Multi-Objective Alignment (MOA) aims to align LLMs' responses with multiple human preference objectives, with Direct Preference Optimization (DPO) emerging as a prominent approach. However, we find that DPO-based MOA approaches suffer from widespread preference conflicts in the data, where different objectives favor different responses. This results in conflicting optimization directions, hindering the optimization on the Pareto Front. To address this, we propose to construct Pareto-optimal responses to resolve preference conflicts. To efficiently obtain and utilize such responses, we propose a self-improving DPO framework that enables LLMs to self-generate and select Pareto-optimal responses for self-supervised preference alignment. Extensive experiments on two datasets demonstrate the superior Pareto Front achieved by our framework compared to various baselines. Code is available at \url{https://github.com/zyttt-coder/SIPO}.

Prior studies have found that providing explanations about robots' decisions and actions help to improve system transparency, increase human users' trust of robots, and enable effective human-robot collaboration. Different users have various preferences about what should be included in explanations. However, little research has been conducted for the generation of personalized explanations. In this paper, we present a system for generating personalized explanations of robotic planning via user feedback. We consider robotic planning using Markov decision processes (MDPs) and develop an algorithm to automatically generate a personalized explanation of an optimal robotic plan (i.e., an optimal MDP policy) based on the user preference regarding four elements (i.e., objective, locality, specificity, and abstraction). In addition, we design the system to interact with users via answering users' further questions about the generated explanations. Users have the option to update their preferences to view different explanations. The system is capable of detecting and resolving any preference conflict via user interaction. Our user study results show that the generated personalized explanations improve user satisfaction, while the majority of users liked the system's capabilities of question-answering, and conflict detection and resolution.