# Goal-Conditioned Reinforcement Learning within a Human-Robot Disassembly Environment
## 4 Experiments and Results
### Experiments on the Real System
#### 4.4.2 Policy Transferability  
The performance of the learned policy was evaluated with different robots (Figure 7). In theory, policies learned in task space can transfer with a higher success rate, abstracting from the kinematics and dynamics of each specific robot model [54]. For this purpose, a zero-shot transfer was performed between the KUKA LBR Iiwa robot, which was used for learning, to the Franka Emika Panda robot. The evaluations were performed with three pairs of disassembly parts, of \(10^{-3}\), \(5\times 10^{-4}\) and \(2\times 10^{-4}\) m tolerance, which affected the friction during the extraction. Specifically, 40 extractions were conducted at every 5 deg rotation, between \(-10^{\circ}\) and \(10^{\circ}\) (see Figure 6), of the base and peg. Half of the extractions were carried out with the worker's hand statically positioned near the part and the other half executing a motion similar to that performed on the disassembly plant next to the robot. Static hand evaluations were performed by positioning the hand to the right or left side of the workpiece. All the experiments, in reality, were performed with the neural weights of PPO as it was the agent with the best simulation results. Table 3 gathers the results of the evaluations done with each robot.  
Figure 7: Real-world experiments with two collaborative robots: KUKA LBR Iiwa (**top**) and Franka Emika Panda (**bottom**).  
The results showed how the policies learned in the task space can be transferred directly between robots. However, depending on the dimensions of the workspace, the kinematic constraints of each of the robots should be considered.  
The KUKA LBR Iiwa robot achieved a disassembly success rate of 95% while avoiding obstacles, 100% with static obstacles, and 90% with dynamic obstacles, when the tolerance between parts was \(10^{-3}\) m. The Franka Emika Panda, in turn, succeeded 93% of the time in disassembly, 94% with static obstacles, and 92% with moving obstacles. In no case was there a collision with the co-worker. The high-efficiency rates of both robots were due to the linear movements executed by both manipulators in reality. It is also worth noting that in evaluating the disassembly with static obstacles in simulation, all failed episodes were caused by the violation of the rotation threshold set as part of the reward. This terminal reward was established since the peg penetrated slightly into the base owing to the simulator's physics, which in reality does not occur.  
As the tolerance between parts was reduced, both robots' disassembly success rates dropped significantly. However, the results hardly changed between \(5\times 10^{-4}\) and \(2\times 10^{-4}\) tolerances. The KUKA LBR Iiwa achieved success rates of 84.5% and 85% for \(5\times 10^{-4}\) and \(2\times 10^{-4}\) tolerances, respectively, while the Franka Emika Panda achieved results of 83% and 82.5%. A plausible explanation for this drop in performance may be related to the fact that the range of actual friction between the two parts due to their tolerances was not completely randomized during simulation learning. Nevertheless, in none of these cases was a collision with the co-worker. Both simulation and experimental results can be found at [[https://www.youtube.com/watch?v=Sb5](https://www.youtube.com/watch?v=Sb5) sv4 PWzhA]([https://www.youtube.com/watch?v=Sb5](https://www.youtube.com/watch?v=Sb5) sv4 PWzhA) (accessed on 29 September 2022).