Forgetful Large Language Models: Lessons Learned from Using LLMs in Robot Programming

Large language models offer new ways of empowering people to program robot applications-namely, code generation via prompting. However, the code generated by LLMs is susceptible to errors. This work reports a preliminary exploration that empirically characterizes common errors produced by LLMs in robot programming. We categorize these errors into two phases: interpretation and execution. In this work, we focus on errors in execution and observe that they are caused by LLMs being "forgetful" of key information provided in user prompts. Based on this observation, we propose prompt engineering tactics designed to reduce errors in execution. We then demonstrate the effectiveness of these tactics with three language models: ChatGPT, Bard, and LLaMA-2. Finally, we discuss lessons learned from using LLMs in robot programming and call for the benchmarking of LLM-powered end-user development of robot applications.Comment: 9 pages ,8 figures, accepted by the AAAI 2023 Fall Symposium Serie

ID.8: Co-Creating Visual Stories with Generative AI

Storytelling is an integral part of human culture and significantly impacts cognitive and socio-emotional development and connection. Despite the importance of interactive visual storytelling, the process of creating such content requires specialized skills and is labor-intensive. This paper introduces ID.8, an open-source system designed for the co-creation of visual stories with generative AI. We focus on enabling an inclusive storytelling experience by simplifying the content creation process and allowing for customization. Our user evaluation confirms a generally positive user experience in domains such as enjoyment and exploration, while highlighting areas for improvement, particularly in immersiveness, alignment, and partnership between the user and the AI system. Overall, our findings indicate promising possibilities for empowering people to create visual stories with generative AI. This work contributes a novel content authoring system, ID.8, and insights into the challenges and potential of using generative AI for multimedia content creation

An Adaptive Controller Design for Flexible-joint Electrically-driven Robots With Consideration of Time-Varying Uncertainties

Almost all present control strategies for electrically-driven robots are under the rigid robot assumption. Few results can be found for the control of electrically driven robots with joint flexibility. This is because the presence of the joint flexibility greatly increases the complexity of the system dynamics. What is worse is when some system dynamics are not available and a good performance controller is required. In this paper, an adaptive design is proposed to this challenging problem. A backstepping-like procedure incorporating the model reference adaptive control is employed to circumvent the difficulty introduced by its cascade structure and various uncertainties. A Lyapunov-like analysis is used to justify the closed-loop stability and boundedness of internal signals. Moreover, the upper bounds of tracking errors in the transient state are also derived. Computer simulation results are presented to demonstrate the usefulness of the proposed scheme. Keywords: Adaptive control; Flexible-joint electrically-driven robot; FAT 2. Introduction Control of rigid robots has been well understood in recent years, but most of the schemes ignore the dynamics coming from electric motors and harmonic drivers that are widely implemented in the industrial robots. However, actuator dynamics constitute an important part of the complete robot dynamics, especially in the cases of high-velocity movement and highly varying loads[1],[2]. The main reason for using a reduced model is to simplify complexity of controller design. For each joint, consideration of the flexibility from the M. C. Chien was with the Department of Mechanical Engineering, National Taiwan University of Science and Technology. He is now with the Mechanical and Systems Research Laboratories, Industrial Technology Research Institute, No. 195, Sec. 4, Chung-Hsing Rd., Chutung, Hsinchu, 310, Taiwan, R.O.C. (e-mail: [email protected]). 2 A. C. Huang is with the Department of Mechanical Engineering, National Taiwan University of Science and Technology. No. 43, Keelung Rd., Sec. 4, Taipei, Taiwan, ROC. (Tel:+886-2-27376490, Fax: +886-2-37376460, E-mail: [email protected]). (A. C. Huang provides phone number because he is the corresponding author.

Analysis on the Efficiency of Risk Management in the Chinese Listed Companies

[[abstract]]Since a firm’s profitability is associated with a degree of risk taking, risk indicators have been extensively treated as exogenous variables and affected firm performance. The level of risk taking should be determined through internal control quality and firm-specific characteristics to effectively understand the relationship between risk management and firm performance. This study aims to investigate the effects of risk management efficiency on the production efficiency of Chinese listed companies from 2002 to 2016 using the two-step data envelopment analysis (DEA) approach. Empirical results indicate that risk management differs from traditional financial theory, which means that high-level risk would earn high expected returns. Firms with a low efficiency index of enterprises risk management will have low performance. In particular, internal controls were significantly improved after the 2008 financial crisis. Our overall results also suggest that information asymmetry is still a problem in financial markets. To achieve maximum benefits for shareholders and improve the quality of information disclosure, methods for enacting market regulations are still very important issues in China.[[notice]]補正完