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Jiawei Xu

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Multi-rotor Aerial Vehicles in Physical Interactions: A Survey

Dec 05, 2023
Jiawei Xu

Research on Multi-rotor Aerial Vehicles (MAVs) has experienced remarkable advancements over the past two decades, propelling the field forward at an accelerated pace. Through the implementation of motion control and the integration of specialized mechanisms, researchers have unlocked the potential of MAVs to perform a wide range of tasks in diverse scenarios. Notably, the literature has highlighted the distinctive attributes of MAVs that endow them with a competitive edge in physical interaction when compared to other robotic systems. In this survey, we present a categorization of the various types of physical interactions in which MAVs are involved, supported by comprehensive case studies. We examine the approaches employed by researchers to address different challenges using MAVs and their applications, including the development of different types of controllers to handle uncertainties inherent in these interactions. By conducting a thorough analysis of the strengths and limitations associated with different methodologies, as well as engaging in discussions about potential enhancements, this survey aims to illuminate the path for future research focusing on MAVs with high actuation capabilities.

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Towards Robust Offline Reinforcement Learning under Diverse Data Corruption

Oct 19, 2023
Rui Yang, Han Zhong, Jiawei Xu, Amy Zhang, Chongjie Zhang, Lei Han, Tong Zhang

Offline reinforcement learning (RL) presents a promising approach for learning reinforced policies from offline datasets without the need for costly or unsafe interactions with the environment. However, datasets collected by humans in real-world environments are often noisy and may even be maliciously corrupted, which can significantly degrade the performance of offline RL. In this work, we first investigate the performance of current offline RL algorithms under comprehensive data corruption, including states, actions, rewards, and dynamics. Our extensive experiments reveal that implicit Q-learning (IQL) demonstrates remarkable resilience to data corruption among various offline RL algorithms. Furthermore, we conduct both empirical and theoretical analyses to understand IQL's robust performance, identifying its supervised policy learning scheme as the key factor. Despite its relative robustness, IQL still suffers from heavy-tail targets of Q functions under dynamics corruption. To tackle this challenge, we draw inspiration from robust statistics to employ the Huber loss to handle the heavy-tailedness and utilize quantile estimators to balance penalization for corrupted data and learning stability. By incorporating these simple yet effective modifications into IQL, we propose a more robust offline RL approach named Robust IQL (RIQL). Extensive experiments demonstrate that RIQL exhibits highly robust performance when subjected to diverse data corruption scenarios.

* 31 pages, 17 figures 
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A Novel Low-Cost, Recyclable, Easy-to-Build Robot Blimp For Transporting Supplies in Hard-to-Reach Locations

Sep 13, 2023
Karen Li, Shuhang Hou, Matyas Negash, Jiawei Xu, Edward Jeffs, Diego S. D'Antonio, David Saldaña

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Rural communities in remote areas often encounter significant challenges when it comes to accessing emergency healthcare services and essential supplies due to a lack of adequate transportation infrastructure. The situation is further exacerbated by poorly maintained, damaged, or flooded roads, making it arduous for rural residents to obtain the necessary aid in critical situations. Limited budgets and technological constraints pose additional obstacles, hindering the prompt response of local rescue teams during emergencies. The transportation of crucial resources, such as medical supplies and food, plays a vital role in saving lives in these situations. In light of these obstacles, our objective is to improve accessibility and alleviate the suffering of vulnerable populations by automating transportation tasks using low-cost robotic systems. We propose a low-cost, easy-to-build blimp robot (UAVs), that can significantly enhance the efficiency and effectiveness of local emergency responses.

* IEEE Global Humanitarian Technology Conference (GHTC 2023) 
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SBlimp: Design, Model, and Translational Motion Control for a Swing-Blimp

Aug 01, 2023
Jiawei Xu, Diego S D'antonio, Dominic J Ammirato, David Saldaña

We present an aerial vehicle composed of a custom quadrotor with tilted rotors and a helium balloon, called SBlimp. We propose a novel control strategy that takes advantage of the natural stable attitude of the blimp to control translational motion. Different from cascade controllers in the literature that controls attitude to achieve desired translational motion, our approach directly controls the linear velocity regardless of the heading orientation of the vehicle. As a result, the vehicle swings during the translational motion. We provide a planar analysis of the dynamic model, demonstrating stability for our controller. Our design is evaluated in numerical simulations with different physical factors and validated with experiments using a real-world prototype, showing that the SBlimp is able to achieve stable translation regardless of its orientation.

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Toward Fine Contact Interactions: Learning to Control Normal Contact Force with Limited Information

May 29, 2023
Jinda Cui, Jiawei Xu, David Saldaña, Jeff Trinkle

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Dexterous manipulation of objects through fine control of physical contacts is essential for many important tasks of daily living. A fundamental ability underlying fine contact control is compliant control, \textit{i.e.}, controlling the contact forces while moving. For robots, the most widely explored approaches heavily depend on models of manipulated objects and expensive sensors to gather contact location and force information needed for real-time control. The models are difficult to obtain, and the sensors are costly, hindering personal robots' adoption in our homes and businesses. This study performs model-free reinforcement learning of a normal contact force controller on a robotic manipulation system built with a low-cost, information-poor tactile sensor. Despite the limited sensing capability, our force controller can be combined with a motion controller to enable fine contact interactions during object manipulation. Promising results are demonstrated in non-prehensile, dexterous manipulation experiments.

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Finding Optimal Modular Robots for Aerial Tasks

May 29, 2023
Jiawei Xu, David Saldaña

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Traditional aerial vehicles have limitations in their capabilities due to actuator constraints, such as motor saturation. The hardware components and their arrangement are designed to satisfy specific requirements and are difficult to modify during operation. To address this problem, we introduce a versatile modular multi-rotor vehicle that can change its capabilities by reconfiguration. Our modular robot consists of homogeneous cuboid modules, propelled by quadrotors with tilted rotors. Depending on the number of modules and their configuration, the robot can expand its actuation capabilities. In this paper, we build a mathematical model for the actuation capability of a modular multi-rotor vehicle and develop methods to determine if a vehicle is capable of satisfying a task requirement. Based on this result, we find the optimal configurations for a given task. Our approach is validated in realistic 3D simulations, showing that our modular system can adapt to tasks with varying requirements.

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LLA-FLOW: A Lightweight Local Aggregation on Cost Volume for Optical Flow Estimation

Apr 17, 2023
Jiawei Xu, Zongqing Lu, Qingmin Liao

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Lack of texture often causes ambiguity in matching, and handling this issue is an important challenge in optical flow estimation tasks. Some methods insert stacked transformer modules that allow the network to use global information of cost volume for estimation. But the global information aggregation often incurs serious memory and time costs during training and inference, which hinders model deployment. We draw inspiration from the traditional local region constraint and design the local similarity aggregation (LSA) and the shifted local similarity aggregation (SLSA). The aggregation for cost volume is implemented with lightweight modules that act on the feature maps. Experiments on the final pass of Sintel show the lower cost required for our approach while maintaining competitive performance.

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Relative Policy-Transition Optimization for Fast Policy Transfer

Jun 13, 2022
Lei Han, Jiawei Xu, Cheng Zhou, Yizheng Zhang, Zhengyou Zhang

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We consider the problem of policy transfer between two Markov Decision Processes (MDPs). We introduce a lemma based on existing theoretical results in reinforcement learning (RL) to measure the relativity between two arbitrary MDPs, that is the difference between any two cumulative expected returns defined on different policies and environment dynamics. Based on this lemma, we propose two new algorithms referred to as Relative Policy Optimization (RPO) and Relative Transition Optimization (RTO), which can offer fast policy transfer and dynamics modeling, respectively. RPO updates the policy using the relative policy gradient to transfer the policy evaluated in one environment to maximize the return in another, while RTO updates the parameterized dynamics model (if there exists) using the relative transition gradient to reduce the gap between the dynamics of the two environments. Then, integrating the two algorithms offers the complete algorithm Relative Policy-Transition Optimization (RPTO), in which the policy interacts with the two environments simultaneously, such that data collections from two environments, policy and transition updates are completed in one closed loop to form a principled learning framework for policy transfer. We demonstrate the effectiveness of RPTO in OpenAI gym's classic control tasks by creating policy transfer problems via variant dynamics.

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SeqNet: An Efficient Neural Network for Automatic Malware Detection

May 08, 2022
Jiawei Xu, Wenxuan Fu, Haoyu Bu, Zhi Wang, Lingyun Ying

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Malware continues to evolve rapidly, and more than 450,000 new samples are captured every day, which makes manual malware analysis impractical. However, existing deep learning detection models need manual feature engineering or require high computational overhead for long training processes, which might be laborious to select feature space and difficult to retrain for mitigating model aging. Therefore, a crucial requirement for a detector is to realize automatic and efficient detection. In this paper, we propose a lightweight malware detection model called SeqNet which could be trained at high speed with low memory required on the raw binaries. By avoiding contextual confusion and reducing semantic loss, SeqNet maintains the detection accuracy when reducing the number of parameters to only 136K. We demonstrate the effectiveness of our methods and the low training cost requirement of SeqNet in our experiments. Besides, we make our datasets and codes public to stimulate further academic research.

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PogoDrone: Design, Model, and Control of a Jumping Quadrotor

Apr 01, 2022
Brian Zhu, Jiawei Xu, Andrew Charway, David Saldaña

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We present a design, model, and control for a novel jumping-flying robot that is called PogoDrone. The robot is composed of a quadrotor with a passive mechanism for jumping. The robot can continuously jump in place or fly like a normal quadrotor. Jumping in place allows the robot to quickly move and operate very close to the ground. For instance, in agricultural applications, the jumping mechanism allows the robot to take samples of soil. We propose a hybrid controller that switches from attitude to position control to allow the robot to fall horizontally and recover to the original position. We compare the jumping mode with the hovering mode to analyze the energy consumption. In simulations, we evaluate the effect of different factors on energy consumption. In real experiments, we show that our robot can repeatedly impact the ground, jump, and fly in a physical environment.

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