arXiv subjects

Mingjie Lin

Publications and source records attributed to Mingjie Lin.

14 recordsLinked to original sources

arXiv2025

Cross-Embodiment Robotic Manipulation Synthesis via Guided Demonstrations through CycleVAE and Human Behavior Transformer

Cross-embodiment robotic manipulation synthesis for complicated tasks is challenging, partially due to the scarcity of paired cross-embodiment datasets and the impediment of designing intricate controllers. Inspired by robotic learning via guided human expert demonstration, we here propose a novel cross-embodiment robotic manipulation algorithm via CycleVAE and human behavior transformer. First, we utilize unsupervised CycleVAE together with a bidirectional subspace alignment algorithm to align latent motion sequences between cross-embodiments. Second, we propose a casual human behavior transformer design to learn the intrinsic motion dynamics of human expert demonstrations. During the test case, we leverage the proposed transformer for the human expert demonstration generation, which will be aligned using CycleVAE for the final human-robotic manipulation synthesis. We validated our proposed algorithm through extensive experiments using a dexterous robotic manipulator with the robotic hand. Our results successfully generate smooth trajectories across intricate tasks, outperforming prior learning-based robotic motion planning algorithms. These results have implications for performing unsupervised cross-embodiment alignment and future autonomous robotics design. Complete video demonstrations of our experiments can be found in https://sites.google.com/view/humanrobots/home.

cs.RO↗

arXiv2024

APEX: Ambidextrous Dual-Arm Robotic Manipulation Using Collision-Free Generative Diffusion Models

Dexterous manipulation, particularly adept coordinating and grasping, constitutes a fundamental and indispensable capability for robots, facilitating the emulation of human-like behaviors. Integrating this capability into robots empowers them to supplement and even supplant humans in undertaking increasingly intricate tasks in both daily life and industrial settings. Unfortunately, contemporary methodologies encounter serious challenges in devising manipulation trajectories owing to the intricacies of tasks, the expansive robotic manipulation space, and dynamic obstacles. We propose a novel approach, APEX, to address all these difficulties by introducing a collision-free latent diffusion model for both robotic motion planning and manipulation. Firstly, we simplify the complexity of real-life ambidextrous dual-arm robotic manipulation tasks by abstracting them as aligning two vectors. Secondly, we devise latent diffusion models to produce a variety of robotic manipulation trajectories. Furthermore, we integrate obstacle information utilizing a classifier-guidance technique, thereby guaranteeing both the feasibility and safety of the generated manipulation trajectories. Lastly, we validate our proposed algorithm through extensive experiments conducted on the hardware platform of ambidextrous dual-arm robots. Our algorithm consistently generates successful and seamless trajectories across diverse tasks, surpassing conventional robotic motion planning algorithms. These results carry significant implications for the future design of diffusion robots, enhancing their capability to tackle more intricate robotic manipulation tasks with increased efficiency and safety. Complete video demonstrations of our experiments can be found in https://sites.google.com/view/apex-dual-arm/home.

cs.RO↗

arXiv2023

FPGA-QHAR: Throughput-Optimized for Quantized Human Action Recognition on The Edge

Accelerating Human Action Recognition (HAR) efficiently for real-time surveillance and robotic systems on edge chips remains a challenging research field, given its high computational and memory requirements. This paper proposed an integrated end-to-end HAR scalable HW/SW accelerator co-design based on an enhanced 8-bit quantized Two-Stream SimpleNet-PyTorch CNN architecture. Our network accelerator was trained on UCF101 and UCF24 datasets and implemented on edge SoC-FPGA. Our development uses partially streaming dataflow architecture to achieve higher throughput versus network design and resource utilization trade-off. We also fused all convolutional, batch-norm, and ReLU operations into a single homogeneous layer and utilized the Lucas-Kanade motion flow method to enable a high parallelism accelerator design and optimized on-chip engine computing.Furthermore, our proposed methodology achieved nearly 81% prediction accuracy with an approximately 24 FPS real-time inference throughput at 187MHz on ZCU104, which is 1.7x - 1.9x higher than the prior research. Lastly, the designed framework was benchmarked against several hardware chips for higher throughput and performance measurements and is now available as an open-source project on GitHub for training and implementation on edge platforms.

cs.CV↗

arXiv2023

RETRO: Reactive Trajectory Optimization for Real-Time Robot Motion Planning in Dynamic Environments

Reactive trajectory optimization for robotics presents formidable challenges, demanding the rapid generation of purposeful robot motion in complex and swiftly changing dynamic environments. While much existing research predominantly addresses robotic motion planning with predefined objectives, emerging problems in robotic trajectory optimization frequently involve dynamically evolving objectives and stochastic motion dynamics. However, effectively addressing such reactive trajectory optimization challenges for robot manipulators proves difficult due to inefficient, high-dimensional trajectory representations and a lack of consideration for time optimization. In response, we introduce a novel trajectory optimization framework called RETRO. RETRO employs adaptive optimization techniques that span both spatial and temporal dimensions. As a result, it achieves a remarkable computing complexity of $O(T^{2.4}) + O(Tn^{2})$, a significant improvement over the traditional application of DDP, which leads to a complexity of $O(n^{4})$ when reasonable time step sizes are used. To evaluate RETRO's performance in terms of error, we conducted a comprehensive analysis of its regret bounds, comparing it to an Oracle value function obtained through an Oracle trajectory optimization algorithm. Our analytical findings demonstrate that RETRO's total regret can be upper-bounded by a function of the chosen time step size. Moreover, our approach delivers smoothly optimized robot trajectories within the joint space, offering flexibility and adaptability for various tasks. It can seamlessly integrate task-specific requirements such as collision avoidance while maintaining real-time control rates. We validate the effectiveness of our framework through extensive simulations and real-world robot experiments in closed-loop manipulation scenarios.

cs.RO↗

arXiv2022

Unified Control Framework for Real-Time Interception and Obstacle Avoidance of Fast-Moving Objects with Diffusion Variational Autoencoder

Real-time interception of fast-moving objects by robotic arms in dynamic environments poses a formidable challenge due to the need for rapid reaction times, often within milliseconds, amidst dynamic obstacles. This paper introduces a unified control framework to address the above challenge by simultaneously intercepting dynamic objects and avoiding moving obstacles. Central to our approach is using diffusion-based variational autoencoder for motion planning to perform both object interception and obstacle avoidance. We begin by encoding the high-dimensional temporal information from streaming events into a two-dimensional latent manifold, enabling the discrimination between safe and colliding trajectories, culminating in the construction of an offline densely connected trajectory graph. Subsequently, we employ an extended Kalman filter to achieve precise real-time tracking of the moving object. Leveraging a graph-traversing strategy on the established offline dense graph, we generate encoded robotic motor control commands. Finally, we decode these commands to enable real-time motion of robotic motors, ensuring effective obstacle avoidance and high interception accuracy of fast-moving objects. Experimental validation on both computer simulations and autonomous 7-DoF robotic arms demonstrates the efficacy of our proposed framework. Results indicate the capability of the robotic manipulator to navigate around multiple obstacles of varying sizes and shapes while successfully intercepting fast-moving objects thrown from different angles by hand. Complete video demonstrations of our experiments can be found in https://sites.google.com/view/multirobotskill/home.

cs.RO↗

arXiv2022

Hardware-Efficient Template-Based Deep CNNs Accelerator Design

Acceleration of Convolutional Neural Network (CNN) on edge devices has recently achieved a remarkable performance in image classification and object detection applications. This paper proposes an efficient and scalable CNN-based SoC-FPGA accelerator design that takes pre-trained weights with a 16-bit fixed-point quantization and target hardware specification to generate an optimized template capable of achieving higher performance versus resource utilization trade-off. The template analyzed the computational workload, data dependency, and external memory bandwidth and utilized loop tiling transformation along with dataflow modeling to convert convolutional and fully connected layers into vector multiplication between input and output feature maps, which resulted in a single compute unit on-chip. Furthermore, the accelerator was examined among AlexNet, VGG16, and LeNet networks and ran at 200-MHz with a peak performance of 230 GOP/s depending on ZYNQ boards and state-space exploration of different compute unit configurations during simulation and synthesis. Lastly, our proposed methodology was benchmarked against the previous development on Ultra96 for higher performance measurement.

cs.AR↗

arXiv2022

HMT: A Hardware-Centric Hybrid Bonsai Merkle Tree Algorithm for High-Performance Authentication

Merkle tree is a widely used tree structure for authentication of data/metadata in a secure computing system. Recent state-of-the art secure systems use a smaller-sized MT, namely Bonsai Merkle Tree (BMT) to protect the metadata such as encryption counters. Common BMT algorithms were designed for traditional Von Neumann architectures with a software-centric implementation in mind, hence they use a lot of recursions and are often sequential in nature. However, the modern heterogeneous computing platforms employing Field-Programmable Gate Array (FPGA) devices require concurrency-focused algorithms to fully utilize the versatility and parallel nature of such systems. Our goal for this work is to introduce HMT, a hardware-friendly BMT algorithm that enables the verification and update processes to function independently and provides the benefits of relaxed update while being comparable to eager update in terms of update complexity. The methodology of HMT contributes both novel algorithm revisions and innovative hardware techniques to implementing BMT. We introduce a hybrid BMT algorithm that is hardware-targeted, parallel and relaxes the update depending on BMT cache hit but makes the update conditions more flexible compared to lazy update to save additional write-backs. Deploying this new algorithm, we have designed a new BMT controller with a dataflow architecture, speculative buffers and parallel write-back engines that allows for multiple concurrent relaxed authentication. Our empirical performance measurements have demonstrated that HMT can achieve up to 7x improvement in bandwidth and 4.5x reduction in latency over baseline in subsystem level tests. In a real secure-memory system on a Xilinx U200 accelerator FPGA, HMT exhibits up to 14\% faster execution in standard benchmarks compared to state-of-the art BMT solution on FPGA.

cs.CR↗

arXiv2022

DAMON: Dynamic Amorphous Obstacle Navigation using Topological Manifold Learning and Variational Autoencoding

DAMON leverages manifold learning and variational autoencoding to achieve obstacle avoidance, allowing for motion planning through adaptive graph traversal in a pre-learned low-dimensional hierarchically-structured manifold graph that captures intricate motion dynamics between a robotic arm and its obstacles. This versatile and reusable approach is applicable to various collaboration scenarios. The primary advantage of DAMON is its ability to embed information in a low-dimensional graph, eliminating the need for repeated computation required by current sampling-based methods. As a result, it offers faster and more efficient motion planning with significantly lower computational overhead and memory footprint. In summary, DAMON is a breakthrough methodology that addresses the challenge of dynamic obstacle avoidance in robotic systems and offers a promising solution for safe and efficient human-robot collaboration. Our approach has been experimentally validated on a 7-DoF robotic manipulator in both simulation and physical settings. DAMON enables the robot to learn and generate skills for avoiding previously-unseen obstacles while achieving predefined objectives. We also optimize DAMON's design parameters and performance using an analytical framework. Our approach outperforms mainstream methodologies, including RRT, RRT*, Dynamic RRT*, L2RRT, and MpNet, with 40\% more trajectory smoothness and over 65\% improved latency performance, on average.

cs.RO↗

arXiv2022

SERA: Safe and Efficient Reactive Obstacle Avoidance for Collaborative Robotic Planning in Unstructured Environments

Safe and efficient collaboration among multiple robots in unstructured environments is increasingly critical in the era of Industry 4.0. However, achieving robust and autonomous collaboration among humans and other robots requires modern robotic systems to have effective proximity perception and reactive obstacle avoidance. In this paper, we propose a novel methodology for reactive whole-body obstacle avoidance that ensures conflict-free robot-robot interactions even in dynamic environment. Unlike existing approaches based on Jacobian-type, sampling based or geometric techniques, our methodology leverages the latest deep learning advances and topological manifold learning, enabling it to be readily generalized to other problem settings with high computing efficiency and fast graph traversal techniques. Our approach allows a robotic arm to proactively avoid obstacles of arbitrary 3D shapes without direct contact, a significant improvement over traditional industrial cobot settings. To validate our approach, we implement it on a robotic platform consisting of dual 6-DoF robotic arms with optimized proximity sensor placement, capable of working collaboratively with varying levels of interference. Specifically, one arm performs reactive whole-body obstacle avoidance while achieving its pre-determined objective, while the other arm emulates the presence of a human collaborator with independent and potentially adversarial movements. Our methodology provides a robust and effective solution for safe human-robot collaboration in non-stationary environments.

cs.RO↗

arXiv2022

Non-Parametric Stochastic Policy Gradient with Strategic Retreat for Non-Stationary Environment

In modern robotics, effectively computing optimal control policies under dynamically varying environments poses substantial challenges to the off-the-shelf parametric policy gradient methods, such as the Deep Deterministic Policy Gradient (DDPG) and Twin Delayed Deep Deterministic policy gradient (TD3). In this paper, we propose a systematic methodology to dynamically learn a sequence of optimal control policies non-parametrically, while autonomously adapting with the constantly changing environment dynamics. Specifically, our non-parametric kernel-based methodology embeds a policy distribution as the features in a non-decreasing Euclidean space, therefore allowing its search space to be defined as a very high (possible infinite) dimensional RKHS (Reproducing Kernel Hilbert Space). Moreover, by leveraging the similarity metric computed in RKHS, we augmented our non-parametric learning with the technique of AdaptiveH- adaptively selecting a time-frame window of finishing the optimal part of whole action-sequence sampled on some preceding observed state. To validate our proposed approach, we conducted extensive experiments with multiple classic benchmarks and one simulated robotics benchmark equipped with dynamically changing environments. Overall, our methodology has outperformed the well-established DDPG and TD3 methodology by a sizeable margin in terms of learning performance.

cs.RO↗

arXiv2022

Hardware-Efficient Deconvolution-Based GAN for Edge Computing

Generative Adversarial Networks (GAN) are cutting-edge algorithms for generating new data samples based on the learned data distribution. However, its performance comes at a significant cost in terms of computation and memory requirements. In this paper, we proposed an HW/SW co-design approach for training quantized deconvolution GAN (QDCGAN) implemented on FPGA using a scalable streaming dataflow architecture capable of achieving higher throughput versus resource utilization trade-off. The developed accelerator is based on an efficient deconvolution engine that offers high parallelism with respect to scaling factors for GAN-based edge computing. Furthermore, various precisions, datasets, and network scalability were analyzed for low-power inference on resource-constrained platforms. Lastly, an end-to-end open-source framework is provided for training, implementation, state-space exploration, and scaling the inference using Vivado high-level synthesis for Xilinx SoC-FPGAs, and a comparison testbed with Jetson Nano.

cs.LG↗

arXiv2021

Survivable Robotic Control through Guided Bayesian Policy Search with Deep Reinforcement Learning

Many robot manipulation skills can be represented with deterministic characteristics and there exist efficient techniques for learning parameterized motor plans for those skills. However, one of the active research challenge still remains to sustain manipulation capabilities in situation of a mechanical failure. Ideally, like biological creatures, a robotic agent should be able to reconfigure its control policy by adapting to dynamic adversaries. In this paper, we propose a method that allows an agent to survive in a situation of mechanical loss, and adaptively learn manipulation with compromised degrees of freedom -- we call our method Survivable Robotic Learning (SRL). Our key idea is to leverage Bayesian policy gradient by encoding knowledge bias in posterior estimation, which in turn alleviates future policy search explorations, in terms of sample efficiency and when compared to random exploration based policy search methods. SRL represents policy priors as Gaussian process, which allows tractable computation of approximate posterior (when true gradient is intractable), by incorporating guided bias as proxy from prior replays. We evaluate our proposed method against off-the-shelf model free learning algorithm (DDPG), testing on a hexapod robot platform which encounters incremental failure emulation, and our experiments show that our method improves largely in terms of sample requirement and quantitative success ratio in all failure modes. A demonstration video of our experiments can be viewed at: https://sites.google.com/view/survivalrl

cs.RO↗

arXiv2020

Survivable Hyper-Redundant Robotic Arm with Bayesian Policy Morphing

In this paper we present a Bayesian reinforcement learning framework that allows robotic manipulators to adaptively recover from random mechanical failures autonomously, hence being survivable. To this end, we formulate the framework of Bayesian Policy Morphing (BPM) that enables a robot agent to self-modify its learned policy after the diminution of its maneuvering dimensionality. We build upon existing actor-critic framework, and extend it to perform policy gradient updates as posterior learning, taking past policy updates as prior distributions. We show that policy search, in the direction biased by prior experience, significantly improves learning efficiency in terms of sampling requirements. We demonstrate our results on an 8-DOF robotic arm with our algorithm of BPM, while intentionally disabling random joints with different damage types like unresponsive joints, constant offset errors and angular imprecision. Our results have shown that, even with physical damages, the robotic arm can still successfully maintain its functionality to accurately locate and grasp a given target object.

cs.RO↗

arXiv2013

Inferring High Quality Co-Travel Networks

Social networks provide a new perspective for enterprises to better understand their customers and have attracted substantial attention in industry. However, inferring high quality customer social networks is a great challenge while there are no explicit customer relations in many traditional OLTP environments. In this paper, we study this issue in the field of passenger transport and introduce a new member to the family of social networks, which is named Co-Travel Networks, consisting of passengers connected by their co-travel behaviors. We propose a novel method to infer high quality co-travel networks of civil aviation passengers from their co-booking behaviors derived from the PNRs (Passenger Naming Records). In our method, to accurately evaluate the strength of ties, we present a measure of Co-Journey Times to count the co-travel times of complete journeys between passengers. We infer a high quality co-travel network based on a large encrypted PNR dataset and conduct a series of network analyses on it. The experimental results show the effectiveness of our inferring method, as well as some special characteristics of co-travel networks, such as the sparsity and high aggregation, compared with other kinds of social networks. It can be expected that such co-travel networks will greatly help the industry to better understand their passengers so as to improve their services. More importantly, we contribute a special kind of social networks with high strength of ties generated from very close and high cost travel behaviors, for further scientific researches on human travel behaviors, group travel patterns, high-end travel market evolution, etc., from the perspective of social networks.

cs.SI↗