Balancing oneself using the spine is a physiological alignment of the body posture in the most efficient manner by the muscular forces for mammals. For this reason, we can see many disabled quadruped animals can still stand or walk even with three limbs. This paper investigates the optimization of dynamic balance during trot gait based on the spatial relationship between the center of mass (CoM) and support area influenced by spinal flexion. During trotting, the robot balance is significantly influenced by the distance of the CoM to the support area formed by diagonal footholds. In this context, lateral spinal flexion, which is able to modify the position of footholds, holds promise for optimizing balance during trotting. This paper explores this phenomenon using a rat robot equipped with a soft actuated spine. Based on the lateral flexion of the spine, we establish a kinematic model to quantify the impact of spinal flexion on robot balance during trot gait. Subsequently, we develop an optimized controller for spinal flexion, designed to enhance balance without altering the leg locomotion. The effectiveness of our proposed controller is evaluated through extensive simulations and physical experiments conducted on a rat robot. Compared to both a non-spine based trot gait controller and a trot gait controller with lateral spinal flexion, our proposed optimized controller effectively improves the dynamic balance of the robot and retains the desired locomotion during trotting.

To achieve more accurate perception performance, LiDAR and camera are gradually chosen to improve 3D object detection simultaneously. However, it is still a non-trivial task to build an effective fusion mechanism, and this is hindering the development of multi-modal based method. Especially, the mapping relationship construction between two modalities is far from fully explored. Canonical cross-modal mapping suffers from failure when the calibration matrix is incorrect, and it also greatly wastes the amount and density of RGB image information. This paper aims to extend the traditional one-to-one alignment relationship between LiDAR and camera. For all projected point clouds, we enhance their cross-modal mapping relationship through aggregating color-texture related feature and shape-contour related feature. Further, a mapping pyramid is proposed to leverage the semantic representation of the image feature at different stages. Based on the above mapping enhancement strategies, our method increases the engagement rate of image. Finally, we design a fusion module based on an attention mechanism to improve the point cloud feature with the auxiliary image feature. Extensive experiments on the KITTI dataset and SUN-RGBD dataset show that our model achieves satisfactory 3D object detection, especially for categories with sparse point clouds compared with other multi-modal fusion networks.

Meta-reinforcement learning (meta-RL) is a promising approach that enables the agent to learn new tasks quickly. However, most meta-RL algorithms show poor generalization in multi-task scenarios due to the insufficient task information provided only by rewards. Language-conditioned meta-RL improves the generalization capability by matching language instructions with the agent's behaviors. While both behaviors and language instructions have symmetry, which can speed up human learning of new knowledge. Thus, combining symmetry and language instructions into meta-RL can help improve the algorithm's generalization and learning efficiency. We propose a dual-MDP meta-reinforcement learning method that enables learning new tasks efficiently with symmetrical behav-iors and language instructions. We evaluate our method in mul-tiple challenging manipulation tasks, and experimental results show that our method can greatly improve the generalization and learning efficiency of meta-reinforcement learning. Videos are available at https://tumi6robot.wixsite.com/symmetry/.

Lane keeping, as a fundamental functionality of autonomous navigation, remains a challenging task for autonomous robots and vehicles. Recently, spiking neural networks (SNNs) have gained attention and research interest due to their biological plausibility and application potential on neuromorphic processors. SNNs have also been successfully deployed on robots to solve autonomous navigation problems. However, lane keeping with a LiDAR sensor is still an open problem for SNNs. In this work, we propose an end-to-end approach based on an SNN to solve the lane-keeping problem using a 3D LiDAR sensor. For the first time, we explore the capability of the proposed SNN controller to perceive the LiDAR input and exploit the features to perform reward-based feedback learning. To ensure the effectiveness of the controller, the proposed method is deployed and evaluated on two high-fidelity simulators. The experimental results demonstrate the high applicability and performance in different scenarios. Furthermore, experiments show that the SNN is capable of performing lane keeping in a simulated urban environment with only 18 control neurons and 32 synapse connections, producing on average only a 17cm deviation from lane center, which is 4.3 % of the lane width.

LiDAR (Light Detection And Ranging) sensors have been widely used in autonomous vehicles as the main sensors. According to the specification details of the widely used 3D LiDAR products in the market, the distribution of vertical beam channels is set according to a uniform angular resolution, which is not ideally efficient for specific autonomous tasks. In this paper, we propose a novel approach to find the optimized angular distribution of the vertical beam channels for different application scenarios and installation configurations. The experimental results in a study case suggest that concerning the vehicle detection task, the optimized LiDARs perform almost two times better than the ones with the same number of channels in terms of the detection range, and have perception performances close to the LiDARs with double channels in the long distance.

Simultaneous localization and mapping (SLAM) is one of the essential techniques and functionalities used by robots to perform autonomous navigation tasks. Inspired by the rodent hippocampus, this paper presents a biologically inspired SLAM system based on a LiDAR sensor using a hippocampal model to build a cognitive map and estimate the robot pose in indoor environments. Based on the biologically inspired models mimicking boundary cells, place cells, and head direction cells, the SLAM system using LiDAR point cloud data is capable of leveraging the self-motion cues from the LiDAR odometry and the boundary cues from the LiDAR boundary cells to build a cognitive map and estimate the robot pose. Experiment results show that with the LiDAR boundary cells the proposed SLAM system greatly outperforms the camera-based brain-inspired method in both simulation and indoor environments, and is competitive with the conventional LiDAR-based SLAM methods.

Localization in the environment is an essential navigational capability for animals and indoor robotic vehicles. In indoor environments, it is still challenging to perfectly solve the global localization problem using probabilistic methods. However, animals are able to instinctively localize themselves with much less effort. Therefore, an intriguing and promising approach is to seek biological inspiration from animals. In this paper, we present a biologically-inspired global localization system using a LiDAR sensor that utilizes a hippocampal model and a landmark-based relocalization approach. The experiment results show that the proposed method is competitive with Monte Carlo Localization, and the results demonstrate the high accuracy, applicability, and reliability of the proposed biologically-inspired localization system in various localization scenarios.

As a vital cognitive function of animals, the navigation skill is first built on the accurate perception of the directional heading in the environment. Head direction cells (HDCs), found in the limbic system of animals, are proven to play an important role in identifying the directional heading allocentrically in the horizontal plane, independent of the animal's location and the ambient conditions of the environment. However, practical HDC models that can be implemented in robotic applications are rarely investigated, especially those that are biologically plausible and yet applicable to the real world. In this article, we propose a computational HDC network that is consistent with several neurophysiological findings concerning biological HDCs and then implement it in robotic navigation tasks. The HDC network keeps a representation of the directional heading only relying on the angular velocity as an input. We examine the proposed HDC model in extensive simulations and real-world experiments and demonstrate its excellent performance in terms of accuracy and real-time capability.