United States

Enabling humanoid robots to perform long-horizon mobile manipulation planning in real-world environments based on embodied perception and comprehension abilities has been a longstanding challenge. 
With the recent rise of large language models (LLMs), there has been a notable increase in the development of LLM-based planners. These approaches either utilize human-provided textual representations of the real world or heavily depend on prompt engineering to extract such representations, lacking the capability to quantitatively understand the environment, such as determining the feasibility of manipulating objects.
To address these limitations, we present the Instruction-Augmented Long-Horizon Planning (IALP) system, a novel framework that employs LLMs to generate feasible and optimal actions based on real-time sensor feedback, including grounded knowledge of the environment, in a closed-loop interaction. Distinct from prior works, our approach augments user instructions into PDDL problems by leveraging both the abstract reasoning capabilities of LLMs and grounding mechanisms.
By conducting various real-world long-horizon tasks, each consisting of seven distinct manipulatory skills, our results demonstrate that the IALP system can efficiently solve these tasks with an average success rate exceeding 80\%.
Our proposed method can operate as a high-level planner for open-world long-horizon tasks, equipping robots with substantial autonomy in unstructured environments through the utilization of multi-modal sensor inputs.

AAAI 2025

Instruction-Augmented Long-Horizon Planning: Embedding Grounding Mechanisms in Embodied Mobile Manipulation

manipulation

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

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AAAI 2026

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

technical paper

We are pleased to announce the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), which will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

### [Invited Speakers](https://aaai.org/conference/aaai/aaai-25/aaai-25-invited-speakers/)

Register [here](https://aaai.org/conference/aaai/aaai-25/registration/)

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.



Model counting is task of counting the number of satisfying assignments of a Boolean formula. Since counting is intractable in general, most applications use $(\varepsilon, \delta)$-approximations, where the output is within a $(1+\varepsilon)$-factor of the count with  probability at least $1-\delta$. 
Many demanding applications make thousands of counting queries, and the state-of-the-art approximate counter, ApproxMC, makes hundreds of calls to SAT solvers to answer a single approximate counting query.
The sheer number of SAT calls, poses a significant challenge to the existing approaches. 

In this work, we propose an approximation scheme, RealMC, that is tailored to such demanding applications with low time limits. Compared to ApproxMC, RealMC makes 14$\times$ fewer SAT calls while providing the same guarantees as ApproxMC in the constant-factor regime. In an evaluation over 2,247 instances, RealMC solved 271 more and achieved a $2\times$ speedup against ApproxMC.

Towards Real-Time Approximate Counting

Image-guided object assembly represents a burgeoning research topic in computer vision. This paper introduces a novel task: translating multi-view images of a structural 3D model (for example, one constructed with building blocks drawn from a 3D-object library) into a detailed sequence of assembly instructions executable by a robotic arm. Fed with multi-view images of the target 3D model for replication, the model designed for this task must address several sub-tasks, including recognizing individual components used in constructing the 3D model, estimating the geometric pose of each component, and deducing a feasible assembly order adhering to physical rules. Establishing accurate 2D-3D correspondence between multi-view images and 3D objects is technically challenging. To tackle this, we propose an end-to-end model known as the Neural Assembler. This model learns an object graph where each vertex represents recognized components from the images, and the edges specify the topology of the 3D model, enabling the derivation of an assembly plan. We establish benchmarks for this task and conduct comprehensive empirical evaluations of Neural Assembler and alternative solutions. Our experiments clearly demonstrate the superiority of Neural Assembler.

Neural Assembler: Learning to Generate Fine-Grained Robotic Assembly Instructions from Multi-View Images

Due to the communication bottleneck in distributed and decentralized federated learning applications, algorithms using compressed communication have attracted significant attention. The Error Feedback (EF) is a widely-studied compression framework for convergence with biased compressors such as top-$k$ sparsification. Although various improvements have been obtained in recent years, the theoretical guarantee for EF-type framework is still limited. Previous works either 1) rely on strong assumptions such as bounded gradient/dissimilarity assumptions, thus can not deal with arbitrary data heterogeneity and also slow the convergence speed, or 2) can not enjoy linear speedup in the number of clients. In this work, we propose a new EFSkip framework which removes the strong assumptions to allow arbitrary data heterogeneity and enjoys linear speedup for significantly improving upon previous results. In particular, EFSkip achieves the complexity result $O(\frac{\sigma^2}{n\epsilon^4} + \frac{1}{\epsilon^2})$ while previous EF21 only obtains $O(\frac{\sigma^2}{\delta^3\epsilon^4} + \frac{1}{\delta\epsilon^2})$, i.e., EFSkip enjoys the linear speedup in the number of clients $n$ (reducing the result linearly using more clients) and also removes the compression factor $\delta$ (matching the result without compression). We also show that EFSkip enjoys linear speedup and achieves faster convergence for nonconvex problems satisfying Polyak-Lojasiewicz (PL) condition. Finally, the deep learning experiments validate the theoretical results and confirm the practical superiority of EFSkip. We believe that the new EFSkip framework will have a large impact on the communication- and computation-efficient distributed and decentralized federated learning.

EFSkip: A New Error Feedback with Linear Speedup for Compressed Federated Learning with Arbitrary Data Heterogeneity

Dynamic graphs exhibit the intricate and intertwined spatio-temporal evolutionary patterns, widely existing in real-world scenarios. Nevertheless, the structure incompleteness, noise, and redundancy result in poor robustness for the prevailing Dynamic Graph Neural Networks (DGNNs). Dynamic Graph Structure Learning (DGSL) offers promising insight into optimizing graph structures. However, aside from encountering unacceptable quadratic complexity, it overly relies on the heuristic priors, making it difficult to discover underlying predictive patterns. How to efficiently refine dynamic structures, capture intrinsic dependencies, and learn robust representations for downstream tasks is still under-explored. In this work, we propose the **REDGSL**, an **E**fficient **D**ynamic **G**raph **S**tructure **L**earning framework for **R**obust graph representations. To accelerate the spatio-temporal structure learning, we propose a kernelized dynamic message-passing operator that reduces the quadratic time complexity to linear. To capture global intrinsic dynamics, we establish the dynamic graph as a self-contained system with State Space Model. By discretizing the system states with the cross-snapshot graph adjacency, we enable the long-distance dependencies capturing with the selective snapshot scan. To endow learned dynamic structures more expressive with informativeness, we propose the self-supervised Principle of Relevant Information for DGSL to regularize the most relevant yet least redundant information, enhancing global robustness. Extensive experiments demonstrate the superiority of the efficiency and robustness of our REDGSL compared with the state-of-the-art baselines against adversarial attacks.

DG-Mamba: Robust and Efficient Dynamic Graph Structure Learning with Selective State Space Models

The ability to autonomously explore and resolve tasks with minimal human guidance is crucial for the self-development of embodied robots. Although reinforcement learning methods can largely ease human effort, designing reward functions for real-world tasks, especially high-dimensional robotic control, is challenging due to complex relationships among joints and tasks. Recent advancements in the aid of large language models (LLMs) enable automatic reward function design. However, current approaches evaluate reward functions by re-training policies from scratch placing an undue burden on the reward function, expecting it to be effective at every stage of the policy improvement process, from initiation to convergence. We argue for a more practical strategy in robotic autonomy, focusing on refining existing policies with policy-dependent reward functions rather than seeking a universal reward function. To this end, we propose a novel reward-policy co-evolution framework where the reward function and the learned policy benefit from each other's progressive on-the-fly improvements, resulting in more efficient and higher-performing skill acquisition. 
Specifically, the reward evolution process translates the robot's previous best reward function, descriptions of tasks and environment into text inputs. These inputs are used to query LLMs to generate a dynamic amount of reward function candidates, ensuring continuous improvement through each evolutionary round. For policy evolution, our method generates new policy populations by hybridizing historically optimal and random policies. Through an improved Bayesian optimization, our approach efficiently and robustly identifies the most capable and plastic reward-policy combination, which then proceeds to the next round of co-evolution.
Despite using less data, our approach demonstrates an average normalized improvement of $95.3$\% across various high-dimensional robotic skill learning tasks.

Efficient Language-instructed Skill Acquisition via Reward-Policy Co-Evolution

This paper presents an experiential learning pedagogy that teaches undergraduate business management infor-mation systems students hands-on AI skills through the lens of sustainability. The learning modules aim to em-power undergraduate business students to gain interest and confidence in AI knowledge, skills, and careers, to sharpen their higher order thinking abilities, and to help them gain a deeper understanding of sustainability is-sues. Students learn AI through developing chatbots that address pressing sustainability issues within their own communities. Results of the pilot study indicate that students have increased self-efficacy in AI, more positive attitudes towards AI learning and AI-related careers, en-hanced sustainability awareness, and more confidence in their ability to innovate.

Developing Chatbots for Sustainability: Experiential Learning in an Undergraduate Business Course

Developing competency in artificial intelligence is becoming increasingly crucial for computer science (CS) students at all levels of the CS curriculum. However, most previous research focuses on advanced CS courses, as traditional introductory courses provide limited opportunities to develop AI skills and knowledge. This paper introduces an introductory CS course where students learn computational thinking through computer vision, a sub-field of AI, as an application context. The course aims to achieve computational thinking outcomes alongside critical thinking outcomes that expose students to AI approaches and their societal implications. Through experiential activities such as individual projects and reading discussions, our course seeks to balance technical learning and critical thinking goals. Our evaluation, based on pre-and post-course surveys, shows an improved sense of belonging, self-efficacy, and AI ethics awareness among students. The results suggest that an AI-focused context can enhance participation and employability, student-selected projects support self-efficacy, and ethically grounded AI instruction can be effective for interdisciplinary audiences. Students' discussions on reading assignments demonstrated deep engagement with the complex challenges in today's AI landscape. Finally, we share insights on scaling such courses for larger cohorts and improving the learning experience for introductory CS students.

Computational Thinking with Computer Vision: Developing AI Competency in an Introductory Computer Science Course

Responsible AI (RAI) is the science and practice of ensuring the design, development, use, and oversight of AI are socially sustainable---benefiting diverse stakeholders while controlling the risks. Achieving this goal requires active engagement and participation from the broader public. This paper introduces "We are AI: Taking Control of Technology," a public education course that brings the topics of AI and RAI to the general audience in a peer-learning setting. 

We outline the goals behind the course's development, discuss the multi-year iterative process that shaped its creation, and summarize its content. We also discuss two offerings of "We are AI" to an active and engaged group of librarians and professional staff at New York University, highlighting successes and areas for improvement. The course materials, including a multilingual comic book series by the same name, are publicly available and can be used independently. By sharing our experience in creating and teaching "We are AI", we aim to introduce these resources to the community of AI educators, researchers, and practitioners, supporting their public education efforts.

We Are AI: Taking Control of Technology

This talk explores the challenge of customizing large-scale AI models, particularly generative AI, on cost-effective devices with limited memory and energy resources. Modern AI models demand substantial computational power, often relying on specialized hardware such as GPUs. To address this, the talk introduces compression-aware computing, a framework enabling AI models to recognize and adapt to their compressed states while preserving performance. Compression-aware computing integrates compression techniques like sparsification, quantization, and low-rank decomposition to enhance the efficiency and accuracy of AI models, broadening these models' accessibility across diverse devices. Additionally, this talk highlights one rationale of scalable and sustainable AI in advancing Alzheimer’s research by facilitating the analysis of large single-cell transcriptomics datasets for gene-gene interaction discovery.

Compression-Aware Computing for Scalable and Sustainable AI

Probabilistic inference is a fundamental challenge in machine learning, spanning tasks from approximate Bayesian inference to generative AI. In this talk, I will present theoretically-guaranteed scalable and efficient probabilistic inference with applications in Bayesian deep learning and generative modeling. First, I will introduce a general framework that leverages modern accelerators—specifically low-precision computation—to significantly accelerate inference while preserving accuracy. Next, I will present a new framework for efficient inference in discrete domains, using gradient information to achieve much more informed and directional exploration. Finally, I will showcase experimental results demonstrating the effectiveness of these methods across various ML tasks, including Bayesian neural networks, energy-based models, and large language models.

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Towards Real-Time Approximate Counting

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