GenAI4ELab/papercli-papers huggingface dataset:
Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner
Authors: Aizierjiang Aiersilan
Venue: AAAI 2025 (main)

Abstract: Motion planning is a crucial component in autonomous driving. State-of-the-art motion planners are trained on meticulously curated datasets, which are not only expensive to annotate but also insufficient in capturing rarely seen critical scenarios. Failing to account for such scenarios poses a significant risk to motion planners and may lead to incidents during testing. An intuitive solution is to manually compose such scenarios by programming and executing a simulator (e.g., CARLA). However, this approach incurs substantial human costs. Motivated by this, we propose an inexpensive method for generating diverse critical traffic scenarios to train more robust motion planners. First, we represent traffic scenarios as scripts, which are then used by the simulator to generate traffic scenarios. Next, we develop a method that accepts user-specified text descriptions, which a Large Language Model translates into scripts using in-context learning.
The output scripts are sent to the simulator that produces the corresponding traffic scenarios. As our method can generate abundant safety-critical traffic scenarios, we use them as synthetic training data for motion planners. To demonstrate the value of generated scenarios, we train existing motion planners on our synthetic data, real-world datasets, and a combination of both.
Our experiments show that motion planners trained with our data significantly outperform those trained solely on real-world data, showing the usefulness of our synthetic data and the effectiveness of our data generation method.
MRBTP: Efficient Multi-Robot Behavior Tree Planning and Collaboration
Authors: Yishuai Cai; Xinglin Chen; Zhongxuan Cai; Yunxin Mao; Minglong Li; Wenjing Yang; Ji Wang
Venue: AAAI 2025 (main)

Abstract: Multi-robot task planning and collaboration are critical challenges in robotics. While Behavior Trees (BTs) have been established as a popular control architecture and are plannable for a single robot, the development of effective multi-robot BT planning algorithms remains challenging due to the complexity of coordinating diverse action spaces. We propose the Multi-Robot Behavior Tree Planning (MRBTP) algorithm, with theoretical guarantees of both soundness and completeness. MRBTP features cross-tree expansion to coordinate heterogeneous actions across different BTs to achieve the team's goal. For homogeneous actions, we retain backup structures among BTs to ensure robustness and prevent redundant execution through intention sharing. While MRBTP is capable of generating BTs for both homogeneous and heterogeneous robot teams, its efficiency can be further improved. We then propose an optional plugin for MRBTP when Large Language Models (LLMs) are available to reason goal-related actions for each robot. These relevant actions can be pre-planned to form long-horizon subtrees, significantly enhancing the planning speed and collaboration efficiency of MRBTP. We evaluate our algorithm in warehouse management and everyday service scenarios. Results demonstrate MRBTP's robustness and execution efficiency under varying settings, as well as the ability of the pre-trained LLM to generate effective task-specific subtrees for MRBTP.
Int2Planner: An Intention-based Multi-modal Motion Planner for Integrated Prediction and Planning
Authors: Xiaolei Chen; Junchi Yan; Wenlong Liao; Tao He; Pai Peng
Venue: AAAI 2025 (main)

Abstract: Motion planning is a critical module in autonomous driving, with the primary challenge of uncertainty caused by interactions with other participants. As most previous methods treat prediction and planning as separate tasks, it is difficult to model these interactions. Furthermore, since the route path navigates ego vehicles to a predefined destination, it provides relatively stable intentions for ego vehicles and helps constrain uncertainty. On this basis, we construct Int2Planner, an Intention-based Integrated motion Planner achieves multi-modal planning and prediction. Instead of static intention points, Int2Planner utilizes route intention points for ego vehicles and generates corresponding planning trajectories for each intention point to facilitate multi-modal planning. The experiments on the private dataset and the public nuPlan benchmark show the effectiveness of route intention points, and Int2Planner achieves state-of-the-art performance. We also deploy it in real-world vehicles and have conducted autonomous driving for hundreds of kilometers in urban areas. It further verifies that Int2Planner can continuously interact with the traffic environment.
Optimal Control Operator Perspective and a Neural Adaptive Spectral Method
Authors: Mingquan Feng; Zhijie Chen; Yixin Huang; Yizhou Liu; Junchi Yan
Venue: AAAI 2025 (main)

Abstract: Optimal control problems (OCPs) involve finding a control function for a dynamical system such that a cost functional is optimized. It is central to physical systems in both academia and industry. In this paper, we propose a novel instance-solution control operator perspective, which solves OCPs in a one-shot manner without direct dependence on the explicit expression of dynamics or iterative optimization processes. The control operator is implemented by a new neural operator architecture named Neural Adaptive Spectral Method (NASM), a generalization of classical spectral methods. We theoretically validate the perspective and architecture by presenting the approximation error bounds of NASM for the control operator.  Experiments on synthetic environments and a real-world dataset verify the effectiveness and efficiency of our approach, including substantial speedup in running time, and high-quality in- and out-of-distribution generalization.
Efficient Language-instructed Skill Acquisition via Reward-Policy Co-Evolution
Authors: Changxin Huang; Yanbin Chang; Junfan Lin; Junyang Liang; Runhao Zeng; Jianqiang Li
Venue: AAAI 2025 (main)

Abstract: The ability to autonomously explore and resolve tasks with minimal human guidance is crucial for the self-development of embodied intelligence. 
Although reinforcement learning methods can largely ease human effort, it's challenging to design reward functions for real-world tasks, especially for high-dimensional robotic control, due to complex relationships among joints and tasks. Recent advancements large language models (LLMs) enable automatic reward function design. 
However, approaches evaluate reward functions by re-training policies from scratch placing an undue burden on the reward function, expecting it to be effective throughout the whole policy improvement process. 
We argue for a more practical strategy in robotic autonomy, focusing on refining existing policies with policy-dependent reward functions rather than a universal one.
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 at each round of evolution. 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.
GNN-Transformer Task Planning Enhanced with Semantic-Driven Data Augmentation
Authors: Soojin Jeong; Seongwan Byeon; Sangwoo Kim; HyeokJun Kwon; Yoonseon Oh
Venue: AAAI 2025 (main)

Abstract: Natural language is the most intuitive means for humans to interact with robots, making task planning based on natural language commands a longstanding area of research. Large language models (LLMs) have significantly improved task planning by enhancing understanding of language and common sense. However, current methods still face several challenges: they lack a deep understanding of physical environments, their performance relies heavily on prompt examples, LLMs are oversized and not customized for specific tasks, and the planning costs remain high. 
To overcome these issues, we introduce the GNN-Transformer Task Planner (GTTP), designed to predict task-level actions by leveraging the semantic environment and incorporating historical state data. The GTTP architecture is scalable through the use of GNN layers, while transformer layers facilitate understanding task progression. In addition, our model uses a text encoder to embed environments, allowing it to be trained on simulated datasets and applied directly in real-world scenarios. We also propose an automated data generation method that includes semantic augmentation, planning verification, and instruction generation via LLM. This method enables the collection of 14k instruction-annotated tasks in the VirtualHome environment with minimal human effort. The model has been validated across diverse scenes containing up to 715 objects, achieving significantly higher success rates compared to baseline models. It has also been successfully deployed on a physical mobile manipulator, demonstrating its practical applicability and effectiveness.
Multi-fingered Hand Grasps with Visuo-Tactile Fusion via Multi-Agent Deep Reinforcement Learning
Authors: Peida Jia; Xuanheng Li; Tianqiang Zhu; Rina Wu; Xiangbo Lin; Yi Sun
Venue: AAAI 2025 (main)

Abstract: Humans achieve contact-rich dexterous grasping through the synergy of visual and tactile information. However, the high-dimensional action space of high DoF multi-fingered hands poses significant challenges to this operation. In this study, we address this complexity by controlling the robotic hand at the reduced dimensional level of individual fingers instead of the entire hand, and develop a finger-based multi-agent deep reinforcement learning strategy by regarding the wrist, arm, and each finger of the hand as intelligent agents. We commence by applying a single-agent reinforcement learning algorithm to guide the whole hand to reach the feasible approaching direction and distance to the object. Then, we develop neuroscience-inspired visuo-tactile fusion networks to train multiple agents to control their assigned fingers by effectively leveraging visual and tactile feedback. This enables dynamic and collaborative adjustments of finger-object interactions, ultimately achieving precise contact with specific areas of the objects. The grasping results on 8 objects show that our approach can achieve stable and compliant grasps. To the best of our knowledge, this is the first work that employs a finger-based multi-agent reinforcement learning approach to control the dexterous grasping process under the guidance of both visual and tactile feedback.
State-Based Disassembly Planning
Authors: Chao Lei; Nir Lipovetzky; Krista A. Ehinger
Venue: AAAI 2025 (main)

Abstract: It has been shown recently that physics-based simulation significantly enhances the disassembly capabilities of real-world assemblies with diverse 3D shapes and stringent motion constraints. However, the efficiency suffers when tackling intricate disassembly tasks that require numerous simulations and increased simulation time. In this work, we propose a State-Based Disassembly Planning (SBDP) approach, prioritizing physics-based simulation with translational motion over rotational motion to facilitate autonomy, reducing dependency on human input, while storing intermediate motion states to improve search scalability. We introduce two novel evaluation functions derived from new Directional Blocking Graphs (DBGs) enriched with state information to scale up the search.  Our experiments show that SBDP with new evaluation functions and DBGs constraints outperforms the state-of-the-art in disassembly planning in terms of success rate and computational efficiency over benchmark datasets consisting of thousands of physically valid industrial assemblies.
FloNa: Floor Plan Guided Embodied Visual Navigation
Authors: Jiaxin Li; Weiqi Huang; Zan Wang; Wei Liang; Huijun Di; Feng Liu
Venue: AAAI 2025 (main)

Abstract: 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.
Safe Planner: Empowering Safety Awareness in Large Pre-Trained Models for Robot Task Planning
Authors: Siyuan Li; Feifan Liu; Lingfei Cui; Jiani Lu; Qinqin Xiao; Xirui Yang; Peng Liu; Kewu Sun; Zhe Ma; Xun Wang
Venue: AAAI 2025 (main)

Abstract: Robot task planning is an important problem for autonomous robots in long-horizon challenging tasks.
As large pre-trained models have demonstrated superior planning ability, recent research investigates utilizing large models to achieve autonomous planning for robots in diverse tasks. 
However, since the large models are pre-trained with Internet data and lack the knowledge of real task scenes, large models as planners may make unsafe decisions that hurt the robots and the surrounding environments. 
To solve this challenge, we propose a novel Safe Planner framework, which empowers safety awareness in large pre-trained models to accomplish safe and executable planning. 
In this framework, we develop a safety prediction module to guide the high-level large model planner, and this safety module trained in a simulator can be effectively transferred to real-world tasks. 
The proposed Safe Planner framework is evaluated on both simulated environments and real robots. The experiment results demonstrate that Safe Planner not only achieves state-of-the-art task success rates, but also substantially improves safety during task execution.
ConDo: Continual Domain Expansion for Absolute Pose Regression
Authors: Zijun Li; Zhipeng Cai; Bochun Yang; Xuelun Shen; Siqi Shen; Xiaoliang Fan; Michael Paulitsch; Cheng Wang
Venue: AAAI 2025 (main)

Abstract: Visual localization is a fundamental machine learning problem. Absolute Pose Regression (APR) trains a scene-dependent model to efficiently map an input image to the camera pose in a pre-defined scene. However, many applications have continually changing environments, where inference data at novel poses or scene conditions (weather, geometry) appear after deployment. Training APR on a fixed dataset leads to overfitting, making it fail catastrophically on challenging novel data. This work proposes Continual Domain Expansion (ConDo), which continually collects unlabeled inference data to update the deployed APR. Instead of applying standard unsupervised domain adaptation methods which are ineffective for APR, ConDo effectively learns from unlabeled data by distilling knowledge from scene-agnostic localization methods. By sampling data uniformly from historical and newly collected data, ConDo can effectively expand the generalization domain of APR. Large-scale benchmarks with various scene types are constructed to evaluate models under practical (long-term) data changes. ConDo consistently and significantly outperforms baselines across architectures, scene types, and data changes. On challenging scenes (Fig.1), it reduces the localization error by >7x (14.8m vs 1.7m). Analysis shows the robustness of ConDo against compute budgets, replay buffer sizes and teacher prediction noise. Comparing to model re-training, ConDo achieves similar performance up to 25x faster.
When Should We Prefer State-to-Visual DAgger over Visual Reinforcement Learning?
Authors: Tongzhou Mu; Zhaoyang Li; Stanisław Wiktor Strzelecki; Xiu Yuan; Yunchao Yao; Litian Liang; Hao Su
Venue: AAAI 2025 (main)

Abstract: Learning policies from high-dimensional visual inputs, such as pixels and point clouds, is crucial in various applications. Visual reinforcement learning is a promising approach that directly trains policies from visual observations, although it faces challenges in sample efficiency and computational costs. This study conducts an empirical comparison of State-to-Visual DAgger — a two-stage framework that initially trains a state policy before adopting online imitation to learn a visual policy — and Visual RL across a diverse set of tasks. We evaluate both methods across 16 tasks from three benchmarks, focusing on their asymptotic performance, sample efficiency, and computational costs. Surprisingly, our findings reveal that State-to-Visual DAgger does not universally outperform Visual RL but shows significant advantages in challenging tasks, offering more consistent performance. In contrast, its benefits in sample efficiency are less pronounced, although it often reduces the overall wall-clock time required for training. Based on our findings, we provide recommendations for practitioners and hope that our results contribute valuable perspectives for future research in visual policy learning.
Heterogeneous Multi-Robot Graph Coverage with Proximity and Movement Constraints
Authors: Dolev Mutzari; Yonatan Aumann; Sarit Kraus
Venue: AAAI 2025 (main)

Abstract: Multi-Robot Coverage problems have been extensively studied in robotics, planning and multi-agent systems. In this work, we consider the coverage problem when there are constraints on the proximity (e.g., maximum distance between the agents, or a blue agent must be adjacent to a red agent) and the movement (e.g., terrain traversability and material load capacity) of the robots.
Such constraints naturally arise in many real-world applications, e.g. in search-and-rescue and maintenance operations. Given such a setting, the goal is to compute a covering tour of the graph with a minimum number of steps, and that adheres to the proximity and movement constraints.   
For this problem, our contributions are four: (i) a formal formulation of the problem, (ii) an exact algorithm that is FPT in parameters ||F||, d and ω - the set of robot formations that encode the proximity constraints, the maximum nodes degree, and the tree-width of the graph, respectively, (iii) for the case that the graph is a tree: a PTAS approximation scheme, that given an ε produces a tour that is within a 1+ ε⋅error(||F||, d)) of the optimal one, and the computation runs in time poly(n) ⋅ h(1/ε, ||F||).
(iv) for the case that the graph is a tree, with k=3 robots, and the constraint is that all agents are connected: a PTAS scheme with multiplicative approximation error of 1 + O(ε), independent of d.
Evolutionary Reinforcement Learning with Parameterized Action Primitives for Diverse Manipulation Tasks
Authors: Xianxu Qiu; Haiming Huang; Weiwei Chen; Qiuzhen Lin; Wei-Neng Chen; Fuchun Sun
Venue: AAAI 2025 (main)

Abstract: Reinforcement learning (RL) has shown promising performance in tackling robotic manipulation tasks (RMTs), which require learning a prolonged sequence of manipulation actions to control robots efficiently. However, most RL algorithms often suffer from two problems when solving RMTs: inefficient exploration due to the extremely large action space and catastrophic forgetting due to the poor sampling efficiency. To alleviate these problems, this paper introduces an Evolutionary Reinforcement Learning algorithm with parameterized Action Primitives, called ERLAP, which combines the advantages of an evolutionary algorithm (EA) and hierarchical RL (HRL) to solve diverse RMTs. A library of heterogeneous action primitives is constructed in HRL to enhance the exploration efficiency of robots and dual populations with new evolutionary operators are run in EA to optimize these primitive sequences, which can  diversify the distribution of replay buffer and avoid catastrophic forgetting. The experiments show that ERLAP outperforms four state-of-the-art RL algorithms in simulated RMTs with dense rewards and can effectively avoid catastrophic forgetting in a set of more challenging simulated RMTs with sparse rewards.
Enhancing Multi-Robot Semantic Navigation Through Multimodal Chain-of-Thought Score Collaboration
Authors: Zhixuan Shen; Haonan Luo; Kexun Chen; Fengmao Lv; Tianrui Li
Venue: AAAI 2025 (main)

Abstract: Understanding how humans cooperatively utilize semantic knowledge to explore unfamiliar environments and decide on navigation directions is critical for house service multi-robot systems. Previous methods primarily focused on single-robot centralized planning strategies, which severely limited exploration efficiency. Recent research has considered decentralized planning strategies for multiple robots, assigning separate planning models to each robot, but these approaches often overlook communication costs. In this work, we propose Multimodal Chain-of-Thought Co-Navigation (MCoCoNav), a modular approach that utilizes multimodal Chain-of-Thought to plan collaborative semantic navigation for multiple robots. MCoCoNav combines visual perception with Vision Language Models (VLMs) to evaluate exploration value through probabilistic scoring, thus reducing time costs and achieving stable outputs. Additionally, a global semantic map is used as a communication bridge, minimizing communication overhead while integrating observational results. Guided by scores that reflect exploration trends, robots utilize this map to assess whether to explore new frontier points or revisit history nodes. Experiments on HM3D_v0.2 and MP3D demonstrate the effectiveness of our approach.