends on your goals - if you have a real robot and you’d like to perform sim2real your choice of the observations is limited to what is available on a real robot and from it’s surrounding. But even in this case you can use asymmetric PPO version for training and a full set of observations to pass to the value function, see Shadow Hand environment as an example.
If you don’t have a sim2real goal sharing all information provided by a simulator, and sometimes even with hand-crafted features would be a good first step. It depends on the complexity of the task as well - simpler tasks can be usually solved with a very limited set of observations. And having less observations allows using smaller networks and fater training.
Also, the reward is very important, I’d say often it’s more important than observations choice.