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新论文 · v1arXiv:2611.14853

Can I join you? Extended Social Force Based Model with Predictive Control and Barrier Functions for Humanoid Group Repositioning Amidst Obstacles

Chuxuan Zhang, Niloofar Jahanshahi, Yasaman Etesam, Mo Chen, Angelica Lim

原文摘要

As social and service robots increasingly share interaction spaces in public, they may form groups while coordinating tasks or interacting with humans. Initial group formation alone is insufficient in dynamic settings, because it can be disrupted when a person or another agent joins, leaves, or passes through the group. Although group dynamics and repositioning have been widely studied in swarm robotics and virtual-agent settings, little attention has been given to socially appropriate repositioning behaviors in groups of humanoid robots. Existing social-force-based approaches provide a useful foundation for group behavior, but they have limited obstacle handling, and can produce unsafe behavior as environmental complexity increases. We propose a novel framework that modulates traditional Social Force, guided by Model Predictive Control (MPC) to plan ahead, bounded by Control Barrier Functions (CBF) to increase safety for robot groups repositioning behaviors. This approach aims to produce safer and socially aware group repositioning behaviors under various group formation disturbance. We evaluate the method in simulation through an ablation study and an online user study. The results show that our proposed method can produce smoother and safer repositioning behaviors in complex environments while preserving social appropriateness.

cs.RO · cs.HC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39524

Last-Iterate Convergence for Constrained MDPs: From Dual Descent to Primal-Dual

Michael Lu, Max Qiushi Lin, Mo Chen, Sharan Vaswani

原文摘要

We study policy optimization for infinite-horizon, discounted constrained Markov decision processes. While primal-dual ($\texttt{PD}$) methods are widely used in practice, their theoretical guarantees typically hold for an impractical mixture policy. In contrast, dual descent ($\texttt{DD}$) methods output a single policy ("last iterate") with guarantees on its performance. However, such methods require iteratively solving entropy-regularized MDPs in an inner-loop, limiting their practical effectiveness. Moreover, existing analyses rely on strong coverage assumptions that are difficult to satisfy in practice. Consequently, we first provide a new analysis of $\texttt{DD}$ methods that does not require coverage assumptions. We then employ entropy-regularized natural policy gradient and dual regularization to eliminate the inner loop, resulting in an efficient $\texttt{PD}$ algorithm. We develop a novel tracking analysis which shows that, compared to existing methods, the proposed algorithm achieves improved last-iterate convergence guarantees. Furthermore, our theoretical framework allows us to analyze a truncated bilevel method that interpolates between $\texttt{DD}$ and $\texttt{PD}$ methods. Finally, we generalize our analysis to handle linear function approximation. Compared to prior last-iterate methods, we prove stronger guarantees under less restrictive assumptions.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2512.22793

Reach-Avoid Differential game with Reachability Analysis for UAVs: A decomposition approach

Minh Bui, Simon Monckton, Mo Chen

原文摘要

Reach-avoid (RA) games have significant applications in security and defense, particularly for unmanned aerial vehicles (UAVs). These problems are inherently challenging due to the need to consider obstacles, consider the adversarial nature of opponents, ensure optimality, and account for nonlinear dynamics. Hamilton-Jacobi (HJ) reachability analysis has emerged as a powerful tool for tackling these challenges; however, while it has been applied to games involving two spatial dimensions, directly extending this approach to three spatial dimensions is impossible due to high dimensionality. On the other hand, alternative approaches for solving RA games lack the generality to consider games with three spatial dimensions involving agents with non-trivial system dynamics. In this work, we propose a novel framework for dimensionality reduction by decomposing the problem into a horizontal RA sub-game and a vertical RA sub-game. We then solve each sub-game using HJ reachability analysis and consider second-order dynamics that account for the defender's acceleration. To reconstruct the solution to the original RA game from the sub-games, we introduce a HJ-based tracking control algorithm in each sub-game that not only guarantees capture of the attacker but also tracking of the attacker thereafter. We prove the conditions under which the capture guarantees are maintained. The effectiveness of our approach is demonstrated via numerical simulations, showing that the decomposition maintains optimality and guarantees in the original problem. Our methods are also validated in a Gazebo physics simulator, achieving successful capture of quadrotors in three spatial dimensions space for the first time to the best of our knowledge.

eess.SY · cs.AI · cs.GT

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1116ACL Anthology · ACL 2026

JanusMM: A Benchmark for Self-Deprecation Understanding in Real-World Multimodal Conversations

Xinyi Xu, Bingguang Hao ByteDance Inc., Yongyi Xiong, Zimo Chen University of Science and Technology of China, Xinchen Liu, Hongxin Guo, Xuelong Wang, Silin Zhou 等 9 位作者

原文摘要

Self-deprecation is a prevalent communicative strategy in human society, often using image-text interplay to express emotions and intentions. Despite self-deprecation is widespread in real-world conversations, the ability of multimodal large language models (MLLMs) to understand it remains underexplored. To fill this gap, we introduce JanusMM, the first benchmark designed to evaluate MLLMs’ understanding of self-deprecation in real-world conversations. JanusMM contains 2,016 bilingual memes from three types of social interactions and provides a dual-task evaluation framework with six new metrics. The first task assesses MLLMs’ abilities in self-deprecation recognition and reasoning, while the second task evaluates the consistency of their understanding by simulating the perspectives of the initiator and responder. We evaluate ten frontier MLLMs and find that they exhibit weak recognition and reasoning abilities, with their understanding of self-deprecation remaining inconsistent across both perspectives.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-short.48ACL Anthology · ACL 2026

PExA: Parallel Exploration Agent for Complex Text-to-SQL

Tanmay Parekh, Ella Hofmann-Coyle, Shuyi Wang, Sachith Sri Ram Kothur Bloomberg, Srivas Prasad, Yunmo Chen Microsoft

原文摘要

LLM-based agents for text-to-SQL often struggle with latency-performance trade-off, where performance improvements come at the cost of latency or vice versa. We reformulate text-to-SQL generation within the lens of software test coverage where the original query is prepared with a suite of test cases with simpler, atomic SQLs that are executed in parallel and together ensure semantic coverage of the original query. After iterating on test case coverage, the final SQL is generated only when enough information is gathered, leveraging the explored test case SQLs to ground the final generation. We validated our framework on a state-of-the-art benchmark for text-to-SQL, Spider 2.0, achieving a new state-of-the-art with 70.2% execution accuracy.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · 2026 · 正式发表