SoftBank Robotics (France)

Artificial muscles play a crucial role in musculoskeletal robotics and prosthetics to approximate the force-generating functionality of biological muscle. However, current artificial muscle systems are typically limited to either contraction or extension, not both. This limitation hinders the development of fully functional artificial musculoskeletal systems. We address this challenge by introducing an artificial antagonistic muscle system capable of both contraction and extension. Our design integrates non-stretchable electrohydraulic soft actuators (HASELs) with electrostatic clutches within an antagonistic musculoskeletal framework. This configuration enables an antagonistic joint to achieve a full range of motion without displacement loss due to tendon slack. We implement a synchronization method to coordinate muscle and clutch units, ensuring smooth motion profiles and speeds. This approach facilitates seamless transitions between antagonistic muscles at operational frequencies of up to 3.2 Hz. While our prototype utilizes electrohydraulic actuators, this muscle-clutch concept is adaptable to other non-stretchable artificial muscles, such as McKibben actuators, expanding their capability for extension and full range of motion in antagonistic setups. Our design represents a significant advancement in the development of fundamental components for more functional and efficient artificial musculoskeletal systems, bringing their capabilities closer to those of their biological counterparts.

Abstract The emerging field of biohybrid robotics aims to create the next generation of soft and sustainable robots by using engineered biological muscle tissues integrated with soft materials as artificial muscles (bio-actuators). Both cardiac and skeletal muscle cells can be used for biohybrid actuation. Generally, cardiac bio-actuators take the shape of thin cellular films, while locomotive skeletal muscle bio-actuators form bulk tissues. The geometry of a bio-actuator should be optimized for the type of desired motion,e.g., thin film layers are optimal for swimming actuators mimicking fish. Until now, the geometry of skeletal muscle bio-actuators has been constrained to ring- or block-like tissues generally differentiated around a pair of pillars due to the need to oppose the contraction force exerted during the skeletal muscle differentiation process. In this work, we extend the possible geometry of skeletal muscle bio-actuators by demonstrating a bilayered design that mimics the motion of jellyfish. We take advantage of a volumetric printing method,i.e., xolography, which allows us to micropattern poly(ethylene glycol) diacrylate and gelatin methacrylate hydrogels to serve as scaffolds for seeding a layer of the skeletal muscle cell matrix. We demonstrate the locomotion speed of our bio-actuators is 3.4x faster than previously reported counterparts. In addition, our skeletal bio-actuators outperform most cardiac ones. Further optimization of our bilayer biofabrication for improved reproducibility of the maturation process of the skeletal muscle tissue will pave the way for the next generation of performant skeletal muscle-based actuators for biohybrid robots.

Constructs intended to capture social attitudes and behaviour towards social robots are incredibly varied, with little overlap or consistency in how they may be related. In this study we conduct exploratory analyses between participants’ self-reported attitudes and behaviour towards a social robot. We designed an autonomous interaction where 102 participants interacted with a social robot (Pepper) in a hypothetical travel planning scenario, during which the robot displayed various multi-modal social behaviours. Several behavioural measures were embedded throughout the interaction, followed by a self-report questionnaire targeting participant’s social attitudes towards the robot (social trust, liking, rapport, competency trust, technology acceptance, mind perception, social presence, and social information processing). Several relationships were identified between participant’s behaviour and self-reported attitudes towards the robot. Implications for how to conceptualise and measure interactions with social robots are discussed.

Today it seems even more evident that social robots will have a more integral role to play in the real-world scenarios and need to participate in the full richness of human society. Central to the success of robots being socially intelligent agents is insuring effective interactions between humans and robots. In order to achieve that goal, researchers and engineers from both industry and academia need to come together to share ideas, trials, failures, and successes. This workshops aims at creating the bridge between industry and academia and as such creating a community to tackle the current and future challenges of socially intelligent human-robot interaction in real-world scenarios by finding solutions for them.

Abstract This paper gives an overview of autonomous underwater vehicles’ (AUVs’) applications, shapes due to the specific kinetic and dynamic requirements, state estimation, control, navigation, and communication principles. The emphasis is put on AUVs deployed within EU Horizon 2020 COMPASS 2020 (Coordination of Maritime assets for Persistent and Systematic Surveillance 2020) project for marine surveillance and reconnaissance missions in European seas. Basic data facts on the AUVs delivered by ECA Group are given, along with some general directions for further research in the field with the aim of achieving more efficient and environmental-friendly underwater missions in the future.KeywordsAUVROVCOMPASSSWARMBio-mimetic AUVsSo-FiInertial navigationAcoustic navigationGeophysical navigationSonarSynthetic aperture sonarSecurityMilitarySurveillanceZephyrAR5AR3OPVMCMOSSASFLSINSDVLCTDISSfDOM

For robots to interact with humans in close proximity safely and efficiently, a specialized method to compute whole-body robot posture and plan contact locations is required. In our work, a humanoid robot is used as a caregiver that is performing a physical assistance task. We propose a method for formulating and initializing a non-linear optimization posture generation problem from an intuitive description of the assistance task and the result of a human point cloud processing. The proposed method allows to plan wholebody posture and contact locations on a task-specific surface of a human body, under robot equilibrium, friction cone, torque/joint limits, collision avoidance, and assistance task inherent constraints. The proposed framework can uniformly handle any arbitrary surface generated from point clouds, for autonomously planing the contact locations and interaction forces on potentially moving, movable, and deformable surfaces, which occur in direct physical human-robot interaction. We conclude the letter with examples of posture generation for physical human-robot interaction scenarios.

Most of the existing soft locomotive robots are capable of moving on horizontal planes and small-angled slopes, but few of them can accomplish the large-angled slope climbing or wall climbing. We introduce an inchworm inspired soft crawling-climbing robot capable of continuous motion from a horizontal plane to a slope of up to 75 degrees and it can perform multiple locomotion including crawling, climbing, and the transition between them. This soft crawling-climbing robot is powered by three pneumatic actuators connected in series that deforms with two degrees of freedom, negative pressure sucker feet that generate periodical adhesion, and a semi-automatic controlling system synchronizing the body shape and anchoring of the feet. The robot mimics the body deformation and feet anchoring pattern exhibited by inchworms and can be applied in inspection, surveillance, and rescuing.

This paper introduces a new raw-audio, environmental, kitchen-related, non-vocal dataset to fill what we consider as a gap in the context of audio datasets. Our so-called Kitchen20 dataset is compared to other datasets such as ESC-50 and shown that both datasets can be merged together in what we call ESC-70. A human quantitative appreciation of the audio samples contained in Kitchen20 is provided as well as several machine learning benchmarks on both Kitchen20 and ESC-70.