Embodied AI is a crucial frontier in robotics, capable of planning and executing action sequences for robots to accomplish long-horizon tasks physical environments. In this work, we introduce EmbodiedGPT, an end-to-end multi-modal foundation model embodied AI, empowering agents with understanding execution capabilities. To achieve this, have made the following efforts: (i) We craft large-scale dataset, termed EgoCOT. The dataset consists carefully selected videos from Ego4D along...

Quantile regression is increasingly encountered in modern big data applications due to its robustness and flexibility. We consider the scenario of learning conditional quantiles a specific target population when available may go beyond be supplemented from other sources that possibly share similarities with target. A crucial question how properly distinguish use useful information improve quantile estimation inference at develop transfer methods for high-dimensional by detecting informative...

This work describes the development of a robotic system that acquires knowledge incrementally through human interaction where new objects and motions are taught on fly. The developed was one five finalists in KUKA Innovation Award competition demonstrated during Hanover Messe 2018 Germany. main contributions i) novel incremental object learning module - deep based localization recognition allows to teach robot, ii) an intuitive user interface for specifying 3D motion task associated with...

Wheelchair-mounted robotic manipulators have the potential to help elderly and individuals living with disabilities carry out their activities of daily (ADLs) independently. Robotics researchers focus on assistive tasks from perspective various control schemes motion types, whereas, health research focuses clinical assessment rehabilitation, arguably leaving important differences between two domains. In particular, there been many studies which are relevant functional independence, but...

We consider the problem of mapless collision-avoidance navigation where humans are present using 2D laser scans. Our proposed method uses ego-safety to measure collision from robot's perspective and social-safety impact actions on surrounding pedestrians. Specifically, part predicts intrusion action into interaction area with humans. train policy reinforcement learning a simple simulator directly evaluate learned in Gazebo real robot tests. Experiments show smoothly transferred different...

We present a robot eye-hand coordination learning method that can directly learn visual task specification by watching human demonstrations. Task is represented as function, which learned using inverse reinforcement learning(IRL [1]) inferring reward model from state transitions. The then used continuous feedbacks in an uncalibrated servoing(UVS [2]) controller designed for the execution phase. Our proposed raw videos, removes need hand-engineered specification. Benefiting use of traditional...

We consider the problem of visual imitation learning without human kinesthetic teaching or teleoperation, nor access to an interactive reinforcement training environment. present a geometric perspective this where feature correspondences are learned from one video and used execute tasks via servoing. Specifically, we propose VGS-IL (Visual Geometric Skill Imitation Learning), end-to-end geometry-parameterized task concept inference method, infer globally consistent association rules...

The overall survival (OS) rate of patients with colorectal cancer (CRC) remains low due to the lack clear prognostic markers. Therefore, identification valuable markers is urgently required. Snail and E-Cadherin (E-Cad) are important protein molecules in EMT process play a crucial role tumor invasion metastasis. present study investigated clinical significance E-cad expression CRC. Compared those adjacent tissue, levels were significantly increased decreased, respectively, Moreover, high...

Based on the static (scalar measurement) and aggregated QAR data, group carried out quantile regression to explore influential features that affects maximum vertical acceleration during landing used sufficient dimension reduction methods verify findings. Nine were suggested be patterns how they affect hard analyzed.

Reinforcement learning using a novel predictive representation is applied to autonomous driving accomplish the task of between lane markings where substantial benefits in performance and generalization are observed on unseen test roads both simulation real Jackal robot. The learned by general value functions (GVFs) provide out-of-policy, or counter-factual, predictions future centeredness road angle that form compact state agent improving online offline reinforcement learn drive an vehicle...

We study the problem of learning manipulation skills from human demonstration video by inferring association relationships between geometric features. Motivation for this work stems observation that humans perform eye-hand coordination tasks using primitives to define a task while control error drives through execution. propose graph based kernel regression method directly infer underlying constraints Incremental Maximum Entropy Inverse Reinforcement Learning (InMaxEnt IRL). The learned...

We consider real-world reinforcement learning (RL) of robotic manipulation tasks that involve both visuomotor skills and contact-rich skills. aim to train a policy maps multimodal sensory observations (vision force) manipulator's joint velocities under practical considerations. propose use offline samples learn set general value functions (GVFs) make counterfactual predictions from the visual inputs. show combining learned with force feedbacks in online allows efficient given only terminal...

In this article, a new composite quantile regression estimation (CQR) approach is proposed for partially linear varying coefficient models (PLVCM) under loss function with B-spline approximations. The major advantage of the procedures over existing ones easy to implement using software, and it requires no specification error distributions. Under regularity conditions, consistency asymptotic normality estimators are also derived. Finally, simulation study real data application undertaken...

Traditional statistical methods and machine learning on massive datasets are challenging owing to limitations of computer primary memory. Composite quantile regression neural network (CQRNN) is an efficient robust estimation method. But most existing computational algorithms cannot solve CQRNN for reliably efficiently. In this end, we propose a divide conquer (DC-CQRNN) method extend datasets. The major idea the overall dataset into some subsets, applying data within each final results...

Voltage sags are a serious problem within power supplies, which pose threats to both residential electricity and industrial manufacturing. Since any one sag may be recorded by multiple monitoring devices from different substations, the issue of redundant information in data arises. In this regard, novel method for voltage events based on projection technology, shape dynamic time warping (shapeDTW), spectral clustering is proposed. The main contributions paper summarized as follows: (1) We...

Abstract It is crucial for vehicular communications to optimize the field strength coverage on roads, which can be illustrated by radio map (RM). In this paper, a deep convolutional neural network‐long short‐term memory (DCNN‐LSTM) model construction of road RM proposed. First, multi‐modal dataset built, including measured strength, longitude, latitude and elevation data obtained at various measurement points, as well outline maps buildings that are close points. Second, DCNN‐LSTM designed...