Zan Wang

IROS Conference 2025 Conference Paper

Env-Mani: Quadrupedal Robot Loco-Manipulation with Environment-in-the-Loop

• Yixuan Li
• Zan Wang
• Wei Liang

Dogs can climb onto tables using their front legs for support, enabling them to retrieve objects and significantly expand their workspace by leveraging the external environment. However, the ability of quadrupedal robots to perform similar skills remains largely unexplored. In this work, we introduce a unified, learning-based loco-manipulation framework for quadrupedal robots, allowing them to utilize the external environment as support to extend their workspace and enhance their manipulation capabilities. Specifically, our method proposes a unified policy that takes limited onboard sensors and proprioception as input, generating whole-body actions that enable the robot to manipulate objects. To guide the policy learning for environment-in-the-loop manipulation, we design a set of rewards that address challenges such as imprecise perception and center-of-mass shifts. Additionally, we employ curriculum learning to train both teacher and student policies, ensuring effective skill transfer in complex tasks. We train the policy in simulation and conduct extensive experiments, demonstrating that our approach allows robots to manipulate previously inaccessible objects, opening up new possibilities for enhancing quadrupedal robot capabilities without the need for hardware modifications or additional costs. The project page is available at https://sites.google.com/view/env-mani.

AAAI Conference 2025 Conference Paper

FloNa: Floor Plan Guided Embodied Visual Navigation

• Jiaxin Li
• Weiqi Huang
• Zan Wang
• Wei Liang
• Huijun Di
• Feng Liu

Humans naturally rely on floor plans to navigate in unfamiliar environments, as they are readily available, reliable, and provide rich geometrical guidance. However, existing visual navigation settings overlook this valuable prior knowledge, leading to limited efficiency and accuracy. To eliminate this gap, we introduce a novel navigation task: Floor Plan Visual Navigation (FloNa), the first attempt to incorporate floor plans into embodied visual navigation. While the floor plan offers significant advantages, two key challenges emerge: (1) handling the spatial inconsistency between the floor plan and the actual scene layout for collision-free navigation, and (2) aligning observed images with the floor plan sketch despite their distinct modalities. To address these challenges, we propose FloDiff, a novel diffusion policy framework incorporating a localization module to facilitate alignment between the current observation and the floor plan. We further collect 20k navigation episodes across 117 scenes in the iGibson simulator to support the training and evaluation. Extensive experiments demonstrate the effectiveness and efficiency of our framework in unfamiliar scenes using floor plan knowledge.

ICRA Conference 2025 Conference Paper

PhysPart: Physically Plausible Part Completion for Interactable Objects

• Rundong Luo
• Haoran Geng
• Congyue Deng
• Puhao Li
• Zan Wang
• Baoxiong Jia
• Leonidas J. Guibas
• Siyuan Huang 0001

Interactable objects are ubiquitous in our daily lives. Recent advances in 3D generative models make it possible to automate the modeling of these objects, benefiting a range of applications from 3D printing to the creation of robot simulation environments. However, while significant progress has been made in modeling 3D shapes and appearances, modeling object physics, particularly for interactable objects, remains challenging due to the physical constraints imposed by interpart motions. In this paper, we tackle the problem of physically plausible part completion for interactable objects, aiming to generate 3D parts that not only fit precisely into the object but also allow smooth part motions. To this end, we propose a diffusion-based part generation model that utilizes geometric conditioning through classifier-free guidance and formulates physical constraints as a set of stability and mobility losses to guide the sampling process. Additionally, we demonstrate the generation of dependent parts, paving the way toward sequential part generation for objects with complex part-whole hierarchies. Experimentally, we introduce a new metric for measuring physical plausibility based on motion success rates. Our model outperforms existing baselines over shape and physical metrics, especially those that do not adequately model physical constraints. We also demonstrate our applications in 3D printing, robot manipulation, and sequential part generation, showing our strength in realistic tasks with the demand for high physical plausibility.

IROS Conference 2024 Conference Paper

Mastering Scene Rearrangement with Expert-Assisted Curriculum Learning and Adaptive Trade-Off Tree-Search

• Zan Wang
• Hanqing Wang 0001
• Wei Liang 0008

Scene Rearrangement Planning (SRP) has recently emerged as a crucial interior scene task; however, current approaches still face two primary issues. First, prior works define the action space of SRP using handcrafted coarse-grained actions, which are inflexible for scene arrangement transition and impractical for real-world deployment. Secondly, the scarcity of realistic indoor scene rearrangement data hinders popular data-hungry learning approaches and quantitative evaluation. To tackle these issues, we propose a fine-grained action space definition and curate a large-scale scene rearrangement dataset to facilitate the training of learning approaches and comprehensive benchmarking. Building upon this dataset, we introduce a novel framework, PLATO, designed for efficient agent training and inference. Our approach features an exPert-assisted curriculum Learning (PL) paradigm that possesses a Behavior Cloning (BC) and an offline Reinforcement Learning (RL) curriculum for agent training, along with an advanced tree-search-based planner enhanced by an Adaptive Trade-Off (ATO) strategy to improve expert agent performance further. We demonstrate the superior performance of our method over baseline agents through extensive experiments and provide a detailed analysis to elucidate its rationale. Our project website can be accessed at plato.github.io.

IROS Conference 2024 Conference Paper

Visual Loop Closure Detection with Thorough Temporal and Spatial Context Exploitation

• Jiaxin Li
• Zan Wang
• Huijun Di
• Jian Li
• Wei Liang 0008

Despite advancements in visual Simultaneous Localization and Mapping (SLAM), prevailing visual Loop Closure Detection (LCD) methods primarily rely on computationally intensive image similarity comparisons, neglecting temporal-spatial context during long-term exploration. To address this issue, we propose TOSA, a novel visual LCD algorithm harnessing TempOral and SpAtial context for efficient LCD. Specifically, as the agent explores through time, our approach recurrently updates a latent feature incorporating historical information via a Long Short-Term Memory (LSTM) module. Upon receiving a query frame, TOSA seamlessly fuses the latent feature with the query feature to predict the candidates’ distribution, thus averting intensive similarity computation. Additionally, TOSA integrates a temporal-spatial convolution for candidate refinement by thoroughly exploiting the temporal consistency and spatial correlation to enhance selected candidates, further boosting the performance. Extensive experiments across four standard datasets showcase the superiority of our method over existing state-of-the-art techniques, demonstrating the effectiveness of utilizing rich temporal-spatial contexts.

NeurIPS Conference 2022 Conference Paper

HUMANISE: Language-conditioned Human Motion Generation in 3D Scenes

• Zan Wang
• Yixin Chen
• Tengyu Liu
• Yixin Zhu
• Wei Liang
• Siyuan Huang

Learning to generate diverse scene-aware and goal-oriented human motions in 3D scenes remains challenging due to the mediocre characters of the existing datasets on Human-Scene Interaction (HSI); they only have limited scale/quality and lack semantics. To fill in the gap, we propose a large-scale and semantic-rich synthetic HSI dataset, denoted as HUMANISE, by aligning the captured human motion sequences with various 3D indoor scenes. We automatically annotate the aligned motions with language descriptions that depict the action and the individual interacting objects; e. g. , sit on the armchair near the desk. HUMANIZE thus enables a new generation task, language-conditioned human motion generation in 3D scenes. The proposed task is challenging as it requires joint modeling of the 3D scene, human motion, and natural language. To tackle this task, we present a novel scene-and-language conditioned generative model that can produce 3D human motions of the desirable action interacting with the specified objects. Our experiments demonstrate that our model generates diverse and semantically consistent human motions in 3D scenes.

YNIMG Journal 2021 Journal Article

Identification of microRNA-9 linking the effects of childhood maltreatment on depression using amygdala connectivity

• Cancan He
• Ying Bai
• Zan Wang
• Dandan Fan
• Qing Wang
• Xinyi Liu
• Haisan Zhang
• Hongxing Zhang

AAAI Conference 2019 Short Paper

An Optimal Rewiring Strategy for Cooperative Multiagent Social Learning

• Hongyao Tang
• Jianye Hao
• Li Wang
• Tim Baarslag
• Zan Wang

Multiagent coordination in cooperative multiagent systems (MASs) has been widely studied in both fixed-agent repeated interaction setting and static social learning framework. However, two aspects of dynamics in real-world MASs are currently missing. First, the network topologies can dynamically change during the course of interaction. Second, the interaction utilities between each pair of agents may not be identical and not known as a prior. Both issues mentioned above increase the difficulty of coordination. In this paper, we consider the multiagent social learning in a dynamic environment in which agents can alter their connections and interact with randomly chosen neighbors with unknown utilities beforehand. We propose an optimal rewiring strategy to select most beneficial peers to maximize the accumulated payoffs in long-run interactions. We empirically demonstrate the effects of our approach in large-scale MASs.

AAMAS Conference 2019 Conference Paper

An Optimal Rewiring Strategy for Cooperative Multiagent Social Learning

• Hongyao Tang
• Jianye Hao
• Li Wang
• Zan Wang
• Tim Baarslag

Multiagent coordination is a key problem in cooperative multiagent systems (MASs). It has been widely studied in both fixed-agent repeated interaction setting and static social learning framework. However, two aspects of dynamics in real-world MASs are currently neglected. First, the network topologies can change during the course of interaction dynamically. Second, the interaction utilities can be different among each pair of agents and usually unknown before interaction. Both issues mentioned above increase the difficulty of coordination. In this paper, we consider the multiagent social learning in a dynamic environment in which agents can alter their connections and interact with randomly chosen neighbors with unknown utilities beforehand. We propose an optimal rewiring strategy to select most beneficial peers to maximize the accumulated payoffs in long-run interactions. We empirically demonstrate the effects of our approach in a variety of large-scale MASs.

AAMAS Conference 2019 Conference Paper

Automatic Feature Engineering by Deep Reinforcement Learning

• Jianyu Zhang
• Jianye Hao
• Françoise Fogelman-Soulié
• Zan Wang

We present a framework called Learning Automatic Feature Engineering Machine (LAFEM), which formalizes the Feature Engineering (FE) problem as an optimization problem over a Heterogeneous Transformation Graph (HTG). We propose a Deep Q-learning on HTG to support efficient learning of fine-grained and generalized FE policies that can transfer knowledge of engineering "good" features from a collection of datasets to other unseen datasets.

AAMAS Conference 2018 Conference Paper

Recurrent Deep Multiagent Q-Learning for Autonomous Agents in Future Smart Grid

• Yaodong Yang
• Jianye Hao
• Zan Wang
• Mingyang Sun
• Goran Strbac

The broker mechanism is widely applied to serve for interested parties to derive long-term policies to reduce costs or gain profits in smart grid. However, brokers are faced with a number of challenging problems such as balancing demand and supply from customers and competing with other coexisting brokers to maximize profits. In this paper, we develop an effective pricing strategy for brokers in local electricity retail market based on recurrent deep multiagent reinforcement learning and sequential clustering. We use real household electricity consumption data to simulate the retail market for evaluating our strategy. The experiments demonstrate the superior performance of the proposed pricing strategy and highlight the effectiveness of our reward shaping mechanism.

IJCAI Conference 2018 Conference Paper

Recurrent Deep Multiagent Q-Learning for Autonomous Brokers in Smart Grid

• Yaodong Yang
• Jianye Hao
• Mingyang Sun
• Zan Wang
• Changjie Fan
• Goran Strbac

The broker mechanism is widely applied to serve for interested parties to derive long-term policies in order to reduce costs or gain profits in smart grid. However, a broker is faced with a number of challenging problems such as balancing demand and supply from customers and competing with other coexisting brokers to maximize its profit. In this paper, we develop an effective pricing strategy for brokers in local electricity retail market based on recurrent deep multiagent reinforcement learning and sequential clustering. We use real household electricity consumption data to simulate the retail market for evaluating our strategy. The experiments demonstrate the superior performance of the proposed pricing strategy and highlight the effectiveness of our reward shaping mechanism.

YNICL Journal 2017 Journal Article

Brain insulin resistance deteriorates cognition by altering the topological features of brain networks

• Fan Su
• Hao Shu
• Qing Ye
• Zan Wang
• Chunming Xie
• Baoyu Yuan
• Zhijun Zhang
• Feng Bai

TAAS Journal 2017 Journal Article

Efficient and Robust Emergence of Norms through Heuristic Collective Learning

• Jianye Hao
• Jun Sun
• Guangyong Chen
• Zan Wang
• Chao Yu
• Zhong Ming

In multiagent systems, social norms serves as an important technique in regulating agents’ behaviors to ensure effective coordination among agents without a centralized controlling mechanism. In such a distributed environment, it is important to investigate how a desirable social norm can be synthesized in a bottom-up manner among agents through repeated local interactions and learning techniques. In this article, we propose two novel learning strategies under the collective learning framework, collective learning EV-l and collective learning EV-g, to efficiently facilitate the emergence of social norms. Extensive simulations results show that both learning strategies can support the emergence of desirable social norms more efficiently and be applicable in a wider range of multiagent interaction scenarios compared with previous work. The influence of different topologies is investigated, which shows that the performance of all strategies is robust across different network topologies. The influences of a number of key factors (neighborhood size, actions space, population size, fixed agents and isolated subpopulations) on norm emergence performance are investigated as well.