In-hand manipulation tasks using hand-like robotic grippers offer a promising approach to accomplish various tasks in a human-centered environment. Due to the inherent safety of soft robots, in-hand manipulations performed by soft robots provide great opportunities for future human-robot collaboration, which is the scope of this paper. By modeling a new and innovative soft-robotic gripper known as the Anthropomorphic Soft Gripper and synthesis of an open-loop controller with deep reinforcement learning, it is demonstrated how the movement of objects by in-hand manipulations can be accomplished. Moreover, this work explores the application of deep reinforcement learning methods without the employment for domain randomization. As noted by Bhatt et al. the inherent soft properties of soft robotic grippers enable remarkably robust in-hand manipulation in open-loop control, giving the impetus for the approach that is being followed in this work. Motion sequences generated in simulation are successfully transferred to the real anthropomorphic soft gripper and validated in experiments.

Soft continuum robots enable new application areas in contrast to standard rigid robots, such as interaction with a varying environment. Due to their compliant continuous structure, they are inherently safe and adaptive to environmental conditions. In this paper, the interaction with the environment is performed at the tool-center-point of a soft continuum manipulator and is realized by a hybrid force-position control. For this, a force estimation model is derived to substitute the force sensor at the tool-center-point. The force estimation is probabilistic and relies on normal distributions considering model parameters and deviations from model identification of the soft continuum robot. It also provides a qualitative measure for the contact estimation. This paper first presents the probabilistic force estimation model and then shows the hybrid force-position control using the presented model. From the results, it is concluded that force sensing is replaceable for the environment interaction.