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Context. Solar Orbiter, the new-generation mission dedicated to solar and heliospheric exploration, was successfully launched on February 10, 2020, 04:03 UTC from Cape Canaveral. During its first perihelion passage in June 2020, two successive interplanetary coronal mass ejections (ICMEs), propagating along the heliospheric current sheet (HCS), impacted the spacecraft. Aims. This paper addresses the investigation of the ICMEs encountered by Solar Orbiter on June 7−8, 2020, from both an observational and a modeling perspective. The aim is to provide a full description of those events, their mutual interaction, and their coupling with the ambient solar wind and the HCS. Methods. Data acquired by the MAG magnetometer, the Energetic Particle Detector suite, and the Radio and Plasma Waves instrument are used to provide information on the ICMEs’ magnetic topology configuration, their magnetic connectivity to the Sun, and insights into the heliospheric plasma environment where they travel, respectively. On the modeling side, the Heliospheric Upwind eXtrapolation model, the 3D COronal Rope Ejection technique, and the EUropean Heliospheric FORecasting Information Asset (EUHFORIA) tool are used to complement Solar Orbiter observations of the ambient solar wind and ICMEs, and to simulate the evolution and interaction of the ejecta in the inner heliosphere, respectively. Results. Both data analysis and numerical simulations indicate that the passage of two distinct, dynamically and magnetically interacting (via magnetic reconnection processes) ICMEs at Solar Orbiter is a possible scenario, supported by the numerous similarities between EUHFORIA time series at Solar Orbiter and Solar Orbiter data. Conclusions. The combination of in situ measurements and numerical simulations (together with remote sensing observations of the corona and inner heliosphere) will significantly lead to a deeper understanding of the physical processes occurring during the CME-CME interaction. 

Robotic grasping is successful when a robot can sense and grasp an object without letting it slip. Beyond industrial robotic tasks, there are two main robotic grasping methods. The first is planning-based grasping where the object geometry is known beforehand and stable grasps are calculated using algorithms [1]. The second uses tactile feedback. Currently, there are capacitive sensors placed beneath stiff pads on the front of robotic fingers [2]. With post-execution grasp adjustment procedures to estimate grasp stability, a support vector machine classifier can distinguish stable and unstable grasps. The accuracy across the classes of tested objects is 81% [1]. We are proposing to improve the classifier's accuracy by wrapping flexible sensors around the robotic finger to gain information from the edges and sides of the finger.