Quadruped robots face limitations in long-range navigation efficiency due to their reliance on legs. To ameliorate the limitations, we introduce a Reinforcement Learning-based Active Transporter Riding method (RL-ATR), inspired by humans' utilization of personal transporters, including Segways. The RL-ATR features a transporter riding policy and two state estimators. The policy devises adequate maneuvering strategies according to transporter-specific control dynamics, while the estimators resolve sensor ambiguities in non-inertial frames by inferring unobservable robot and transporter states. Comprehensive evaluations in simulation validate proficient command tracking abilities across various transporter-robot models and reduced energy consumption compared to legged locomotion. Moreover, we conduct ablation studies to quantify individual component contributions within the RL-ATR. This riding ability could broaden the locomotion modalities of quadruped robots, potentially expanding the operational range and efficiency.

We propose a hierarchical reinforcement learning (HRL) framework for efficient Navigation Among Movable Obstacles (NAMO) using a mobile manipulator. Our approach combines interaction-based obstacle property estimation with structured pushing strategies, facilitating the dynamic manipulation of unforeseen obstacles while adhering to a preplanned global path. The high-level policy generates pushing commands that consider environmental constraints and path-tracking objectives, while the low-level policy precisely and stably executes these commands through coordinated whole-body movements. Comprehensive simulation-based experiments demonstrate improvements in performing NAMO tasks, including higher success rates, shortened traversed path length, and reduced goal-reaching times, compared to baselines. Additionally, ablation studies assess the efficacy of each component, while a qualitative analysis further validates the accuracy and reliability of the real-time obstacle property estimation.

A quadruped robot faces balancing challenges on a six-degrees-of-freedom moving platform, like subways, buses, airplanes, and yachts, due to independent platform motions and resultant diverse inertia forces on the robot. To alleviate these challenges, we present the Learning-based Active Stabilization on Moving Platforms (LAS-MP), featuring a self-balancing policy and system state estimators. The policy adaptively adjusts the robot’s posture in response to the platform’s motion. The estimators infer robot and platform states based on proprioceptive sensor data. For a systematic training scheme across various platform motions, we introduce platform trajectory generation and scheduling methods. Our evaluation demonstrates superior balancing performance across multiple metrics compared to three baselines. Furthermore, we conduct a detailed analysis of the LAS-MP, including ablation studies and evaluation of the estimators, to validate the effectiveness of each component.

In the field of off-road navigation, integrating terrain data into path planning is becoming increasingly vital. It considers terrain roughness, slope, and step height, which are crucial parameters to ensure stability. This approach significantly differs from indoor driving scenarios, where the terrain is generally flat and exhibits less variability. In this paper, we define the traversability of terrain, numerically quantify it in terms of cost, and apply various asymptotically optimal planners, including RRT-Connect, RRT*, and PRM*, to identify the most cost-effective optimally traversable path. These planners are specifically designed to iteratively improve their solutions over time, gradually approaching the optimal solution as computation time increases. The effectiveness of these planners is evaluated within a limited time budget to assess their performance in simulation under realistic off-road conditions.

Real-time remote manipulation requires careful operations by a user to ensure the safety of a robot, which is designed to follow user's commands, against dynamic obstacles. However, a user may give commands to a robot at the risk of collision with dynamic obstacles due to a user's unfamiliar control ability or unexpected situations. In this paper, we propose a risk-aware user command adjustment method to avoid potential collision with dynamic obstacles. Our method consists of a network that predicts the risk of dynamic obstacles and another network that synthesizes commands to avoid obstacles. Based on the predicted risk, our method decides an adjusted command between a user command and a command to avoid collisions. We evaluate our method in problems that face collisions with dynamic obstacles when following given commands and in problems with static obstacles. We show that our method improves safety against the risk of dynamic obstacles or follows user commands when there is no risk. We also demonstrate the feasibility of our method using the real fetch manipulator with seven-degrees-of-freedom.

Trajectory optimization (TO) is an efficient tool to generate a redundant manipulator's joint trajectory following a 6-dimensional Cartesian path. The optimization performance largely depends on the quality of initial trajectories. However, the selection of a high-quality initial trajectory is non-trivial and requires a considerable time budget due to the extremely large space of the solution trajectories and the lack of prior knowledge about task constraints in configuration space. To alleviate the issue, we present a learning-based initial trajectory generation method that generates high-quality initial trajectories in a short time budget by adopting example-guided reinforcement learning. In addition, we suggest a null-space projected imitation reward to consider null-space constraints by efficiently learning kinematically feasible motion captured in expert demonstrations. Our statistical evaluation in simulation shows the improved optimality, efficiency, and applicability of TO when we plug in our method's output, compared with three other baselines. We also show the performance improvement and feasibility via real-world experiments with a seven-degree-of-freedom manipulator.

We propose a novel method to represent the inside and outside structures of a living biological sample with a label-free process using deep learning. The proposed approach combines the 3D refractive index characteristics with the deep learning method, making a new contribution to the bioimaging fields. In particular, the proposed deep learning model produces numerical 3D refractive indexes, which can not only provide important biometric information for the medical field but also analyze the statistical elements directly from the numerical output. We acquired a data set consisting of multi-viewed holographic images and 3D refractive index images of living blood-cell samples through holographic tomogram microscopy. We found that the proposed model’s PSNR is 10.17dB and 3D visualization of the generated 3D refractive index data is reasonably performed by comparing it with the ground truth.

A regular photographic picture provides 2-dimensional information of the object by recording only intensity of light, whereas a CGH provides 3-dimensional information about the object, including distance information, by recording light interference. CGH is becoming possible to display high-resolution CGH images due to advance in display technology. However, CGH requires a great deal of time to generate a high-resolution image because it calculates large volumes of information, such as light reflected from an object and light interference. To solve this problem, we propose a model that predicts interference information and generates high-resolution CGH images from low-resolution CGH images using a deep learning model. We propose a dual-generator GAN model consisting of two generators and one discriminator, and compare the results with existing models that generate high-resolution CGH images. The generated high-resolution CGH images were measured and evaluated using a SSIM and PSNR indicators.

This paper considers the problem of prolonged occlusions on navigation sensors due to dust, smudges, soils, etc. Such uncontrollable occlusions often cause lower visibility as well as higher uncertainty that require considerably sophisticated behavior. To secure visibility (i.e., confidence about the world), we propose a confidence-based navigation method that encourages the robot to explore the uncertain region around the robot maximizing its local confidence. To effectively extract features from the variable size of sensor occlusions, we adopt a point-cloud based representation network. Our method returns a resilient navigation policy via deep reinforcement learning, autonomously avoiding collisions under sensor occlusions while reaching a goal. We evaluate our method in simulated and real-world environments with either static or dynamic obstacles under various sensor-occlusion scenarios. The experimental result shows that our method outperforms baseline methods under the highly occurring sensor occlusion, and achieves maximum 90% and 80% success rates in the tested static and dynamic environments, respectively.