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Enhancing Troubleshooting Task-Oriented Dialog Systems with Large Language models 1

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17th international conference on Intelligent robotics and Applications

作者： Zhou, Jiahao Zhang, Qiang Zhang, Fengda Yuan, Caixia Beijing Univ Posts & Telecommun Beijing Peoples R China State Grid Smart Grid Res Inst CoLtd Beijing Peoples R China State Grid Shandong Elect Power Res Inst Jinan Peoples R China

ISBN: (数字)9789819607921

ISBN: (纸本)9789819607914;9789819607921

Task-oriented dialog (TOD) systems use external knowledge sources to help users accomplish specific tasks. While most current TOD research focuses on simple information-collecting tasks in a slot-filling framework, multi-step reasoning tasks like troubleshooting remain under-explored. Leveraging the advancements of large language models (LLMs), we propose a novel LLM-based multi-agent learning framework to build troubleshooting dialogue systems and evaluate the effectiveness of various multi-agent learning settings in a TOD system. Our results show that LLMs designed for open-domain dialog face challenges when directly applied to TOD systems, but with multi-agent cooperative enhancements, LLMs can achieve commendable performance.

关键词： Large Language Model Task Oriented Dialog System Multi-agent

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Relative Distributed Formation and Obstacle Avoidance with Multi-agent Reinforcement Learning 39

Relative Distributed Formation and Obstacle Avoidance with M...

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IEEE international conference on robotics and automation (ICRA)

作者： Yan, Yuzi Li, Xiaoxiang Qiu, Xinyou Qiu, Jiantao Wang, Jian Wang, Yu Shen, Yuan Tsinghua Univ Dept Elect Engn Beijing Peoples R China Tsinghua Univ Beijing Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9781728196817

Multi-agent formation as well as obstacle avoidance is one of the most actively studied topics in the field of multi-agent systems. Although some classic controllers like model predictive control (MPC) and fuzzy control achieve a certain measure of success, most of them require precise global information which is not accessible in harsh environments. On the other hand, some reinforcement learning (RL) based approaches adopt the leader-follower structure to organize different agents' behaviors, which sacrifices the collaboration between agents thus suffering from bottlenecks in maneuverability and robustness. In this paper, we propose a distributed formation and obstacle avoidance method based on multi-agent reinforcement learning (MARL). Agents in our system only utilize local and relative information to make decisions and control themselves distributively, and will reorganize themselves into a new topology quickly in case that any of them is disconnected. Our method achieves better performance regarding formation error, formation convergence rate and on-par success rate of obstacle avoidance compared with baselines (both classic control methods and another RL-based method). The feasibility of our method is verified by both simulation and hardware implementation with Ackermann-steering vehicles.

关键词： Geometry Adaptation models Reinforcement learning Hardware Robustness Topology Collision avoidance

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Combining Model-based and Data-based approaches for online predictions of human trajectories 10

Combining Model-based and Data-based approaches for online p...

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10th IEEE RAS/EMBS international conference for Biomedical robotics and Biomechatronics (BioRob)

作者： Orhan, Aymeric Verdel, Dorian Bruneau, Olivier Geffard, Franck Berret, Bastien Imperial Coll Sci Technol & Med Dept Bioengn Human Robot Grp London W12 0BZ England Univ Paris Saclay Mech Engn Dept ENS Paris Saclay LURPA F-91190 Gif Sur Yvette France Univ Paris Saclay Sport Sci Dept CIAMS Orsay France Univ Orleans CIAMS Orleans France Univ Paris Saclay List CEA Palaiseau France

ISBN: (纸本)9798350386523;9798350386530

Accurately predicting human movement trajectories is of critical interest in multiple fields, including human-exoskeleton interaction. In general, such predictions can be obtained from model-based approaches (e.g., optimal control theory) or from data-driven approaches (e.g., learning from human demonstrations). Data-driven methods generally require numerous demonstrations but avoid the computational burden of model-based methods. In this paper, we introduce a hybrid method mixing the strengths of these two approaches to enable the prediction of human trajectories in a receding-horizon fashion when the number of actual demonstrations is very limited. First, we propose to use the stochastic optimal feedforward-feedback control framework to generate a large set of humanlike trajectories, including their variability. Second, we used the probabilistic movement primitives (ProMPs) framework to learn the distribution of these synthetic trajectories and make online predictions about the upcoming human trajectories from the observation of past movement data. Here, we evaluated our hybrid method on an existing data set composed of arm reaching movements in a parasagittal plane. Overall, our method proves to be advantageous when generalization is needed and demonstrations are lacking, such as in novel targets scenarios. The introduced method shows promise to efficiently predict realistic human trajectories on a given time horizon, even when limited or no human demonstration is available for the task at hand.

关键词： Prediction models

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Probabilistic Inference of Simulation Parameters via Parallel Differentiable Simulation 39

Probabilistic Inference of Simulation Parameters via Paralle...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Heiden, Eric Denniston, Christopher E. Millard, David Ramos, Fabio Sukhatme, Gaurav S. Univ Southern Calif Dept Comp Sci Los Angeles CA 90007 USA NVIDIA Seattle WA USA USC Los Angeles CA USA

ISBN: (纸本)9781728196817

Reproducing real world dynamics in simulation is critical for the development of new control and perception methods. This task typically involves the estimation of simulation parameter distributions from observed rollouts through an inverse inference problem characterized by multi-modality and skewed distributions. We address this challenging problem through a novel Bayesian inference approach that approximates a posterior distribution over simulation parameters given real sensor measurements. By extending the commonly used Gaussian likelihood model for trajectories via the multiple-shooting formulation, our gradient-based particle inference algorithm, Stein Variational Gradient Descent, is able to identify highly nonlinear, underactuated systems. We leverage GPU code generation and differentiable simulation to evaluate the likelihood and its gradient for many particles in parallel. Our algorithm infers nonparametric distributions over simulation parameters more accurately than comparable baselines and handles constraints over parameters efficiently through gradient-based optimization. We evaluate estimation performance on several physical experiments. On an underactuated mechanism where a 7-DOF robot arm excites an object with an unknown mass configuration, we demonstrate how the inference technique can identify symmetries between the parameters and provide highly accurate predictions.

关键词： Uncertainty Estimation Predictive models Probabilistic logic Robot sensing systems Prediction algorithms Inference algorithms

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Stronger Generalization Guarantees for Robot Learning by Combining Generative models and Real-World Data 39

Stronger Generalization Guarantees for Robot Learning by Com...

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IEEE international conference on robotics and automation (ICRA)

作者： Agarwal, Abhinav Veer, Sushant Ren, Allen Z. Majumdar, Anirudha Princeton Univ Dept Mech & Aerosp Engn Princeton NJ 08544 USA NVIDIA Res Santa Clara CA 95051 USA

ISBN: (纸本)9781728196817

We are motivated by the problem of learning policies for robotic systems with rich sensory inputs (e.g., vision) in a manner that allows us to guarantee generalization to environments unseen during training. We provide a framework for providing such generalization guarantees by leveraging a finite dataset of real-world environments in combination with a (potentially inaccurate) generative model of environments. The key idea behind our approach is to utilize the generative model in order to implicitly specify a prior over policies. This prior is updated using the real-world dataset of environments by minimizing an upper bound on the expected cost across novel environments derived via Probably Approximately Correct (PAC)-Bayes generalization theory. We demonstrate our approach on two simulated systems with nonlinear/hybrid dynamics and rich sensing modalities: (i) quadrotor navigation with an onboard vision sensor, and (ii) grasping objects using a depth sensor. Comparisons with prior work demonstrate the ability of our approach to obtain stronger generalization guarantees by utilizing generative models. We also present hardware experiments for validating our bounds for the grasping task.

关键词： Training Upper bound Navigation Grasping Vision sensors Robot sensing systems Robot learning

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On methods for Modeling Controlled Dynamics in Stabilization Problems

On Methods for Modeling Controlled Dynamics in Stabilization...

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2024 international Russian automation conference, RusAutoCon 2024

作者： Krasinskiy, Aleksandr Ya. Aitov, Vasily Gr. Dept. of Computer Modeling and Probability Theory Moscow Russia Information and Computing Center Rosbiotech Moscow Russia

Automatic devices make up the vast maj ority of technological equipment in modern production. To obtain an output product of appropriate quality, the most complex program control algorithms can be implemented in actuators, including those found by mathematical control theory methods. Stable given operating mode implementation for controlled systems is ensured by its stabilization. In mathematical control theory, rigorous methods have been developed for solving stabilization problems;the effectiveness of their practical application is determined by the adequacy of the mathematical model of the control object used. The article outlines the main approaches to building models with a discussion of their required accuracy. Specific examples of solving stabilization problems for certain technical systems classes with different orders models of accuracy are given. © 2024 IEEE.

关键词： Stabilization

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A Gradient-free Penalty ADMM for Solving Distributed Convex Optimization Problems with Feasible Set Constraints 17

A Gradient-free Penalty ADMM for Solving Distributed Convex ...

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17th international conference on Control, automation, robotics and Vision (ICARCV)

作者： Liu, Chenyang Dou, Xiaohua Cheng, Songsong Fan, Yuan Anhui Univ Sch Elect Engn & Automat Anhui Engn Lab Human Robot Integrat Syst & Intell Hefei 230601 Anhui Peoples R China Jiuquan Satellite Launching Ctr Dept Tech Lanzhou 732750 Gansu Peoples R China

ISBN: (纸本)9781665476874

In this paper, we propose a gradient-free penalty ADMM to distributedly solve constrained convex optimization problems over network systems. All agents only communicate with their neighbors and local decision variables constrained in feasible set constraints. We design a pseudo-gradient to obtain an approximate gradient. Then we update decision variables based on the approximate gradient and penalty ADMM method. Moreover, we analyze the convergent performance of the proposed algorithm and summarize some conclusions.

关键词： Distributed optimization feasible set constraints ADMM gradient-free methods penalty

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Review of Inference Time Prediction Approaches of DNN: Emphasis on Service robots with cloud-edge-device architecture

Review of Inference Time Prediction Approaches of DNN: Empha...

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2023 IEEE international conference on robotics and Biomimetics, ROBIO 2023

作者： Xiang, Tian Meng, Qiwei Zhang, Ji Zhang, Beibei Song, Wei Xie, Anhuan Gu, Jason Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Research Center for Intelligent Robotics Hangzhou311100 China The Chinese University of Hong Kong T Stone Robotics Institute Department of Mechanical and Automation Engineering Hong Kong Research Institute of Intelligent Computing Zhejiang Laboratory Research Center for Intelligent Computing Software Hangzhou311100 China Dalhousie University Electrical and Computer Engineering Halifax Canada

ISBN: (纸本)9798350325706

In recent years, the global robot market has witnessed substantial growth, particularly in the domain of service robots. Despite their expanding presence, service robots encounter limitations when operating autonomously in unstructured environments, primarily due to their constrained computational capacities. As a solution, the fusion of cloud and edge computing resources becomes imperative to expedite task inference and enhance scenario perception capabilities. The integration of cloud-edge-device models holds significant promise in bolstering the operational efficiency of robots. This entails the dynamic partitioning of intricate robotic tasks, executed collaboratively across cloud, edge, and device resources. In this landscape, deep neural network (DNN) models play a pivotal role in facilitating a wide array of robotic tasks. The inference time for each layer of a DNN model in actual deployment, emerges as a critical determinant in the model's partitioning strategy. It also serves as an important metric influencing the model's suitability for a specific hardware platform. This article presents an overview of recent advancements in predicting inference and training time of DNN models, summarizes the related methods, and finally discusses the challenges in this field and the research that can be studied in the future. © 2023 IEEE.

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Hybrid Event Shaping to Stabilize Periodic Hybrid Orbits 39

Hybrid Event Shaping to Stabilize Periodic Hybrid Orbits

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IEEE international conference on robotics and automation (ICRA)

作者： Zhu, James Kong, Nathan J. Council, George Johnson, Aaron M. Carnegie Mellon Univ Dept Mech Engn Pittsburgh PA 15213 USA

ISBN: (纸本)9781728196817

Many controllers for legged robotic systems leverage open- or closed-loop control at discrete hybrid events to enhance stability. These controllers appear in several well studied phenomena such as the Raibert stepping controller, paddle juggling, and swing leg retraction. This work introduces hybrid event shaping (HES): a generalized method for analyzing and designing stable hybrid event controllers. HES utilizes the saltation matrix, which gives a closed-form equation for the effect that hybrid events have on stability. We also introduce shape parameters, which are higher order terms that can be tuned completely independently from the system dynamics to promote stability. Optimization methods are used to produce values of these parameters that optimize a stability measure. Hybrid event shaping captures previously developed control methods while also producing new optimally stable trajectories without the need for continuous-domain feedback.

关键词： Legged locomotion System dynamics Shape Optimization methods Extraterrestrial measurements Stability analysis Orbits

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methods for Combining and Representing Non-Contextual Autonomy Scores for Unmanned Aerial Systems 8

Methods for Combining and Representing Non-Contextual Autono...

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8th international conference on automation, robotics and Applications (ICARA)

作者： Hertel, Brendan Donald, Ryan Dumas, Christian Ahmadzadeh, S. Reza Univ Massachusetts Lowell Persistent Auton & Robot Learning PeARL Lab Lowell MA 01854 USA

ISBN: (纸本)9781665483834

Measuring an overall autonomy score for a robotic system requires the combination of a set of relevant aspects and features of the system that might be measured in different units, qualitative, and/or discordant. In this paper, we build upon an existing non-contextual autonomy framework that measures and combines the Autonomy Level and the Component Performance of a system as an overall autonomy score. We examine several methods of combining features, showing how some methods find different rankings from the same data. We discuss resolving this issue by employing the weighted product method. Furthermore, we introduce two new means for representing relative and absolute autonomy score, namely, the autonomy coordinate system and the autonomy distance which represents the overall autonomy of a system. We apply our method to a set of seven Unmanned Aerial Systems (UAS) and obtain their absolute autonomy score as well as their relative score with respect to the best system.

关键词： Autonomy Non-Contextual Scoring Ranking Unmanned Aerial Systems

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