In-betweening is a technique for generating transitions given start and target character states. The majority of existing works require multiple (often 10) frames as input, which are not always available. In addition, they produce results that lack diversity, which may not fulfill artists' requirements. Addressing these gaps, our work deals with a focused yet challenging problem: generating diverse and high-quality transitions given exactly two frames (only the start and target frames). To cope with this challenging scenario, we propose a bi-directional motion generation and stitching scheme which generates forward and backward transitions from the start and target frames with two adversarial autoregressive networks, respectively, and stitches them midway between the start and target frames. In contrast to stitching at the start or target frames, where the ground truth cannot be altered, there is no strict midway ground truth. Thus, our method can capitalize on this flexibility and generate high-quality and diverse transitions simultaneously. Specifically, we employ conditional variational autoencoders (CVAEs) to implement our autoregressive networks and propose a novel stitching loss to stitch the bi-directional generated motions around the midway point. Extensive experiments demonstrate that our method achieves higher motion quality and more diverse results than existing methods on the LaFAN1, Human3.6m and AMASS datasets.

This paper presents a soft actuator inspired by origami and a tunable strain limiting layer, which is proposed for reconfigurable soft robotic grasping. Main structure of the actuator is based on Miura origami which generates extension under pressurized air while a limiting layer with tunable length enables the actuator with different motion patterns. By driving the limiting layer through a servo motor, the range of motion and trajectory of the actuator can be pre-programed and the gripper’s grasping range will be affected accordingly. This paper discusses the design, fabrication, analysis and experimental verification of the actuator. Then grasping performance of the gripper under objects of different shapes, sizes, and weights is experimentally evaluated. The reconfigurable soft gripper can be applied as an end-effector to accomplish adaptive grasping tasks with various targets.

Monitoring the state of contact is essential for robotic devices, especially grippers that implement geckoinspired adhesives where intimate contact is crucial for a firm attachment. However, due to the lack of deformable sensors, few have demonstrated tactile sensing for gecko grippers. We present Viko, an adaptive gecko gripper that utilizes vision-based tactile sensors to monitor contact state. The sensor provides high-resolution real-time measurements of contact area and shear force. Moreover, the sensor is adaptive, low-cost, and compact. We integrated gecko-inspired adhesives into the sensor surface without impeding its adaptiveness and performance. Using a robotic arm, we evaluate the performance of the gripper by a series of grasping test. The gripper has a maximum payload of 8N even at a low fingertip pitch angle of 30°. We also showcase the gripper’s ability to adjust fingertip pose for better contact using sensor feedback. Further, everyday object picking is presented as a demonstration of the gripper’s adaptiveness.

Tactile sensing on human feet is crucial for motion control, however, has not been explored in robotic counterparts. This work is dedicated to endowing tactile sensing to legged robot’s feet and showing that a single-legged robot can be stabilized with only tactile sensing signals from its foot. We propose a robot leg with a novel vision-based tactile sensing foot system and implement a processing algorithm to extract contact information for feedback control in stabilizing tasks. A pipeline to convert images of the foot skin into high-level contact information using a deep learning framework is presented. The leg was quantitatively evaluated in a stabilization task on a tilting surface to show that the tactile foot was able to estimate both the surface tilting angle and the foot poses. Feasibility and effectiveness of the tactile system were investigated qualitatively in comparison with conventional single-legged robotic systems using inertia measurement units (IMU). Experiments demon-strate the capability of vision-based tactile sensors in assisting legged robots to maintain stability on unknown terrains and the potential for regulating more complex motions for humanoid robots.

Self-supervised learning for depth estimation possesses several advantages over supervised learning. The benefits of no need for ground-truth depth, online fine-tuning, and better generalization with unlimited data attract researchers to seek self-supervised solutions. In this work, we propose a new self-supervised framework for stereo matching utilizing multiple images captured at aligned camera positions. A cross photometric loss, an uncertainty-aware mutual-supervision loss, and a new smoothness loss are introduced to optimize the network in learning disparity maps end-to-end without ground-truth depth information. To train this framework, we build a new multiscopic dataset consisting of synthetic images rendered by 3D engines and real images captured by real cameras. After being trained with only the synthetic images, our network can perform well in unseen outdoor scenes. Our experiment shows that our model obtains better disparity maps than previous unsupervised methods on the KITTI dataset and is comparable to supervised methods when generalized to unseen data. Our source code and dataset are available at https://sites.google.com/view/multiscopic.

This article describes an innovative design of variable stiffness soft actuator, which can potentially be utilized for manipulation and locomotion of soft robots. The new actuator is a combination of two types of actuations: soft pneumatic actuation and muscle-like supercoiled polymer (SCP) actuation. Soft pneumatic actuator has two roles: first is to generate bending motions and second is to increase the stiffness of the whole actuator together with SCP artificial muscles. SCP artificial muscles are exploited to generate pre-load to resist the whole actuator from (excessive) deformation when external load is applied. These two types of actuations are arranged antagonistically to realize stiffness tuning of the whole actuator. At a given bending position, stiffness of the actuator could be tuned by controlling the pressure inside the air chamber and the tension on the SCP artificial muscles. In experimental section, tests are conducted to characterize the applied SCP artificial muscles before they are applied to the proposed actuator. Afterwards, tests of proposed actuator are performed to examine its variable stiffness capability. From experimental results, the proposed actuator can achieve 3.47 times stiffness variation ratio from 0.0312 N/mm(40kPa air pressure and no SCP actuation) to 0.1083 N/mm(82kPa air pressure and SCP actuation at 0.143 W/cm) at the same position (bending angle of 56 degree). This study exhibits the potential of applying SCP artificial muscles to promote the performance of soft robots.