Current numerical methods for solving kinematic identification (KI) and inverse kinematics (IK) are limited in accuracy, convergence rates, and robustness, necessitating further enhancement. This paper presents a modified Halley method for solving the KI and IK problems of serial robots based on the product of exponentials formula, achieving quintic convergence. Specifically, a general error model is first established based on exponential coordinates, and KI and IK are reformulated as root-finding problems. Next, the modified Halley method, which we prove to be a fifth-order method and incorporates a damping strategy, is proposed to resolve the singularity issue and enhance robustness. Subsequently, the Jacobian and Hessian matrices required for the proposed method are analytically derived based on the time differential of exponentials. Furthermore, highly simplified explicit formulas for these matrices are presented for the IK problem. Simulations on serial robots with various configurations validate the proposed method's accuracy, convergence rates, and robustness in solving KI and IK problems, as well as its advantages over the state-of-the-art. Additionally, experimental validation of KI on two physical robots further demonstrates the effectiveness of the proposed method. Our custom-written MATLAB and C++ codebases are made publicly available for download.

Quantum capacity exhibits intriguing phenomena such as super-activation, amplification, and super-amplification, which can be explored using constructions based on private channels. In this work, we refine the connection between private channels and additivity questions in quantum channel capacity. Specifically, the shield system, which safeguards the key system's privacy by leaking quantum information, can recover some of this lost information when paired with an auxiliary channel like the erasure channel$\mathcal{E}_{p}$, thereby enhancing overall capacity. Building on this insight, we construct new and simple examples of super-amplification for the erasure channel$\mathcal{E}_{p}$with any erasure probability$p$. In addition, we provide a refined version of the Smith-Yard argument that also applies for arbitrary$p$. Finally, we revisit the fundamental question of whether regularization in channel capacity formulas requires infinitely many channel uses, showing that the necessary number may instead depend on the channel's dimension.

Vehicle-to-infrastructure cooperative 3D object detection (VIC3D) is a task that leverages both vehicle and roadside sensors to jointly perceive the surrounding environment. However, considering the high speed of vehicles, the real-time requirements, and the limitations of communication bandwidth, roadside devices transmit the results of perception rather than raw sensor data or feature maps in our real-world scenarios. And affected by various environmental factors, the transmission delay is dynamic. To meet the needs of practical applications, we present OTVIC, which is the first multi-modality and multi-view dataset with online transmission from real scenes for vehicle-to-infrastructure cooperative 3D object detection. The ego-vehicle receives the results of infrastructure perception in real-time, collected from a section of highway in Chengdu, China. Moreover, we propose LfFormer, which is a novel end-to-end multi-modality late fusion framework with transformer for VIC3D task as a baseline based on OTVIC. Experiments prove our fusion framework’s effectiveness and robustness. Our project is available at https://sites.google.com/view/otvic.

Safety is of great importance in multi-robot navigation problems. In this paper, we propose a control barrier function (CBF) based optimizer that ensures robot safety with both high probability and flexibility, using only sensor measurement. The optimizer takes action commands from the policy network as initial values and provides refinement to drive the potentially dangerous ones back into safe regions. With the help of a deep world model that predicts the evolution of surrounding dynamics and the consequences of different actions, the CBF module can guide the optimization within a reasonable time horizon. We also present a novel joint training framework that improves the cooperation between the Reinforcement Learning (RL) based policy and the CBF-based optimizer by utilizing reward feedback from the CBF module. We observe that our policy can achieve a higher success rate while maintaining the safety of multiple robots in significantly fewer episodes. Experiments are conducted in multiple scenarios both in simulation and the real world, the results demonstrate the effectiveness of our method in maintaining the safety of multiple robots. Code is available at https://github.com/YuxiangCui/MARL-OCBF.

One of the challenges in vision-based driving trajectory generation is dealing with out-of-distribution scenarios. In this paper, we propose a domain generalization method for vision-based driving trajectory generation for autonomous vehicles in urban environments, which can be seen as a solution to extend the Invariant Risk Minimization (IRM) method in complex problems. We leverage an adversarial learning approach to train a trajectory generator as the decoder. Based on the pre-trained decoder, we infer the latent variables corresponding to the trajectories, and pre-train the encoder by regressing the inferred latent variable. Finally, we fix the decoder but fine-tune the encoder with the final trajectory loss. We compare our proposed method with the state-of-the-art trajectory generation method and some recent domain generalization methods on both datasets and simulation, demonstrating that our method has better generalization ability. Our project is available at https://sites.google.com/view/dg-traj-gen.

Recent studies on deep-learning-based small defection segmentation approaches are trained in specific settings and tend to be limited by fixed context. Throughout the training, the network inevitably learns the representation of the background of the training data before figuring out the defection. They underperform in the inference stage once the context changed and can only be solved by training in every new settings. This eventually leads to the limitation in practical robotic applications where contexts keep varying. To cope with this, instead of training a network context by context and hoping it to generalize, why not stop misleading it with any limited context and start training it with pure simulation? In this paper, we propose the network SSDS that learns a way of distinguishing small defections between two images regardless of the context, so that the network can be trained once for all. A small defection detection layer utilizing the pose sensitivity of phase correlation between images is introduced and is followed by an outlier masking layer. The network is trained on randomly generated simulated data with simple shapes and is generalized across the real world. Finally, SSDS is validated on real-world collected data and demonstrates the ability that even when trained in cheap simulation, SSDS can still find small defections in the real world showing the effectiveness and its potential for practical applications. Code is available here