During the last two months the consortium has been performing further real world experiments with the first application of learning methods. Further work on our simulations and evaluations of those has been performed.

1) A new sampling strategy was developed and implemented. We used the new method to do an analysis for the GS20 gripper and the Dolt object. The whole set of tried grasp can be seen here, the successful ones after simulation here and the ones produced by the sampling process here (red = failed grasp, green = successful grasp, blue=unstable grasp).

2) We started to evaluate different aspects concerned with transferability between different simulation setups and real world setups.

3) A method for updating grasp priorities has been developed. For the Dolt object we performed a first set of experiments to compare the performance of the original grasp set and the set with updated priorities. We found a statistically significant success rate improvement.

As the baseline grasp strategy for comparison, we have used grasped chosen manually by an experienced. We used 1029 grasp experiments as the basis for learning new priorities. We then tested and compared the learned priorities with those manually chosen. We found the success likelihood to be 52.1% +/- 4.6% with the manually chosen strategy (blue line) and 63.5%+/- 4.4% with the learned priorities (purple line). The uncertainties are chosen using the 95% confidence intervals. Thus, we obtain a clearly statistically significant improvement through the learning procedure, and it should be stressed again that the comparison was made with a state-of-the-art manual method.

During the last two months the consortium has been performing first real world experiments and further work on our simulation and learning methods has been performed.

1) After further preparations for the real world experiments in the SCAPE 2-finger setup were accomplished we performed a larger batch of real world experiment, with grasps generated from simulation. The video here visualizes the distribution of success (green) and failures (red) in this case.

2) Simulations of the Cranfield plate on a table using the SDH 3 finger hand have been modified and several million simulations have been executed. This video shows the success space. See here for simulation in progress.

3) To guarantees global consistency and avoids artifacts (steaming from the artificial segmenting of the data into cubicles) in our learning methods and condensation techniques we implemented a global probability estimate.