Shiqi Zhang

AAAI Conference 2026 Conference Paper

From Woofs to Words: Towards Intelligent Robotic Guide Dogs with Verbal Communication

• Yohei Hayamizu
• David DeFazio
• Hrudayangam Mehta
• Zainab Altaweel
• Jacqueline Choe
• Chao Lin
• Jake Juettner
• Furui Xiao

Assistive robotics is an important subarea of robotics that focuses on the well-being of people with disabilities. A robotic guide dog is an assistive quadruped robot for assisting visually impaired people in obstacle avoidance and navigation. Enabling language capabilities on robotic guide dogs goes beyond naively adding an existing dialog system onto a mobile robot. The novel challenges include grounding language to the dynamically changing environment and improving spatial awareness for the human handler. To address those challenges, we develop a novel dialog system for robotic guide dogs that uses large language models to verbalize both navigational plans and scenes. The goal is to enable verbal communication for collaborative decision-making within the handler-robot team. In experiments, we performed a human study to evaluate different verbalization strategies, and a simulation study to evaluate the efficiency and accuracy in navigation tasks.

AAAI Conference 2026 Conference Paper

HABIT: Chrono-Synergia Robust Progressive Learning Framework for Composed Image Retrieval

• Zixu Li
• Yupeng Hu
• Zhiwei Chen
• Shiqi Zhang
• Qinlei Huang
• Zhiheng Fu
• Yinwei Wei

Composed Image Retrieval (CIR) is a flexible image retrieval paradigm that enables users to accurately locate the target image through a multimodal query composed of a reference image and modification text. Although this task has demonstrated promising applications in personalized search and recommendation systems, it encounters a severe challenge in practical scenarios known as the Noise Triplet Correspondence (NTC) problem. This issue primarily arises from the high cost and subjectivity involved in annotating triplet data. To address this problem, we identify two central challenges: the precise estimation of composed semantic discrepancy and the insufficient progressive adaptation to modification discrepancy. To tackle these challenges, we propose a cHrono-synergiA roBust progressIve learning framework for composed image reTrieval (HABIT), which consists of two core modules. First, the Mutual Knowledge Estimation Module quantifies sample cleanliness by calculating the Transition Rate of mutual information between the composed feature and the target image, thereby effectively identifying clean samples that align with the intended modification semantics. Second, the Dual-consistency Progressive Learning Module introduces a collaborative mechanism between the historical and current models, simulating human habit formation to retain good habits and calibrate bad habits, ultimately enabling robust learning under the presence of NTC. Extensive experiments conducted on two standard CIR datasets demonstrate that HABIT significantly outperforms most methods under various noise ratios, exhibiting superior robustness and retrieval performance.

EAAI Journal 2025 Journal Article

Dynamic Interactive Graph Convolutional Recurrent Network for bidirectional spatiotemporal traffic flow forecasting

• Zhen Liu
• Shiqi Zhang
• Yuzhuang Pian
• Yonghong Liu

Accurate prediction of traffic inflow and outflow is essential for efficient urban mobility management and multimodal transit systems. However, existing approaches struggle with two main challenges: (i) The dynamic spatiotemporal heterogeneity that varies across different regions and times, complicating the prediction task. (ii) The asymmetric interdependence between inflows and outflows is often overlooked, leading to an inadequate representation of intricate bidirectional relationships. To address these challenges, we propose the Dynamic Interactive Graph Convolutional Recurrent Network (DIGCRN). In particular, DIGCRN incorporates an inflow and outflow feature interaction learning that utilizes an interactive gated mechanism to achieve spatiotemporal characteristic transformation, thereby capturing the asymmetric interdependence between inflows and outflows. Subsequently, a gated recurrent unit based on an adaptive graph convolutional network is employed to recursively capture spatiotemporal features. The final multi-scale convolution module realizes the fusion of inflow and outflow features at different granularity levels. Comprehensive empirical evaluations on the Hangzhou Metro and New York City Taxi datasets indicate that DIGCRN surpasses all baselines, achieving improvements of up to 3. 46% in mean absolute error (MAE) and 7. 28% in root mean square error (RMSE) compared to the best-performing baseline models. The code is available at https: //github. com/LiuZhen1234567/DIGCRN.

NeurIPS Conference 2025 Conference Paper

IDOL: Meeting Diverse Distribution Shifts with Prior Physics for Tropical Cyclone Multi-Task Estimation

• HantingYan Yan
• Pan Mu
• Shiqi Zhang
• Yuchao Zhu
• Jinglin Zhang
• Cong Bai

Tropical Cyclone (TC) estimation aims to accurately estimate various TC attributes in real time. However, distribution shifts arising from the complex and dynamic nature of TC environmental fields, such as varying geographical conditions and seasonal changes, present significant challenges to reliable estimation. Most existing methods rely on multi-modal fusion for feature extraction but overlook the intrinsic distribution of feature representations, leading to poor generalization under out-of-distribution (OOD) scenarios. To address this, we propose an effective Identity Distribution-Oriented Physical Invariant Learning framework (IDOL), which imposes identity-oriented constraints to regulate the feature space under the guidance of prior physical knowledge, thereby dealing distribution variability with physical invariance. Specifically, the proposed IDOL employs the wind field model and dark correlation knowledge of TC to model task-shared and task-specific identity tokens. These tokens capture task dependencies and intrinsic physical invariances of TC, enabling robust estimation of TC wind speed, pressure, inner-core, and outer-core size under distribution shifts. Extensive experiments conducted on multiple datasets and tasks demonstrate the outperformance of the proposed IDOL, verifying that imposing identity-oriented constraints based on prior physical knowledge can effectively mitigates diverse distribution shifts in TC estimation.

ICRA Conference 2025 Conference Paper

OG-Gaussian: Occupancy Based Street Gaussians for Autonomous Driving

• Yedong Shen
• Xinran Zhang
• Yifan Duan
• Shiqi Zhang
• Heng Li
• Yilong Wu
• Jianmin Ji
• Yanyong Zhang

Accurate and realistic 3D scene reconstruction enables the lifelike creation of autonomous driving simulation environments. With advancements in 3D Gaussian Splatting (3DGS), previous studies have applied it to reconstruct complex dynamic driving scenes. These methods typically require expensive LiDAR sensors and pre-annotated datasets of dynamic objects. To address these challenges, we propose OG-Gaussian, a novel approach that replaces LiDAR point clouds with Occupancy Grids (OGs) generated from surround-view camera images using Occupancy Prediction Network (ONet). Our method leverages the semantic information in OGs to separate dynamic vehicles from static street background, converting these grids into two distinct sets of initial point clouds for reconstructing both static and dynamic objects. Additionally, we estimate the trajectories and poses of dynamic objects through a learning-based approach, eliminating the need for complex manual annotations. Experiments on Waymo Open dataset demonstrate that OG-Gaussian is on par with the current state-of-the-art in terms of reconstruction quality and rendering speed, achieving an average PSNR of 35. 13 and a rendering speed of 143 FPS, while significantly reducing computational costs and economic overhead.

AAAI Conference 2025 Conference Paper

TC-Diffuser: Bi-Condition Multi-Modal Diffusion for Tropical Cyclone Forecasting

• Shiqi Zhang
• Pan Mu
• Cheng Huang
• Jinglin Zhang
• Cong Bai

Tropical cyclones (TCs) are complex weather systems with strong winds and heavy rainfall, causing substantial loss of life and property. Therefore, accurate TC forecasting is crucial for the effective prevention of disasters caused by TCs. TC forecasting can be regarded as a spatio-temporal prediction problem. It has been proven that using multi-modal data can effectively introduce atmospheric information to achieve better prediction results and higher interpretability. But it also introduces inevitably introduces noise into the prediction process. The diffusion model's unique noise modeling capability can reduce prediction noise when using multi-modal datasets. However, adapting it to TC forecasting has two main challenges: how to extract valuable information from multi-modal data, and how to utilize them to guide the generation process. For the first challenge, while recent methods can predict multiple TC attributes using multi-modal data, they often overlook the interdependence of multiple attributes and the semantic gap between modalities. Considering the interdependence of attributes, we propose two condition generators that capture the commonalities and characteristics of TC attributes, extracting spatio-temporal and environmental features and incorporating expert knowledge. To reduce the semantic gap between multi-modal data, we introduce the PGSA-LSTM module to map primary and auxiliary modalities. For the second challenge, we propose a novel Bi-condition diffusion model that sequentially processes conditions from the characteristics to commonalities of attributes, thereby expanding the guidance information that the diffusion model can accept. Our results surpass state-of-the-art deep learning models and outperform the numerical weather prediction model used by the China Central Meteorological Observatory. TC-Diffuser shows high generalizability across global ocean areas, strong robustness in handling missing data, and higher computational efficiency.

EAAI Journal 2023 Journal Article

Multi-spatio-temporal Fusion Graph Recurrent Network for Traffic Forecasting

• Wei Zhao
• Shiqi Zhang
• Bing Zhou
• Bei Wang

Accurate traffic forecasting is crucial for smart city development in the new era. However, the intricate spatial and temporal dependencies in traffic data present significant challenges for prediction accuracy. Existing methods often rely on predefined adjacency matrices to capture Spatio-temporal dependencies, which may not adapt well to the dynamic nature of road traffic. To address these challenges, we propose the Multi-Spatio-temporal Fusion Graph Recurrent Network (MSTFGRN). This innovative approach introduces a data-driven method for generating a weighted adjacency matrix, effectively capturing real-time spatial dependencies that are not adequately captured by predefined matrices. The MSTFGRN also incorporates a novel two-way Spatio-temporal fusion operation to learn hidden dependencies between parallel Spatio-temporal relations at different time points. Additionally, a global attention mechanism is integrated into the Spatio-temporal fusion module, enabling the simultaneous capture of global Spatio-temporal dependencies. Through extensive trials on publicly available highway traffic datasets, our method demonstrates state-of-the-art performance compared to alternative baselines.

AAMAS Conference 2022 Conference Paper

Augmented Reality Visualizations using Imitation Learning for Collaborative Warehouse Robots

• Kishan Chandan
• Jack Albertson
• Shiqi Zhang

Augmented reality (AR) technologies have been applied to humanrobot collaboration (HRC) domains to enable people to visualize the state of the robots. Current AR-based visualization strategies are manually designed. This design process requires a lot of human efforts, and domain knowledge. When too little information is visualized, human users find the AR interface not useful; when too much is visualized, they find it difficult to process the visualized information. In this paper, we develop an intelligent AR agent that learns visualization policies (what to visualize, when, and how) from demonstrations. We developed a Unity-based platform for simulating warehouse environments where human-robot teammates work on collaborative delivery tasks. We have collected a dataset that includes 6000 demonstrations of visualizing robots’ current and planned behaviors. Our results from experiments with real human participants show that, compared with competitive baselines from the literature, our learned visualization strategy significantly increases the efficiency of human-robot teams in delivery tasks.

AAAI Conference 2022 Conference Paper

Efficient Dialog Policy Learning by Reasoning with Contextual Knowledge

• Haodi Zhang
• Zhichao Zeng
• Keting Lu
• Kaishun Wu
• Shiqi Zhang

Goal-oriented dialog policy learning algorithms aim to learn a dialog policy for selecting language actions based on the current dialog state. Deep reinforcement learning methods have been used for dialog policy learning. This work is motivated by the observation that, although dialog is a domain with rich contextual knowledge, reinforcement learning methods are ill-equipped to incorporate such knowledge into the dialog policy learning process. In this paper, we develop a deep reinforcement learning framework for goal-oriented dialog policy learning that learns user preferences from user goal data, while leveraging commonsense knowledge from people. The developed framework has been evaluated using a realistic dialog simulation platform. Compared with baselines from the literature and the ablations of our approach, we see significant improvements in learning efficiency and the quality of the computed action policies.

PRL Workshop 2021 Workshop Paper

Guiding Robot Exploration in Reinforcement Learning via Automated Planning: an Abstract

• Yohei Hayamizu
• Saeid Amiri
• Kishan Chandan
• Keiki Takadama
• Shiqi Zhang

AAAI Conference 2020 Conference Paper

Learning and Reasoning for Robot Sequential Decision Making under Uncertainty

• Saeid Amiri
• Mohammad Shokrolah Shirazi
• Shiqi Zhang

Robots frequently face complex tasks that require more than one action, where sequential decision-making (SDM) capabilities become necessary. The key contribution of this work is a robot SDM framework, called LCORPP, that supports the simultaneous capabilities of supervised learning for passive state estimation, automated reasoning with declarative human knowledge, and planning under uncertainty toward achieving long-term goals. In particular, we use a hybrid reasoning paradigm to refine the state estimator, and provide informative priors for the probabilistic planner. In experiments, a mobile robot is tasked with estimating human intentions using their motion trajectories, declarative contextual knowledge, and human-robot interaction (dialog-based and motion-based). Results suggest that, in efficiency and accuracy, our framework performs better than its no-learning and no-reasoning counterparts in office environment.

JAIR Journal 2020 Journal Article

The PETLON Algorithm to Plan Efficiently for Task-Level-Optimal Navigation

• Shih-Yun Lo
• Shiqi Zhang
• Peter Stone

Intelligent mobile robots have recently become able to operate autonomously in large-scale indoor environments for extended periods of time. In this process, mobile robots need the capabilities of both task and motion planning. Task planning in such environments involves sequencing the robot’s high-level goals and subgoals, and typically requires reasoning about the locations of people, rooms, and objects in the environment, and their interactions to achieve a goal. One of the prerequisites for optimal task planning that is often overlooked is having an accurate estimate of the actual distance (or time) a robot needs to navigate from one location to another. State-of-the-art motion planning algorithms, though often computationally complex, are designed exactly for this purpose of finding routes through constrained spaces. In this article, we focus on integrating task and motion planning (TMP) to achieve task-level-optimal planning for robot navigation while maintaining manageable computational efficiency. To this end, we introduce TMP algorithm PETLON (Planning Efficiently for Task-Level-Optimal Navigation), including two configurations with different trade-offs over computational expenses between task and motion planning, for everyday service tasks using a mobile robot. Experiments have been conducted both in simulation and on a mobile robot using object delivery tasks in an indoor office environment. The key observation from the results is that PETLON is more efficient than a baseline approach that pre-computes motion costs of all possible navigation actions, while still producing plans that are optimal at the task level. We provide results with two different task planning paradigms in the implementation of PETLON, and offer TMP practitioners guidelines for the selection of task planners from an engineering perspective.

AAAI Conference 2019 Conference Paper

Goal-Oriented Dialogue Policy Learning from Failures

• Keting Lu
• Shiqi Zhang
• Xiaoping Chen

Reinforcement learning methods have been used for learning dialogue policies. However, learning an effective dialogue policy frequently requires prohibitively many conversations. This is partly because of the sparse rewards in dialogues, and the very few successful dialogues in early learning phase. Hindsight experience replay (HER) enables learning from failures, but the vanilla HER is inapplicable to dialogue learning due to the implicit goals. In this work, we develop two complex HER methods providing different tradeoffs between complexity and performance, and, for the first time, enabled HER-based dialogue policy learning. Experiments using a realistic user simulator show that our HER methods perform better than existing experience replay methods (as applied to deep Q-networks) in learning rate.

JAIR Journal 2019 Journal Article

REBA: A Refinement-Based Architecture for Knowledge Representation and Reasoning in Robotics

• Mohan Sridharan
• Michael Gelfond
• Shiqi Zhang
• Jeremy Wyatt

This article describes REBA, a knowledge representation and reasoning architecture for robots that is based on tightly-coupled transition diagrams of the domain at two different levels of granularity. An action language is extended to support non-boolean fluents and non-deterministic causal laws, and used to describe the domain's transition diagrams, with the fine-resolution transition diagram being defined as a refinement of the coarse-resolution transition diagram. The coarse-resolution system description, and a history that includes prioritized defaults, are translated into an Answer Set Prolog (ASP) program. For any given goal, inference in the ASP program provides a plan of abstract actions. To implement each such abstract action, the robot automatically zooms to the part of the fine-resolution transition diagram relevant to this action. The zoomed fine-resolution system description, and a probabilistic representation of the uncertainty in sensing and actuation, are used to construct a partially observable Markov decision process (POMDP). The policy obtained by solving the POMDP is invoked repeatedly to implement the abstract action as a sequence of concrete actions. The fine-resolution outcomes of executing these concrete actions are used to infer coarse-resolution outcomes that are added to the coarse-resolution history and used for subsequent coarse-resolution reasoning. The architecture thus combines the complementary strengths of declarative programming and probabilistic graphical models to represent and reason with non-monotonic logic-based and probabilistic descriptions of uncertainty and incomplete domain knowledge. In addition, we describe a general methodology for the design of software components of a robot based on these knowledge representation and reasoning tools, and provide a path for proving the correctness of these components. The architecture is evaluated in simulation and on a mobile robot finding and moving target objects to desired locations in indoor domains, to show that the architecture supports reliable and efficient reasoning with violation of defaults, noisy observations and unreliable actions, in complex domains.

IJCAI Conference 2018 Conference Paper

Multi-modal Predicate Identification using Dynamically Learned Robot Controllers

• Saeid Amiri
• Suhua Wei
• Shiqi Zhang
• Jivko Sinapov
• Jesse Thomason
• Peter Stone

Intelligent robots frequently need to explore the objects in their working environments. Modern sensors have enabled robots to learn object properties via perception of multiple modalities. However, object exploration in the real world poses a challenging trade-off between information gains and exploration action costs. Mixed observability Markov decision process (MOMDP) is a framework for planning under uncertainty, while accounting for both fully and partially observable components of the state. Robot perception frequently has to face such mixed observability. This work enables a robot equipped with an arm to dynamically construct query-oriented MOMDPs for multi-modal predicate identification (MPI) of objects. The robot's behavioral policy is learned from two datasets collected using real robots. Our approach enables a robot to explore object properties in a way that is significantly faster while improving accuracies in comparison to existing methods that rely on hand-coded exploration strategies.

AAMAS Conference 2018 Conference Paper

PETLON: Planning Efficiently for Task-Level-Optimal Navigation

• Shih-Yun Lo
• Shiqi Zhang
• Peter Stone

Intelligent mobile robots have recently become able to operate autonomously in large-scale indoor environments for extended periods of time. Task planning in such environments involves sequencing the robot’s high-level goals and subgoals, and typically requires reasoning about the locations of people, rooms, and objects in the environment, and their interactions to achieve a goal. One of the prerequisites for optimal task planning that is often overlooked is having an accurate estimate of the actual distance (or time) a robot needs to navigate from one location to another. State-of-the-art motion planners, though often computationally complex, are designed exactly for this purpose of finding routes through constrained spaces. In this work, we focus on integrating task and motion planning (TMP) to achieve task-level optimal planning for robot navigation while maintaining manageable computational efficiency. To this end, we introduce TMP algorithm PETLON (Planning Efficiently for Task-Level-Optimal Navigation) for everyday service tasks using a mobile robot. PETLON is more efficient than planning approaches that pre-compute motion costs of all possible navigation actions, while still producing plans that are optimal at the task level.

AAAI Conference 2017 Conference Paper

Dynamically Constructed (PO)MDPs for Adaptive Robot Planning

• Shiqi Zhang
• Piyush Khandelwal
• Peter Stone

To operate in human-robot coexisting environments, intelligent robots need to simultaneously reason with commonsense knowledge and plan under uncertainty. Markov decision processes (MDPs) and partially observable MDPs (POMDPs), are good at planning under uncertainty toward maximizing long-term rewards; P-LOG, a declarative programming language under Answer Set semantics, is strong in commonsense reasoning. In this paper, we present a novel algorithm called iCORPP to dynamically reason about, and construct (PO)MDPs using P-LOG. iCORPP successfully shields exogenous domain attributes from (PO)MDPs, which limits computational complexity and enables (PO)MDPs to adapt to the value changes these attributes produce. We conduct a number of experimental trials using two example problems in simulation and demonstrate iCORPP on a real robot. Results show significant improvements compared to competitive baselines.

AAMAS Conference 2017 Conference Paper

Multirobot Symbolic Planning under Temporal Uncertainty

• Shiqi Zhang
• Yuqian Jiang
• Guni Sharon
• Peter Stone

Multirobot symbolic planning (MSP) aims at computing plans, each in the form of a sequence of actions, for a team of robots to achieve their individual goals while minimizing overall cost. Solving MSP problems requires modeling limited domain resources (e. g. , corridors that allow at most one robot at a time) and the possibility of action synergy (e. g. , multiple robots going through a door after a single door-opening action). However, the temporal uncertainty that propagates over actions, such as delays caused by obstacles in navigation actions, makes it challenging to plan for resource sharing and realizing synergy in a team of robots. This paper, for the first time, introduces the problem of MSP under temporal uncertainty (MSPTU). We present a novel, iterative inter-dependent planning (IIDP) algorithm, including two configurations (simple and enhanced), for solving general MSPTU problems. We then focus on multirobot navigation tasks, presenting a full instantiation of IIDP that includes a new algorithm for computing conditional plan cost under temporal uncertainty and a novel shifted-Poisson distribution for accumulating temporal uncertainty over actions. The algorithms have been implemented both in simulation and on real robots. We observed a significant reduction in overall cost compared to baselines in which robots do not communicate or model temporal uncertainty. CCS Concepts •Computing methodologies → Robotic planning; Multi-agent planning; Planning under uncertainty;

IJCAI Conference 2016 Conference Paper

Robot Scavenger Hunt: A Standardized Framework for Evaluating Intelligent Mobile Robots

• Shiqi Zhang
• Dongcai Lu
• Xiaoping Chen
• Peter Stone

In recent years, many different types of intelligent mobile robots have been developed in research and industrial labs. Although there are significant differences in both hardware and software over these robots, many of them share a common set of AI capabilities, e. g. , planning, learning, vision and natural language processing. At the same time, almost all of them are equipped with traditional robotic capabilities such as mapping, localization, and navigation. However, to date it has been difficult to compare and contrast their capabilities in any controlled way. The main goal of the Robot Scavenger Hunt is to provide a standardized framework that includes a set of standardized tasks for evaluating the AI and robotic capabilities of medium-sized intelligent mobile robots. Compared to existing benchmarks, e. g. , RoboCup@Home, Robot Scavenger Hunt aims at evaluations in larger spaces (multi-floor buildings vs. rooms) over longer periods of time (hours vs. minutes) while interacting with real human residents.

AAAI Conference 2015 Conference Paper

CORPP: Commonsense Reasoning and Probabilistic Planning, as Applied to Dialog with a Mobile Robot

• Shiqi Zhang
• Peter Stone

In order to be fully robust and responsive to a dynamically changing real-world environment, intelligent robots will need to engage in a variety of simultaneous reasoning modalities. In particular, in this paper we consider their needs to i) reason with commonsense knowledge, ii) model their nondeterministic action outcomes and partial observability, and iii) plan toward maximizing long-term rewards. On one hand, Answer Set Programming (ASP) is good at representing and reasoning with commonsense and default knowledge, but is ill-equipped to plan under probabilistic uncertainty. On the other hand, Partially Observable Markov Decision Processes (POMDPs) are strong at planning under uncertainty toward maximizing long-term rewards, but are not designed to incorporate commonsense knowledge and inference. This paper introduces the CORPP algorithm which combines Plog, a probabilistic extension of ASP, with POMDPs to integrate commonsense reasoning with planning under uncertainty. Our approach is fully implemented and tested on a shopping request identification problem both in simulation and on a real robot. Compared with existing approaches using P-log or POMDPs individually, we observe significant improvements in both efficiency and accuracy.

IJCAI Conference 2015 Conference Paper

Learning to Interpret Natural Language Commands through Human-Robot Dialog

• Jesse Thomason
• Shiqi Zhang
• Raymond J Mooney
• Peter Stone

Intelligent robots frequently need to understand requests from naive users through natural language. Previous approaches either cannot account for language variation, e. g. , keyword search, or require gathering large annotated corpora, which can be expensive and cannot adapt to new variation. We introduce a dialog agent for mobile robots that understands human instructions through semantic parsing, actively resolves ambiguities using a dialog manager, and incrementally learns from humanrobot conversations by inducing training data from user paraphrases. Our dialog agent is implemented and tested both on a web interface with hundreds of users via Mechanical Turk and on a mobile robot over several days, tasked with understanding navigation and delivery requests through natural language in an office environment. In both contexts, We observe significant improvements in user satisfaction after learning from conversations.

AAMAS Conference 2012 Conference Paper

Active Visual Sensing and Collaboration on Mobile Robots using Hierarchical POMDPs

• Shiqi Zhang
• Mohan Sridharan

A key challenge to widespread deployment of mobile robots in the real-world is the ability to robustly and autonomously sense the environment and collaborate with teammates. Real-world domains are characterized by partial observability, non-deterministic action outcomes and unforeseen changes, making autonomous sensing and collaboration a formidable challenge. This paper poses vision-based sensing, information processing and collaboration as an instance of probabilistic planning using partially observable Markov decision processes. Reliable, efficient and autonomous operation is achieved using a hierarchical decomposition that includes: (a) convolutional policies to exploit the local symmetry of high-level visual search; (b) adaptive observation functions, policy re-weighting, automatic belief propagation and online updates of the domain map for autonomous adaptation to domain changes; and (c) a probabilistic strategy for a team of robots to robustly share beliefs. All algorithms are evaluated in simulation and on physical robots localizing target objects in dynamic indoor domains.

AAAI Conference 2012 Conference Paper

Combining Probabilistic Planning and Logic Programming on Mobile Robots

• Shiqi Zhang
• Forrest Bao
• Mohan Sridharan