In the field of signal processing for robotics, the inverse kinematics of robot arms presents a significant challenge due to multiple solutions caused by redundant degrees of freedom (DOFs). Precision is also a crucial performance indicator for robot arms. Current methods typically rely on conditional deep generative models (CDGMs), which often fall short in precision. In this paper, we propose Conditional Embodied Self-Supervised Learning (CEMSSL) and introduce a unified framework based on CEMSSL for high-precision multi-solution inverse kinematics learning. This framework enhances the precision of existing CDGMs by up to 2-3 orders of magnitude while maintaining their original properties. Furthermore, our method is extendable to other fields of signal processing where obtaining multi-solution data in advance is challenging, as well as to other problems involving multi-solution inverse processes.

Tennis is widely popular across various age groups. However, mastering fluid stroke mechanics remains a significant challenge, requiring substantial time and practice. The lack of scientifically grounded tools for skill acquisition and training tools further hampers the development of tennis proficiency. In this paper, we investigate the application of an intelligent tennis robot equipped with advanced human-machine interaction capabilities, designed to serve as an effective tool for tennis learners, especially for enhancing motion geometry and coordination. First, we detail the design and development of the intelligent tennis robot, highlighting its core functions and operational principles, including ball serving, human motion sensing, and motion feedback mechanisms. Subsequently, we introduce a novel human motion analysis method that integrates motion geometry and kinematic analyses. Following this, we present a real-time motion feedback system that identifies deficiencies in players' movements, thereby facilitating the enhancement of their motion memory. Finally, we conduct experiments over players of varying skill levels, analyzing their motion patterns and providing practical examples. The proposed human-machine interaction framework offers a pioneering solution for intelligent tennis training, enabling players to understand their movements and correct errors immediately after each stroke.

Modern robots must coexist with humans in dense urban environments. A key challenge is the ghost probe problem, where pedestrians or objects unexpectedly rush into traffic paths. This issue affects both autonomous vehicles and human drivers. Existing works propose vehicle-to-everything (V2X) strategies and non-line-of-sight (NLOS) imaging for ghost probe zone detection. However, most require high computational power or specialized hardware, limiting real-world feasibility. Additionally, many methods do not explicitly address this issue. To tackle this, we propose DPGP, a hybrid 2D-3D fusion framework for ghost probe zone prediction using only a monocular camera during training and inference. With unsupervised depth prediction, we observe ghost probe zones align with depth discontinuities, but different depth representations offer varying robustness. To exploit this, we fuse multiple feature embeddings to improve prediction. To validate our approach, we created a 12K-image dataset annotated with ghost probe zones, carefully sourced and cross-checked for accuracy. Experimental results show our framework outperforms existing methods while remaining cost-effective. To our knowledge, this is the first work extending ghost probe zone prediction beyond vehicles, addressing diverse non-vehicle objects. We will open-source our code and dataset for community benefit.

Precision is a crucial performance indicator for robot arms. During interacting with human, high precision enables a robot arm to be used effectively and safely, while low precision may lead to safety issues. Traditional methods for improving robot arm precision rely on error compensation. However, these methods are often not robust and lack adaptability. Learning-based methods offer greater flexibility and adaptability, while current researches show that they often fall short in achieving high precision and struggle to handle many scenarios requiring high precision. In this paper, we propose a novel high-precision robot arm manipulation framework based on online iterative learning and forward simulation, which can achieve positioning error (precision) less than end-effector physical minimum displacement. In other words, our proposed method can compensate for the precision-limitation of the hardware structure of the robot arms. Furthermore, we consider the joint angular resolution of the real robot arm, which is usually neglected in related works. A series of experiments on both simulation and real UR3 robot arm platforms demonstrate that our proposed method is effective and promising. The related code will be available soon.

Trajectory prediction is a fundamental technology for advanced autonomous driving systems and represents one of the most challenging problems in the field of cognitive intelligence. Accurately predicting the future trajectories of each traffic participant is a prerequisite for building high safety and high reliability decision-making, planning, and control capabilities in autonomous driving. However, existing methods often focus solely on the motion of other traffic participants without considering the underlying intent behind that motion, which increases the uncertainty in trajectory prediction. Autonomous vehicles operate in real-time environments, meaning that trajectory prediction algorithms must be able to process data and generate predictions in real-time. While many existing methods achieve high accuracy, they often struggle to effectively handle heterogeneous traffic scenarios. In this paper, we propose a Subjective Intent-based Low-latency framework for Multiple traffic participants joint trajectory prediction. Our method explicitly incorporates the subjective intent of traffic participants based on their key points, and predicts the future trajectories jointly without map, which ensures promising performance while significantly reducing the prediction latency. Additionally, we introduce a novel dataset designed specifically for trajectory prediction. Related code and dataset will be available soon.

In indoor parking lots, the use of RTK/GNSS for vehicle localization is often impractical due to the significantly smaller space compared to outdoor roads, which demands higher precision in both mapping and localization. Although feature point based visual SLAM algorithms have achieved high localization accuracy, they impose significant storage demands on embedded systems, and the visual feature point maps are not time-stable and are sensitive to lighting conditions. In this paper, we propose a real-time mapping and localization system for multi-floor parking lot. For the mapping part, we introduce a map-free SLAM method for precise ego-pose estimation, along with an efficient incremental map update framework that supports loop closure and multi-session mapping tasks. In the localization part, a semantic map is reused for vehicle localization based on bidirectional incentive descriptors. We incorporate degenerate cases into our optimization process, which greatly enhances the localization results. To the best of our knowledge, this is the first comprehensive system proposed for multi-floor parking lots. Experimental results demonstrate that our approach achieves state-of-the-art mapping and localization accuracy in multi-floor environments on embedded platforms.