从真实世界观测中估计仿真准备的场景对于后续的规划和策略学习任务至关重要。遗憾的是，现有方法在杂乱环境中表现不佳，往往存在计算成本高、鲁棒性差和通用性受限的问题。我们提出了一种统一的优化方法，用于真实到仿真场景估计，通过物理约束联合恢复多个刚体物体的形状和姿态。我们的方法基于两个关键技术创新：首先，我们利用最近引入的形状可微接触模型，其全局可微性允许在建模物体间接触时对物体几何和姿态进行联合优化；其次，我们利用增强拉格朗日Hessian的结构稀疏性，推导出一个高效的线性系统求解器，其计算成本与场景复杂度呈有利关系。基于此方法，我们开发了端到端的Simulation-ready Physics-Aware Reconstruction for Cluttered Scenes (SPARCS) 管道，整合了基于学习的对象初始化、物理约束的联合形状-姿态优化和可微纹理细化。在最多包含5个物体和22个凸包的杂乱场景实验中，我们的方法能够稳健地重建出物理有效且仿真准备的物体形状和姿态。项目网页：https://rory-weicheng.github.io/SPARCS/.
英文摘要：
Estimating simulation-ready scenes from real-world observations is crucial for downstream planning and policy learning tasks. Regretfully, existing methods struggle in cluttered environments, often exhibiting prohibitive computational cost, poor robustness, and restricted generality when scaling to multiple interacting objects. We propose a unified optimization-based formulation for real-to-sim scene estimation that jointly recovers the shapes and poses of multiple rigid objects under physical constraints. Our method is built on two key technical innovations. First, we leverage the recently introduced shape-differentiable contact model, whose global differentiability permits joint optimization over object geometry and pose while modeling inter-object contacts. Second, we exploit the structured sparsity of the augmented Lagrangian Hessian to derive an efficient linear system solver whose computational cost scales favorably with scene complexity. Building on this formulation, we develop an end-to-end Simulation-ready Physics-Aware Reconstruction for Cluttered Scenes (SPARCS) pipeline, which integrates learning-based object initialization, physics-constrained joint shape-pose optimization, and differentiable texture refinement. Experiments on cluttered scenes with up to 5 objects and 22 convex hulls demonstrate that our approach robustly reconstructs physically valid, simulation-ready object shapes and poses. Project webpage: https://rory-weicheng.github.io/SPARCS/.
Tsinghua University（清华大学）；University of California, San Diego（加利福尼亚大学圣迭戈分校）
Grasping is among the most fundamental and long-lasting problems in robotics study. This paper studies the problem of 6-DoF(degree of freedom) grasping by a parallel gripper in a cluttered scene captured using a commodity depth sensor from a single viewpoint. We address the problem in a learning-based framework. At the high level, we rely on a single-shot grasp proposal network, trained with synthetic data and tested in real-world scenarios. Our single-shot neural network architecture can predict amodal grasp proposal efficiently and effectively. Our training data synthesis pipeline can genera…
We introduce a novel method to teach a robotic agent to interactively explore cluttered yet structured scenes, such as kitchen pantries and grocery shelves, by leveraging the physical plausibility of the scene. We propose a novel learning framework to train an effective scene exploration policy to discover hidden objects with minimal interactions. First, we define a novel scene grammar to represent structured clutter. Then we train a Graph Neural Network (GNN) based Scene Generation agent using deep reinforcement learning (deep RL), to manipulate this Scene Grammar to create a diverse set of s…
Massachusetts Institute of Technology（麻省理工学院）；University of Washington（华盛顿大学）；NVIDIA（英伟达）；University of Texas at Dallas（德克萨斯大学达拉斯分校）
6D grasping in cluttered scenes is a longstanding problem in robotic manipulation. Open-loop manipulation pipelines may fail due to inaccurate state estimation, while most end-to-end grasping methods have not yet scaled to complex scenes with obstacles. In this work, we propose a new method for end-to-end learning of 6D grasping in cluttered scenes. Our hierarchical framework learns collision-free target-driven grasping based on partial point cloud observations. We learn an embedding space to encode expert grasping plans during training and a variational autoencoder to sample diverse grasping …
NVIDIA（英伟达）；German Aerospace Center (DLR)（德国航空航天中心）；Technical University of Munich (TUM)（慕尼黑工业大学）；University of Washington（华盛顿大学）
Grasping unseen objects in unconstrained, cluttered environments is an essential skill for autonomous robotic manipulation. Despite recent progress in full 6-DoF grasp learning, existing approaches often consist of complex sequential pipelines that possess several potential failure points and run-times unsuitable for closed-loop grasping. Therefore, we propose an end-to-end network that efficiently generates a distribution of 6-DoF parallel-jaw grasps directly from a depth recording of a scene. Our novel grasp representation treats 3D points of the recorded point cloud as potential grasp conta…
We present the Semantic Robot Programming (SRP) paradigm as a convergence of robot programming by demonstration and semantic mapping. In SRP, a user can directly program a robot manipulator by demonstrating a snapshot of their intended goal scene in workspace. The robot then parses this goal as a scene graph comprised of object poses and inter-object relations, assuming known object geometries. Task and motion planning is then used to realize the user's goal from an arbitrary initial scene configuration. Even when faced with different initial scene configurations, SRP enables the robot to seam…
Clutt3R-Seg: Sparse-view 3D Instance Segmentation for Language-grounded Grasping in Cluttered Scenes
Dept. of Mechanical Engineering, SNU（机械工程系，首尔国立大学）；Interdisciplinary Program in Artificial Intelligence, SNU（人工智能跨学科项目，首尔国立大学）；Robotics Lab, Hyundai Motor Company（Hyundai Motor Company机器人实验室）
Computer Science and Artificial Intelligence Laboratory（计算机科学与人工智能实验室）；Massachusetts Institute of Technology（麻省理工学院）
Deep neural network (DNN) architectures have been shown to outperform traditional pipelines for object segmentation and pose estimation using RGBD data, but the performance of these DNN pipelines is directly tied to how representative the training data is of the true data. Hence a key requirement for employing these methods in practice is to have a large set of labeled data for your specific robotic manipulation task, a requirement that is not generally satisfied by existing datasets. In this paper we develop a pipeline to rapidly generate high quality RGBD data with pixelwise labels and objec…
Fraunhofer Institute for Manufacturing Engineering and Automation IPA（弗劳恩霍夫制造工程与自动化研究所（IPA））
In this paper, we introduce a novel learning-based approach for grasping known rigid objects in highly cluttered scenes and precisely placing them based on depth images. Our Placement Quality Network (PQ-Net) estimates the object pose and the quality for each automatically generated grasp pose for multiple objects simultaneously at 92 fps in a single forward pass of a neural network. All grasping and placement trials are executed in a physics simulation and the gained experience is transferred to the real world using domain randomization. We demonstrate that our policy successfully transfers t…
Segmenting unseen object instances in cluttered environments is an important capability that robots need when functioning in unstructured environments. While previous methods have exhibited promising results, they still tend to provide incorrect results in highly cluttered scenes. We postulate that a network architecture that encodes relations between objects at a high-level can be beneficial. Thus, in this work, we propose a novel framework that refines the output of such methods by utilizing a graph-based representation of instance masks. We train deep networks capable of sampling smart pert…
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences（中国科学院脑科学与智能技术卓越创新中心）
Object grasping in cluttered scenes is a widely investigated field of robot manipulation. Most of the current works focus on estimating grasp pose from point clouds based on an efficient single-shot grasp detection network. However, due to the lack of geometry awareness of the local grasping area, it may cause severe collisions and unstable grasp configurations. In this paper, we propose a two-stage grasp pose refinement network which detects grasps globally while fine-tuning low-quality grasps and filtering noisy grasps locally. Furthermore, we extend the 6-DoF grasp with an extra dimension a…
ClutterDexGrasp: A Sim-to-Real System for General Dexterous Grasping in Cluttered Scenes
CFCS, School of Computer Science, Peking University（计算机学院，北京大学）；PKU-AgiBot Lab（北京大学阿吉机器人实验室）；PKU-PsiBot Lab（北京大学Psi机器人实验室）；Princeton University（普林斯顿大学）
FetchBot: Learning Generalizable Object Fetching in Cluttered Scenes via Zero-Shot Sim2Real
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；CFCS, School of Computer Science, Peking University（计算机科学系，北京大学）；Galbot；Beijing Academy of Artificial Intelligence（北京人工智能研究院）
In this paper, we focus on the problem of feature learning in the presence of scale imbalance for 6-DoF grasp detection and propose a novel approach to especially address the difficulty in dealing with small-scale samples. A Multi-scale Cylinder Grouping (MsCG) module is presented to enhance local geometry representation by combining multi-scale cylinder features and global context. Moreover, a Scale Balanced Learning (SBL) loss and an Object Balanced Sampling (OBS) strategy are designed, where SBL enlarges the gradients of the samples whose scales are in low frequency by apriori weights while…
GraspClutter6D: A Large-scale Real-world Dataset for Robust Perception and Grasping in Cluttered Scenes
Korea Institute of Machinery & Materials (KIMM)（韩国机械材料研究院）；Gwangju Institute of Science and Technology (GIST)（全州科学技术院）；Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）
Collision-Free Humanoid Traversal in Cluttered Indoor Scenes
Tsinghua University（清华大学）；Tongji University（同济大学）；Galbot；Shanghai Qi Zhi Institute（上海启智研究院）；South China University of Technology（华南理工大学）；Peking University（北京大学）；Zhejiang University（浙江大学）
Emerging Extrinsic Dexterity in Cluttered Scenes via Dynamics-aware Policy Learning
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）；Galbot；Peking University（北京大学）；Shanghai Jiao Tong University（上海交通大学）
NVIDIA Research（英伟达研究院）；University of Washington（华盛顿大学）；Carnegie Mellon University（卡内基梅隆大学）
Estimating the 6D pose of known objects is important for robots to interact with the real world. The problem is challenging due to the variety of objects as well as the complexity of a scene caused by clutter and occlusions between objects. In this work, we introduce PoseCNN, a new Convolutional Neural Network for 6D object pose estimation. PoseCNN estimates the 3D translation of an object by localizing its center in the image and predicting its distance from the camera. The 3D rotation of the object is estimated by regressing to a quaternion representation. We also introduce a novel loss func…
Pushing objects through cluttered scenes is a challenging task, especially when the objects to be pushed have initially unknown dynamics and touching other entities has to be avoided to reduce the risk of damage. In this paper, we approach this problem by applying deep reinforcement learning to generate pushing actions for a robotic manipulator acting on a planar surface where objects have to be pushed to goal locations while avoiding other items in the same workspace. With the latent space learned from a depth image of the scene and other observations of the environment, such as contact infor…
Object search -- the problem of finding a target object in a cluttered scene -- is essential to solve for many robotics applications in warehouse and household environments. However, cluttered environments entail that objects often occlude one another, making it difficult to segment objects and infer their shapes and properties. Instead of relying on the availability of CAD or other explicit models of scene objects, we augment a manipulation planner for cluttered environments with a state-of-the-art deep neural network for shape completion as well as a volumetric memory system, allowing the ro…
CADGrasp: Learning Contact and Collision Aware General Dexterous Grasping in Cluttered Scenes
Center on Frontiers of Computing Studies, School of Computer Science, Peking University（前沿计算研究中心，计算机学院，北京大学）；National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University（多媒体信息处理国家级重点实验室，计算机学院，北京大学）
Object finding in clutter is a skill that requires perception of the environment and in many cases physical interaction. In robotics, interactive perception defines a set of algorithms that leverage actions to improve the perception of the environment, and vice versa use perception to guide the next action. Scene interactions are difficult to model, therefore, most of the current systems use predefined heuristics. This limits their ability to efficiently search for the target object in a complex environment. In order to remove heuristics and the need for explicit models of the interactions, in…
Robust Dynamic Object Detection in Cluttered Indoor Scenes via Learned Spatiotemporal Cues
Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente（代尔夫特理工大学电子工程、数学与计算机科学学院）；Department of Electrical Engineering, Wright State University（怀特塞德大学电子工程系）；Massachusetts Institute of Technology（麻省理工学院）
University of Maryland（马里兰大学）；Amazon Lab126（亚马逊Lab126）
We present an algorithm for safe robot navigation in complex dynamic environments using a variant of model predictive equilibrium point control. We use an optimization formulation to navigate robots gracefully in dynamic environments by optimizing over a trajectory cost function at each timestep. We present a novel trajectory cost formulation that significantly reduces the conservative and deadlock behaviors and generates smooth trajectories. In particular, we propose a new collision probability function that effectively captures the risk associated with a given configuration and the time to a…
Active sensing and planning in unknown, cluttered environments is an open challenge for robots intending to provide home service, search and rescue, narrow-passage inspection, and medical assistance. Although many active sensing methods exist, they often consider open spaces, assume known settings, or mostly do not generalize to real-world scenarios. We present the active neural sensing approach that generates the kinematically feasible viewpoint sequences for the robot manipulator with an in-hand camera to gather the minimum number of observations needed to reconstruct the underlying environm…
Tsinghua University（清华大学）；Shenzhen Academy of Robotics（深圳机器人研究院）
In this paper, a novel robotic grasping system is established to automatically pick up objects in cluttered scenes. A composite robotic hand composed of a suction cup and a gripper is designed for grasping the object stably. The suction cup is used for lifting the object from the clutter first and the gripper for grasping the object accordingly. We utilize the affordance map to provide pixel-wise lifting point candidates for the suction cup. To obtain a good affordance map, the active exploration mechanism is introduced to the system. An effective metric is designed to calculate the reward for…
Beijing Jiaotong University（北京交通大学）；Intel Labs China（英特尔中国实验室）；Beihang University（北京航空航天大学）；Tsinghua University（清华大学）；Gaussian Robotics（高斯机器人）；Department of Electronic Engineering and BNRist（电子工程系和BNRist）
Service robots should be able to operate autonomously in dynamic and daily changing environments over an extended period of time. While Simultaneous Localization And Mapping (SLAM) is one of the most fundamental problems for robotic autonomy, most existing SLAM works are evaluated with data sequences that are recorded in a short period of time. In real-world deployment, there can be out-of-sight scene changes caused by both natural factors and human activities. For example, in home scenarios, most objects may be movable, replaceable or deformable, and the visual features of the same place may …
Learning Goal-Directed Object Pushing in Cluttered Scenes With Location-Based Attention
Humanoid Robots Lab, University of Bonn, Germany（波恩大学人形机器人实验室）；School of Informatics, The University of Edinburgh, Edinburgh, UK（爱丁堡大学信息学院）；The Alan Turing Institute, London, UK（艾伦·图灵研究所）；The Lamarr Institute, Bonn, Germany（波恩兰马尔研究所）；The Center for Robotics, University of Bonn, Germany（波恩大学机器人中心）
Recent advances in on-policy reinforcement learning (RL) methods enabled learning agents in virtual environments to master complex tasks with high-dimensional and continuous observation and action spaces. However, leveraging this family of algorithms in multi-fingered robotic grasping remains a challenge due to large sim-to-real fidelity gaps and the high sample complexity of on-policy RL algorithms. This work aims to bridge these gaps by first reinforcement-learning a multi-fingered robotic grasping policy in simulation that operates in the pixel space of the input: a single depth image. Usin…
Recent methods for 6D pose estimation of objects assume either textured 3D models or real images that cover the entire range of target poses. However, it is difficult to obtain textured 3D models and annotate the poses of objects in real scenarios. This paper proposes a method, Neural Object Learning (NOL), that creates synthetic images of objects in arbitrary poses by combining only a few observations from cluttered images. A novel refinement step is proposed to align inaccurate poses of objects in source images, which results in better quality images. Evaluations performed on two public data…
NVIDIA（英伟达）；The Robotics Institute, CMU（卡内基梅隆大学机器人研究所）；University of Washington（华盛顿大学）
Grasping in cluttered environments is a fundamental but challenging robotic skill. It requires both reasoning about unseen object parts and potential collisions with the manipulator. Most existing data-driven approaches avoid this problem by limiting themselves to top-down planar grasps which is insufficient for many real-world scenarios and greatly limits possible grasps. We present a method that plans 6-DOF grasps for any desired object in a cluttered scene from partial point cloud observations. Our method achieves a grasp success of 80.3%, outperforming baseline approaches by 17.6% and clea…
Recognition of occluded objects in unseen indoor environments is a challenging problem for mobile robots. This work proposes a new slicing-based topological descriptor that captures the 3D shape of object point clouds to address this challenge. It yields similarities between the descriptors of the occluded and the corresponding unoccluded objects, enabling object unity-based recognition using a library of trained models. The descriptor is obtained by partitioning an object's point cloud into multiple 2D slices and constructing filtrations (nested sequences of simplicial complexes) on the slice…
Real-to-Sim for Highly Cluttered Environments via Physics-Consistent Inter-Object Reasoning
Robotics and Autonomous Systems Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）机器人与自主系统方向）；Institute of Data Science, The University of Hong Kong（香港大学数据科学学院）
George Mason University（乔治梅森大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
Detection of objects in cluttered indoor environments is one of the key enabling functionalities for service robots. The best performing object detection approaches in computer vision exploit deep Convolutional Neural Networks (CNN) to simultaneously detect and categorize the objects of interest in cluttered scenes. Training of such models typically requires large amounts of annotated training data which is time consuming and costly to obtain. In this work we explore the ability of using synthetically generated composite images for training state-of-the-art object detectors, especially for obj…
Efficient Heatmap-Guided 6-Dof Grasp Detection in Cluttered Scenes
Tsinghua University（清华大学）；Shanghai AI Laboratory（上海人工智能实验室）；Beijing National Research Center for Information Science and Technology (BNRist)（北京信息科学与技术国家研究中心）
TransBiolab: A Real-World Multi-View Dataset of Cluttered Transparent Biomedical Objects
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（华中科技大学人工智能与自动化学院）；College of Design and Innovation, Tongji University（同济大学设计创意学院）；Shanghai Institute for Intelligent Autonomous Systems, Tongji University（同济大学上海智能无人系统研究院）；School of Software and Engineering, Huazhong University of Science and Technology（华中科技大学软件学院）
This paper introduces an active object detection and localization framework that combines a robust untextured object detection and 3D pose estimation algorithm with a novel next-best-view selection strategy. We address the detection and localization problems by proposing an edge-based registration algorithm that refines the object position by minimizing a cost directly extracted from a 3D image tensor that encodes the minimum distance to an edge point in a joint direction/location space. We face the next-best-view problem by exploiting a sequential decision process that, for each step, selects…
