He Xu

Xu He, Qiaochu Huang, Zhensong Zhang et al.

Co-speech gestures, if presented in the lively form of videos, can achieve superior visual effects in human-machine interaction. While previous works mostly generate structural human skeletons, resulting in the omission of appearance information, we focus on the direct generation of audio-driven co-speech gesture videos in this work. There are two main challenges: 1) A suitable motion feature is needed to describe complex human movements with crucial appearance information. 2) Gestures and speech exhibit inherent dependencies and should be temporally aligned even of arbitrary length. To solve these problems, we present a novel motion-decoupled framework to generate co-speech gesture videos. Specifically, we first introduce a well-designed nonlinear TPS transformation to obtain latent motion features preserving essential appearance information. Then a transformer-based diffusion model is proposed to learn the temporal correlation between gestures and speech, and performs generation in the latent motion space, followed by an optimal motion selection module to produce long-term coherent and consistent gesture videos. For better visual perception, we further design a refinement network focusing on missing details of certain areas. Extensive experimental results show that our proposed framework significantly outperforms existing approaches in both motion and video-related evaluations. Our code, demos, and more resources are available at https://github.com/thuhcsi/S2G-MDDiffusion.

Haihang Wang, Fares J. Abu-Dakka, Tran Nguyen Le et al.

Soft robotic hands and grippers are increasingly attracting attention as a robotic end-effector. Compared with rigid counterparts, they are safer for human-robot and environment-robot interactions, easier to control, lower cost and weight, and more compliant. Current soft robotic hands have mostly focused on the soft fingers and bending actuators. However, the palm is also essential part for grasping. In this work, we propose a novel design of soft humanoid hand with pneumatic soft fingers and soft palm. The hand is inexpensive to fabricate. The configuration of the soft palm is based on modular design which can be easily applied into actuating all kinds of soft fingers before. The splaying of the fingers, bending of the whole palm, abduction and adduction of the thumb are implemented by the soft palm. Moreover, we present a new design of soft finger, called hybrid bending soft finger (HBSF). It can both bend in the grasping axis and deflect in the side-to-side axis as human-like motion. The functions of the HBSF and soft palm were simulated by SOFA framework. And their performance was tested in experiments. The 6 fingers with 1 to 11 segments were tested and analyzed. The versatility of the soft hand is evaluated and testified by the grasping experiments in real scenario according to Feix taxonomy. And the results present the diversity of grasps and show promise for grasping a variety of objects with different shapes and weights.

Donghao Xu, Zhezhang Ding, Xu He et al.

Driving in a human-like manner is important for an autonomous vehicle to be a smart and predictable traffic participant. To achieve this goal, parameters of the motion planning module should be carefully tuned, which needs great effort and expert knowledge. In this study, a method of learning cost parameters of a motion planner from naturalistic driving data is proposed. The learning is achieved by encouraging the selected trajectory to approximate the human driving trajectory under the same traffic situation. The employed motion planner follows a widely accepted methodology that first samples candidate trajectories in the trajectory space, then select the one with minimal cost as the planned trajectory. Moreover, in addition to traditional factors such as comfort, efficiency and safety, the cost function is proposed to incorporate incentive of behavior decision like a human driver, so that both lane change decision and motion planning are coupled into one framework. Two types of lane incentive cost -- heuristic and learning based -- are proposed and implemented. To verify the validity of the proposed method, a data set is developed by using the naturalistic trajectory data of human drivers collected on the motorways in Beijing, containing samples of lane changes to the left and right lanes, and car followings. Experiments are conducted with respect to both lane change decision and motion planning, and promising results are achieved.