近300篇机器人操作工作汇总！涵盖从抓取到复杂操控的各类任务、方法和应用

Robot Manipulation（机器人操控）是机器人技术中的一个关键领域，涉及机器人在物理环境中与物体的交互和操作能力。它旨在让机器人能够自主感知、规划并执行复杂的物体抓取、移动、旋转和精细操作等任务。机器人操控技术广泛应用于工业自动化、医疗手术、家务辅助、物流搬运等场景，为机器人能够适应和完成多样化的任务提供了技术支撑。

本项目汇总了Robot Manipulation领域的关键研究论文，涵盖从抓取到复杂操控的各类任务、方法和应用，提供了关于表征学习、强化学习、多模态学习、3D表征等技术的最新进展，方便机器人操控领域的研究者和实践者学习阅读。

最近收集整理了300+篇关于Robotics+Manipulation的文献，公开在了github上，repo链接：https://github.com/BaiShuanghao/Awesome-Robotics-Manipulation

内容出自国内首个具身智能全栈学习社区：具身智能之心知识星球，这里包含所有你想要的。

Grasp相关

1）Rectangle-based Grasp

• Title: HMT-Grasp: A Hybrid Mamba-Transformer Approach for Robot Grasping in Cluttered Environments｜https://arxiv.org/abs/2410.03522
• Title: Lightweight Language-driven Grasp Detection using Conditional Consistency Model｜https://arxiv.org/abs/2407.17967
• Title: grasp_det_seg_cnn: End-to-end Trainable Deep Neural Network for Robotic Grasp Detection and Semantic Segmentation from RGB｜https://arxiv.org/abs/2107.05287
• Title: GR-ConvNet: Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network｜https://arxiv.org/abs/1909.04810

2）6-DoF Grasp

• Title: Real-to-Sim Grasp: Rethinking the Gap between Simulation and Real World in Grasp Detection｜https://arxiv.org/abs/2410.06521
• Title: OrbitGrasp: SE(3)-Equivariant Grasp Learning｜https://arxiv.org/abs/2407.03531
• Title: EquiGraspFlow: SE(3)-Equivariant 6-DoF Grasp Pose Generative Flows｜https://openreview.net/pdf?id=5lSkn5v4LK
• Title: An Economic Framework for 6-DoF Grasp Detection｜https://arxiv.org/abs/2407.08366
• Title: Generalizing 6-DoF Grasp Detection via Domain Prior Knowledge｜https://arxiv.org/abs/2404.01727
• Title: Rethinking 6-Dof Grasp Detection: A Flexible Framework for High-Quality Grasping｜https://arxiv.org/abs/2403.15054
• Title: AnyGrasp: Robust and Efficient Grasp Perception in Spatial and Temporal Domains｜https://arxiv.org/abs/2212.08333,
• Title: GraspNet-1Billion: A Large-Scale Benchmark for General Object Grasping｜https://openaccess.thecvf.com/content_CVPR_2020/papers/Fang_GraspNet-1Billion_A_Large-Scale_Benchmark_for_General_Object_Grasping_CVPR_2020_paper.pdf
• Title: 6-DOF GraspNet: Variational Grasp Generation for Object Manipulation｜https://arxiv.org/abs/1905.10520

3）Grasp with 3D Techniques

• Title: Implicit Grasp Diffusion: Bridging the Gap between Dense Prediction and Sampling-based Grasping｜https://openreview.net/pdf?id=VUhlMfEekm

• Title: Learning Any-View 6DoF Robotic Grasping in Cluttered Scenes via Neural Surface Rendering｜https://arxiv.org/abs/2306.07392,

• Title: Language Embedded Radiance Fields for Zero-Shot Task-Oriented Grasping｜https://arxiv.org/abs/2309.07970

• Title: GraspNeRF: Multiview-based 6-DoF Grasp Detection for Transparent and Specular Objects Using Generalizable NeRF｜https://arxiv.org/abs/2210.06575,

• Title: GraspSplats: Efficient Manipulation with 3D Feature Splatting｜https://arxiv.org/abs/2409.02084,

• Title: GaussianGrasper: 3D Language Gaussian Splatting for Open-vocabulary Robotic Grasping｜https://arxiv.org/abs/2403.09637,

4）Language-Driven Grasp

• Title: RTAGrasp: Learning Task-Oriented Grasping from Human Videos via Retrieval, Transfer, and Alignment｜https://arxiv.org/abs/2409.16033
• Title: Language-Driven 6-DoF Grasp Detection Using Negative Prompt Guidance｜https://arxiv.org/abs/2407.13842,
• Title: Reasoning Grasping via Multimodal Large Language Model｜https://arxiv.org/abs/2402.06798
• Title: ThinkGrasp: A Vision-Language System for Strategic Part Grasping in Clutter｜https://arxiv.org/abs/2407.11298
• Title: Towards Open-World Grasping with Large Vision-Language Models｜https://arxiv.org/abs/2406.18722
• Title: Reasoning Tuning Grasp: Adapting Multi-Modal Large Language Models for Robotic Grasping｜https://openreview.net/pdf?id=3mKb5iyZ2V

5）Grasp for Transparent Objects

• Title: T²SQNet: A Recognition Model for Manipulating Partially Observed Transparent Tableware Objects｜https://openreview.net/pdf?id=M0JtsLuhEE
• Title: ASGrasp: Generalizable Transparent Object Reconstruction and Grasping from RGB-D Active Stereo Camera｜https://arxiv.org/abs/2405.05648
• Title: Dex-NeRF: Using a Neural Radiance Field to Grasp Transparent Objects｜https://arxiv.org/abs/2110.14217

Manipulation相关

1）Representation Learning with Auxiliary Tasks

• Title: Contrastive Imitation Learning for Language-guided Multi-Task Robotic Manipulation｜https://arxiv.org/abs/2406.09738

• Title: Vid2Robot: End-to-end Video-conditioned Policy Learning with Cross-Attention Transformers｜https://arxiv.org/abs/2403.12943

• Title: R3M: A Universal Visual Representation for Robot Manipulation｜https://arxiv.org/abs/2203.12601

• Title: HULC: What Matters in Language Conditioned Robotic Imitation Learning over Unstructured Data｜https://arxiv.org/abs/2204.06252

• Title: BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning｜https://arxiv.org/abs/2202.02005

• Title: Spatiotemporal Predictive Pre-training for Robotic Motor Control｜https://arxiv.org/abs/2403.05304

• Title: MUTEX: Learning Unified Policies from Multimodal Task Specifications｜https://arxiv.org/abs/2309.14320

• Title: Language-Driven Representation Learning for Robotics｜https://arxiv.org/abs/2302.12766

• Title: Real-World Robot Learning with Masked Visual Pre-training｜https://arxiv.org/abs/2210.03109

• Title: RACER: Rich Language-Guided Failure Recovery Policies for Imitation Learning｜https://arxiv.org/abs/2409.14674

• Title: EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of Thought｜https://arxiv.org/abs/2305.15021

• Title: Chain-of-Thought Predictive Control｜https://arxiv.org/abs/2304.00776

• Title: VIRT: Vision Instructed Transformer for Robotic Manipulation｜https://arxiv.org/abs/2410.07169

• Title: KOI: Accelerating Online Imitation Learning via Hybrid Key-state Guidance｜https://www.arxiv.org/abs/2408.02912

• Title: GENIMA: Generative Image as Action Models｜https://arxiv.org/abs/2407.07875

• Title: ATM: Any-point Trajectory Modeling for Policy Learning｜https://arxiv.org/abs/2401.00025

• Title: Learning Manipulation by Predicting Interaction｜https://www.arxiv.org/abs/2406.00439

• Title: Object-Centric Instruction Augmentation for Robotic Manipulation｜https://arxiv.org/abs/2401.02814

• Title: Towards Generalizable Zero-Shot Manipulation via Translating Human Interaction Plans｜https://arxiv.org/abs/2312.00775

• Title: CALAMARI: Contact-Aware and Language conditioned spatial Action MApping for contact-RIch manipulation｜https://openreview.net/pdf?id=Nii0_rRJwN

• Title: GHIL-Glue: Hierarchical Control with Filtered Subgoal Images｜https://arxiv.org/abs/2410.20018

• Title: FoAM: Foresight-Augmented Multi-Task Imitation Policy for Robotic Manipulation｜https://arxiv.org/abs/2409.19528

• Title: VideoAgent: Self-Improving Video Generation｜https://arxiv.org/abs/2410.10076

• Title: GR-MG: Leveraging Partially Annotated Data via Multi-Modal Goal Conditioned Policy｜https://arxiv.org/abs/2408.14368

• Title: GR-2: A Generative Video-Language-Action Model with Web-Scale Knowledge for Robot Manipulation｜https://arxiv.org/abs/2410.06158

• Title: VLMPC: Vision-Language Model Predictive Control for Robotic Manipulation｜https://arxiv.org/abs/2407.09829

• Title: GR-1: Unleashing Large-Scale Video Generative Pre-training for Visual Robot Manipulation｜https://arxiv.org/abs/2312.13139

• Title: SuSIE: Zero-Shot Robotic Manipulation with Pretrained Image-Editing Diffusion Models｜https://arxiv.org/abs/2310.10639

• Title: VLP: Video Language Planning｜https://arxiv.org/abs/2310.10625,

2）Visual Representation Learning

• Title: Robots Pre-train Robots: Manipulation-Centric Robotic Representation from Large-Scale Robot Datasets｜https://arxiv.org/abs/2410.22325
• Title: Theia: Distilling Diverse Vision Foundation Models for Robot Learning｜https://arxiv.org/abs/2407.20179
• Title: Learning Manipulation by Predicting Interaction｜https://www.arxiv.org/abs/2406.00439
• Title: Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware｜https://arxiv.org/abs/2304.13705
• Title: Language-Driven Representation Learning for Robotics｜https://arxiv.org/abs/2302.12766
• Title: VIMA: General Robot Manipulation with Multimodal Prompts｜https://arxiv.org/abs/2210.03094
• Title: Real-World Robot Learning with Masked Visual Pre-training｜https://arxiv.org/abs/2210.03109
• Title: R3M: A Universal Visual Representation for Robot Manipulation｜https://arxiv.org/abs/2203.12601
• Title: LIV: Language-Image Representations and Rewards for Robotic Control｜https://arxiv.org/abs/2306.00958
• Title: VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training｜https://arxiv.org/abs/2210.00030
• Title: Can Foundation Models Perform Zero-Shot Task Specification For Robot Manipulation?｜https://arxiv.org/abs/2204.11134

3）Multimodal Representation Learning

• Title: Play to the Score: Stage-Guided Dynamic Multi-Sensory Fusion for Robotic Manipulation｜https://arxiv.org/abs/2408.01366
• Title: MUTEX: Learning Unified Policies from Multimodal Task Specifications｜https://arxiv.org/abs/2309.14320

4）Latent Action Learning

• Title: Discrete Policy: Learning Disentangled Action Space for Multi-Task Robotic Manipulation｜https://arxiv.org/abs/2409.18707, - Title: IGOR: Image-GOal Representations Atomic Control Units for Foundation Models in Embodied AI｜https://www.microsoft.com/en-us/research/uploads/prod/2024/10/Project_IGOR_for_arXiv.pdf
• Title: Latent Action Pretraining from Videos｜https://arxiv.org/abs/2410.11758
• Title: Goal Representations for Instruction Following: A Semi-Supervised Language Interface to Control｜https://arxiv.org/abs/2307.00117
• Title: MimicPlay: Long-Horizon Imitation Learning by Watching Human Play｜https://arxiv.org/abs/2302.12422
• Title: Imitation Learning with Limited Actions via Diffusion Planners and Deep Koopman Controllers｜https://arxiv.org/abs/2410.07584
• Title: Learning to Act without Actions｜https://arxiv.org/abs/2312.10812
• Title: Imitating Latent Policies from Observation｜https://arxiv.org/abs/1805.07914

5）World Model

• Title: MOTO: Offline Pre-training to Online Fine-tuning for Model-based Robot Learning｜https://arxiv.org/abs/2401.03306, - Title: Finetuning Offline World Models in the Real World｜https://arxiv.org/abs/2310.16029,
• Title: Surfer: Progressive Reasoning with World Models for Robotic Manipulation｜https://arxiv.org/abs/2306.11335,

6）Asynchronous Action Learning

• Title: PIVOT-R: Primitive-Driven Waypoint-Aware World Model for Robotic Manipulation｜https://arxiv.org/abs/2410.10394
• Title: HiRT: Enhancing Robotic Control with Hierarchical Robot Transformers｜https://arxiv.org/abs/2410.05273
• Title: MResT: Multi-Resolution Sensing for Real-Time Control with Vision-Language Models｜https://arxiv.org/abs/2401.14502

7）Diffusion Policy Learning

• Title: Diffusion Transformer Policy｜https://arxiv.org/abs/2410.15959,
• Title: SDP: Spiking Diffusion Policy for Robotic Manipulation with Learnable Channel-Wise Membrane Thresholds｜https://arxiv.org/abs/2409.11195,
• Title: The Ingredients for Robotic Diffusion Transformers｜https://arxiv.org/abs/2410.10088,
• Title: GenDP: 3D Semantic Fields for Category-Level Generalizable Diffusion Policy｜https://arxiv.org/abs/2410.17488
• Title: EquiBot: SIM(3)-Equivariant Diffusion Policy for Generalizable and Data Efficient Learning｜https://arxiv.org/abs/2407.01479
• Title: Sparse Diffusion Policy: A Sparse, Reusable, and Flexible Policy for Robot Learning｜https://arxiv.org/abs/2407.01531
• Title: MDT: Multimodal Diffusion Transformer: Learning Versatile Behavior from Multimodal Goals｜https://arxiv.org/abs/2407.05996
• Title: Render and Diffuse: Aligning Image and Action Spaces for Diffusion-based Behaviour Cloning｜https://arxiv.org/abs/2405.18196,
• Title: DP3: 3D Diffusion Policy: Generalizable Visuomotor Policy Learning via Simple 3D Representations｜https://arxiv.org/abs/2403.03954
• Title: PlayFusion: Skill Acquisition via Diffusion from Language-Annotated Play｜https://arxiv.org/abs/2312.04549
• Title: Equivariant Diffusion Policy｜https://arxiv.org/abs/2407.01812
• Title: StructDiffusion: Language-Guided Creation of Physically-Valid Structures using Unseen Objects｜https://arxiv.org/abs/2211.04604
• Title: Goal-Conditioned Imitation Learning using Score-based Diffusion Policies｜https://arxiv.org/abs/2304.02532
• Title: Diffusion Policy: Visuomotor Policy Learning via Action Diffusion｜https://arxiv.org/abs/2303.04137

8）Other Policies

• Title: Autoregressive Action Sequence Learning for Robotic Manipulation｜https://arxiv.org/abs/2410.03132,
• Title: MaIL: Improving Imitation Learning with Selective State Space Models｜https://arxiv.org/abs/2406.08234,

9）Vision Language Action Models

• Title: Run-time Observation Interventions Make Vision-Language-Action Models More Visually Robust｜https://arxiv.org/abs/2410.01971
• Title: TinyVLA: Towards Fast, Data-Efficient Vision-Language-Action Models for Robotic Manipulation｜https://arxiv.org/abs/2409.12514
• Title: RoboMamba: Multimodal State Space Model for Efficient Robot Reasoning and Manipulation｜https://arxiv.org/abs/2406.04339
• Title: A Dual Process VLA: Efficient Robotic Manipulation Leveraging VLM｜https://arxiv.org/abs/2410.15549
• Title: OpenVLA: An Open-Source Vision-Language-Action Model｜https://arxiv.org/abs/2406.09246
• Title: LLARVA: Vision-Action Instruction Tuning Enhances Robot Learning｜https://arxiv.org/abs/2406.11815
• Title: Robotic Control via Embodied Chain-of-Thought Reasoning｜https://arxiv.org/abs/2406.11815
• Title: 3D-VLA: A 3D Vision-Language-Action Generative World Model｜https://arxiv.org/abs/2403.09631
• Title: Octo: An Open-Source Generalist Robot Policy｜https://arxiv.org/abs/2405.12213,
• Title: RoboFlamingo: Vision-Language Foundation Models as Effective Robot Imitators｜https://arxiv.org/abs/2311.01378
• Title: RT-H: Action Hierarchies Using Language｜https://arxiv.org/abs/2403.01823
• Title: Open X-Embodiment: Robotic Learning Datasets and RT-X Models｜https://arxiv.org/abs/2310.08864,
• Title: MOO: Open-World Object Manipulation using Pre-trained Vision-Language Models｜https://arxiv.org/abs/2303.00905
• Title: RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control｜https://arxiv.org/abs/2307.15818
• Title: RT-1: Robotics Transformer for Real-World Control at Scale｜https://arxiv.org/abs/2212.06817

10）Reinforcement Learning

• Title: Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning｜https://arxiv.org/abs/2410.21845
• Title: PointPatchRL -- Masked Reconstruction Improves Reinforcement Learning on Point Clouds｜https://arxiv.org/abs/2410.18800
• Title: SPIRE: Synergistic Planning, Imitation, and Reinforcement for Long-Horizon Manipulation｜https://arxiv.org/abs/2410.18065
• Title: Learning to Manipulate Anywhere: A Visual Generalizable Framework For Reinforcement Learning｜https://arxiv.org/abs/2407.15815
• Title: Plan-Seq-Learn: Language Model Guided RL for Solving Long Horizon Robotics Tasks｜https://arxiv.org/abs/2405.01534,
• Title: Expansive Latent Planning for Sparse Reward Offline Reinforcement Learning｜https://openreview.net/pdf?id=xQx1O7WXSA,
• Title: Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions｜https://arxiv.org/abs/2309.10150,
• Title: Sim2Real Transfer for Reinforcement Learning without Dynamics Randomization｜https://arxiv.org/abs/2002.11635,
• Title: Pre-Training for Robots: Offline RL Enables Learning New Tasks from a Handful of Trials｜https://arxiv.org/abs/2210.05178

11）Motion, Tranjectory and Flow

• Title: Language-Conditioned Path Planning｜https://arxiv.org/abs/2308.16893

• Title: DiffusionSeeder: Seeding Motion Optimization with Diffusion for Rapid Motion Planning｜https://arxiv.org/abs/2410.16727

• Title: ReKep: Spatio-Temporal Reasoning of Relational Keypoint Constraints for Robotic Manipulation｜https://arxiv.org/abs/2409.01652

• Title: CoPa: General Robotic Manipulation through Spatial Constraints of Parts with Foundation Models｜https://arxiv.org/abs/2403.08248

• Title: Task Generalization with Stability Guarantees via Elastic Dynamical System Motion Policies｜https://arxiv.org/abs/2309.01884

• Title: ORION: Vision-based Manipulation from Single Human Video with Open-World Object Graphs｜https://arxiv.org/abs/2405.20321

• Title: Learning Robotic Manipulation Policies from Point Clouds with Conditional Flow Matching｜https://arxiv.org/abs/2409.07343

• Title: RoboTAP: Tracking Arbitrary Points for Few-Shot Visual Imitation｜https://arxiv.org/abs/2308.15975

• Title: VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models｜https://arxiv.org/abs/2307.05973

• Title: LATTE: LAnguage Trajectory TransformEr｜https://arxiv.org/abs/2208.02918

• Title: Track2Act: Predicting Point Tracks from Internet Videos enables Generalizable Robot Manipulation｜https://arxiv.org/abs/2405.01527

• Title: Any-point Trajectory Modeling for Policy Learning｜https://arxiv.org/abs/2401.00025

• Title: Waypoint-Based Imitation Learning for Robotic Manipulation｜https://arxiv.org/abs/2307.14326

• Title: Flow as the Cross-Domain Manipulation Interface｜https://www.arxiv.org/abs/2407.15208

• Title: Learning to Act from Actionless Videos through Dense Correspondences｜https://arxiv.org/abs/2310.08576

12）Data Collection, Selection and Augmentation

• Title: SkillMimicGen: Automated Demonstration Generation for Efficient Skill Learning and Deployment｜https://arxiv.org/abs/2410.18907

• Title: Scaling Robot Policy Learning via Zero-Shot Labeling with Foundation Models｜https://arxiv.org/abs/2410.17772

• Title: Autonomous Improvement of Instruction Following Skills via Foundation Models｜https://arxiv.org/abs/2407.20635

• Title: Manipulate-Anything: Automating Real-World Robots using Vision-Language Models｜https://arxiv.org/abs/2406.18915,

• Title: DexCap: Scalable and Portable Mocap Data Collection System for Dexterous Manipulation｜https://arxiv.org/abs/2403.07788,

• Title: SPRINT: Scalable Policy Pre-Training via Language Instruction Relabeling｜https://arxiv.org/abs/2306.11886,

• Title: Scaling Up and Distilling Down: Language-Guided Robot Skill Acquisition｜https://arxiv.org/abs/2307.14535

• Title: Robotic Skill Acquisition via Instruction Augmentation with Vision-Language Models｜https://arxiv.org/abs/2211.11736

• Title: RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation｜https://arxiv.org/abs/2306.11706,

• Title: Active Fine-Tuning of Generalist Policies｜https://arxiv.org/abs/2410.05026

• Title: Re-Mix: Optimizing Data Mixtures for Large Scale Imitation Learning｜https://arxiv.org/abs/2408.14037

• Title: An Unbiased Look at Datasets for Visuo-Motor Pre-Training｜https://arxiv.org/abs/2310.09289,

• Title: Retrieval-Augmented Embodied Agents｜https://arxiv.org/abs/2404.11699,

• Title: Behavior Retrieval: Few-Shot Imitation Learning by Querying Unlabeled Datasets｜https://arxiv.org/abs/2304.08742,

• Title: RoVi-Aug: Robot and Viewpoint Augmentation for Cross-Embodiment Robot Learning｜https://arxiv.org/abs/2409.03403

• Title: Diffusion Augmented Agents: A Framework for Efficient Exploration and Transfer Learning｜https://arxiv.org/abs/2407.20798

• Title: Diffusion Meets DAgger: Supercharging Eye-in-hand Imitation Learning｜https://arxiv.org/abs/2402.17768,

• Title: GenAug: Retargeting behaviors to unseen situations via Generative Augmentation｜https://arxiv.org/abs/2302.06671

• Title: Contrast Sets for Evaluating Language-Guided Robot Policies｜https://arxiv.org/abs/2406.13636

13）Affordance Learning

• Title: UniAff: A Unified Representation of Affordances for Tool Usage and Articulation with Vision-Language Models｜https://arxiv.org/abs/2409.20551,

• Title: A3VLM: Actionable Articulation-Aware Vision Language Model｜https://arxiv.org/abs/2406.07549,

• Title: AIC MLLM: Autonomous Interactive Correction MLLM for Robust Robotic Manipulation｜https://arxiv.org/abs/2406.11548,

• Title: SAGE: Bridging Semantic and Actionable Parts for Generalizable Manipulation of Articulated Objects｜https://arxiv.org/abs/2312.01307,

• Title: Kinematic-aware Prompting for Generalizable Articulated Object Manipulation with LLMs｜https://arxiv.org/abs/2311.02847,

• Title: Ditto: Building Digital Twins of Articulated Objects from Interaction｜https://arxiv.org/abs/2202.08227,

• Title: Language-Conditioned Affordance-Pose Detection in 3D Point Clouds｜https://arxiv.org/abs/2309.10911,

• Title: Composable Part-Based Manipulation｜https://arxiv.org/abs/2405.05876,

• Title: PartManip: Learning Cross-Category Generalizable Part Manipulation Policy from Point Cloud Observations｜https://arxiv.org/abs/2303.16958,

• Title: GAPartNet: Cross-Category Domain-Generalizable Object Perception and Manipulation via Generalizable and Actionable Parts｜https://arxiv.org/abs/2211.05272,

• Title: SpatialBot: Precise Spatial Understanding with Vision Language Models｜https://arxiv.org/abs/2406.13642,

• Title: RoboPoint: A Vision-Language Model for Spatial Affordance Prediction for Robotics｜https://arxiv.org/abs/2406.10721,

• Title: SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities｜https://arxiv.org/abs/2401.12168,

• Title: RAM: Retrieval-Based Affordance Transfer for Generalizable Zero-Shot Robotic Manipulation｜https://arxiv.org/abs/2407.04689,

• Title: MOKA: Open-World Robotic Manipulation through Mark-Based Visual Prompting｜https://arxiv.org/abs/2403.03174

• Title: SLAP: Spatial-Language Attention Policies｜https://arxiv.org/abs/2304.11235,

• Title: KITE: Keypoint-Conditioned Policies for Semantic Manipulation｜https://arxiv.org/abs/2306.16605,

• Title: HULC++: Grounding Language with Visual Affordances over Unstructured Data｜https://arxiv.org/abs/2210.01911

• Title: CLIPort: What and Where Pathways for Robotic Manipulation｜https://arxiv.org/abs/2109.12098,

• Title: Affordance Learning from Play for Sample-Efficient Policy Learning｜https://arxiv.org/abs/2203.00352

• Title: Transporter Networks: Rearranging the Visual World for Robotic Manipulation｜https://arxiv.org/abs/2010.14406,

14）3D Representation for Manipulation

• Title: MSGField: A Unified Scene Representation Integrating Motion, Semantics, and Geometry for Robotic Manipulation｜https://arxiv.org/abs/2410.15730
• Title: Splat-MOVER: Multi-Stage, Open-Vocabulary Robotic Manipulation via Editable Gaussian Splatting｜https://arxiv.org/abs/2405.04378
• Title: IMAGINATION POLICY: Using Generative Point Cloud Models for Learning Manipulation Policies｜https://arxiv.org/abs/2406.11740
• Title: Physically Embodied Gaussian Splatting: A Realtime Correctable World Model for Robotics｜https://arxiv.org/abs/2406.10788
• Title: RiEMann: Near Real-Time SE(3)-Equivariant Robot Manipulation without Point Cloud Segmentation｜https://arxiv.org/abs/2403.19460
• Title: RoboEXP: Action-Conditioned Scene Graph via Interactive Exploration for Robotic Manipulation｜https://arxiv.org/abs/2402.15487
• Title: D³Fields: Dynamic 3D Descriptor Fields for Zero-Shot Generalizable Rearrangement｜https://arxiv.org/abs/2309.16118
• Title: Object-Aware Gaussian Splatting for Robotic Manipulation｜https://openreview.net/pdf?id=gdRI43hDgo
• Title: Distilled Feature Fields Enable Few-Shot Language-Guided Manipulation｜https://arxiv.org/abs/2308.07931
• Title: Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation｜https://arxiv.org/abs/2112.05124
• Title: SE(3)-Equivariant Relational Rearrangement with Neural Descriptor Fields｜https://arxiv.org/abs/2211.09786

15）3D Representation Policy Learning

• Title: GravMAD: Grounded Spatial Value Maps Guided Action Diffusion for Generalized 3D Manipulation｜https://arxiv.org/abs/2409.20154

• Title: 3D Diffuser Actor: Policy Diffusion with 3D Scene Representations｜https://arxiv.org/abs/2402.10885

• Title: DP3: 3D Diffusion Policy: Generalizable Visuomotor Policy Learning via Simple 3D Representations｜https://arxiv.org/abs/2403.03954

• Title: ManiGaussian: Dynamic Gaussian Splatting for Multi-task Robotic Manipulation｜https://arxiv.org/abs/2403.08321

• Title: SGRv2: Leveraging Locality to Boost Sample Efficiency in Robotic Manipulation｜https://arxiv.org/abs/2406.10615

• Title: GNFactor: Multi-Task Real Robot Learning with Generalizable Neural Feature Fields｜https://arxiv.org/abs/2308.16891

• Title: Visual Reinforcement Learning with Self-Supervised 3D Representations｜https://arxiv.org/abs/2210.07241

• Title: PolarNet: 3D Point Clouds for Language-Guided Robotic Manipulation｜https://arxiv.org/abs/2309.15596

• Title: M2T2: Multi-Task Masked Transformer for Object-centric Pick and Place｜https://arxiv.org/abs/2311.00926

• Title: PerAct: Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulation｜https://arxiv.org/abs/2209.05451

• Title: 3D-MVP: 3D Multiview Pretraining for Robotic Manipulation｜https://arxiv.org/abs/2406.18158

• Title: Discovering Robotic Interaction Modes with Discrete Representation Learning｜https://arxiv.org/abs/2410.20258

• Title: SAM-E: Leveraging Visual Foundation Model with Sequence Imitation for Embodied Manipulation｜https://arxiv.org/abs/2405.19586

• Title: RVT: Robotic View Transformer for 3D Object Manipulation｜https://arxiv.org/abs/2306.14896

• Title: Learning Generalizable Manipulation Policies with Object-Centric 3D Representations｜https://arxiv.org/abs/2310.14386

• Title: SGR: A Universal Semantic-Geometric Representation for Robotic Manipulation｜https://arxiv.org/abs/2306.10474

17）Generalization

• Title: Mirage: Cross-Embodiment Zero-Shot Policy Transfer with Cross-Painting｜https://arxiv.org/abs/2402.19249

• Title: Policy Architectures for Compositional Generalization in Control｜https://arxiv.org/abs/2203.05960

• Title: Programmatically Grounded, Compositionally Generalizable Robotic Manipulation｜https://arxiv.org/abs/2304.13826

• Title: Efficient Data Collection for Robotic Manipulation via Compositional Generalization｜https://arxiv.org/abs/2403.05110

• Title: Natural Language Can Help Bridge the Sim2Real Gap｜https://arxiv.org/abs/2405.10020

• Title: Reconciling Reality through Simulation: A Real-to-Sim-to-Real Approach for Robust Manipulation｜https://arxiv.org/abs/2403.03949

• Title: Local Policies Enable Zero-shot Long-horizon Manipulation｜https://arxiv.org/abs/2410.22332,

• Title: A Backbone for Long-Horizon Robot Task Understanding｜https://arxiv.org/abs/2408.01334,

• Title: STAP: Sequencing Task-Agnostic Policies｜https://arxiv.org/abs/2210.12250

• Title: BOSS: Bootstrap Your Own Skills: Learning to Solve New Tasks with Large Language Model Guidance｜https://arxiv.org/abs/2310.10021

• Title: Learning Compositional Behaviors from Demonstration and Language｜https://openreview.net/pdf?id=fR1rCXjCQX

• Title: Policy Adaptation via Language Optimization: Decomposing Tasks for Few-Shot Imitation｜https://arxiv.org/abs/2408.16228

18）Generalist

• Title: Scaling Cross-Embodied Learning: One Policy for Manipulation, Navigation, Locomotion and Aviation｜https://arxiv.org/abs/2408.11812

• Title: All Robots in One: A New Standard and Unified Dataset for Versatile, General-Purpose Embodied Agents｜https://arxiv.org/abs/2408.10899

• Title: Scaling Proprioceptive-Visual Learning with Heterogeneous Pre-trained Transformers｜https://arxiv.org/abs/2409.20537

• Title: An Embodied Generalist Agent in 3D World｜https://arxiv.org/abs/2311.12871

• Title: Towards Synergistic, Generalized, and Efficient Dual-System for Robotic Manipulation｜https://arxiv.org/abs/2410.08001

• Title: Effective Tuning Strategies for Generalist Robot Manipulation Policies｜https://arxiv.org/abs/2410.01220,

• Title: Octo: An Open-Source Generalist Robot Policy｜https://arxiv.org/abs/2405.12213,

• Title: Steering Your Generalists: Improving Robotic Foundation Models via Value Guidance｜https://arxiv.org/abs/2410.13816

• Title: Open X-Embodiment: Robotic Learning Datasets and RT-X Models｜https://arxiv.org/abs/2310.08864,

• Title: RoboAgent: Generalization and Efficiency in Robot Manipulation via Semantic Augmentations and Action Chunking｜https://arxiv.org/abs/2309.01918,

• Title: Learning to Manipulate Anywhere: A Visual Generalizable Framework For Reinforcement Learning｜https://arxiv.org/abs/2407.15815

• Title: CAGE: Causal Attention Enables Data-Efficient Generalizable Robotic Manipulation｜https://arxiv.org/abs/2407.15815

• Title: Robot Utility Models: General Policies for Zero-Shot Deployment in New Environments｜https://arxiv.org/abs/2409.05865

19）Human-Robot Interaction and Collaboration

• Title: Vocal Sandbox: Continual Learning and Adaptation for Situated Human-Robot Collaboration｜https://openreview.net/pdf?id=ypaYtV1CoG
• Title: APRICOT: Active Preference Learning and Constraint-Aware Task Planning with LLMs｜https://openreview.net/pdf?id=nQslM6f7dW
• Title: Text2Interaction: Establishing Safe and Preferable Human-Robot Interaction｜https://arxiv.org/abs/2408.06105
• Title: KNOWNO: Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners｜https://arxiv.org/abs/2307.01928,
• Title: Yell At Your Robot: Improving On-the-Fly from Language Corrections｜https://arxiv.org/abs/2403.12910,
• Title: "No, to the Right" -- Online Language Corrections for Robotic Manipulation via Shared Autonomy｜https://arxiv.org/abs/2301.02555,

Humanoid

1）Dexterous Manipulation

• Title: DexGraspNet: A Large-Scale Robotic Dexterous Grasp Dataset for General Objects Based on Simulation｜https://arxiv.org/abs/2210.02697,
• Title: Demonstrating Learning from Humans on Open-Source Dexterous Robot Hands｜https://www.roboticsproceedings.org/rss20/p014.pdf,
• Title: CyberDemo: Augmenting Simulated Human Demonstration for Real-World Dexterous Manipulation｜https://arxiv.org/abs/2402.14795,
• Title: Dexterous Functional Grasping｜https://arxiv.org/abs/2312.02975,
• Title: DEFT: Dexterous Fine-Tuning for Real-World Hand Policies｜https://arxiv.org/abs/2310.19797,
• Title: REBOOT: Reuse Data for Bootstrapping Efficient Real-World Dexterous Manipulation｜https://arxiv.org/abs/2309.03322,
• Title: Sequential Dexterity: Chaining Dexterous Policies for Long-Horizon Manipulation｜https://arxiv.org/abs/2309.00987,
• Title: AnyTeleop: A General Vision-Based Dexterous Robot Arm-Hand Teleoperation System｜https://arxiv.org/abs/2307.04577,

2）Other Applications

• Title: Leveraging Language for Accelerated Learning of Tool Manipulation｜https://arxiv.org/abs/2206.13074,

Awesome Benchmarks

1）Grasp Datasets

• Title: QDGset: A Large Scale Grasping Dataset Generated with Quality-Diversity｜https://arxiv.org/abs/2410.02319,
• Title: Real-to-Sim Grasp: Rethinking the Gap between Simulation and Real World in Grasp Detection｜https://arxiv.org/abs/2410.06521,
• Title: Grasp-Anything-6D: Language-Driven 6-DoF Grasp Detection Using Negative Prompt Guidance｜https://arxiv.org/abs/2407.13842
• Title: Grasp-Anything++: Language-driven Grasp Detection｜https://arxiv.org/abs/2406.09489
• Title: Grasp-Anything: Large-scale Grasp Dataset from Foundation Models｜https://arxiv.org/abs/2309.09818,
• Title: GraspNet-1Billion: A Large-Scale Benchmark for General Object Grasping｜https://openaccess.thecvf.com/content_CVPR_2020/papers/Fang_GraspNet-1Billion_A_Large-Scale_Benchmark_for_General_Object_Grasping_CVPR_2020_paper.pdf

2）Manipulation Benchmarks

• Title: RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots｜https://arxiv.org/abs/2406.02523

• Title: ARNOLD: A Benchmark for Language-Grounded Task Learning With Continuous States in Realistic 3D Scenes｜https://arxiv.org/abs/2304.04321,

• Title: HomeRobot: Open-Vocabulary Mobile Manipulation｜https://arxiv.org/abs/2306.11565,

• Title: ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks｜https://arxiv.org/abs/1912.01734,

• Title: Towards Generalizable Vision-Language Robotic Manipulation: A Benchmark and LLM-guided 3D Policy｜https://arxiv.org/abs/2410.01345,

• Title: THE COLOSSEUM: A Benchmark for Evaluating Generalization for Robotic Manipulation｜https://arxiv.org/abs/2402.08191,

• Title: VIMA: General Robot Manipulation with Multimodal Prompts｜https://arxiv.org/abs/2210.03094,

• Title: CALVIN: A Benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation Tasks｜https://arxiv.org/abs/2112.03227,

• Title: RLBench: The Robot Learning Benchmark & Learning Environment｜https://arxiv.org/abs/1909.12271,

• Title: Evaluating Real-World Robot Manipulation Policies in Simulation｜https://arxiv.org/abs/2405.05941

• Title: LADEV: A Language-Driven Testing and Evaluation Platform for Vision-Language-Action Models in Robotic Manipulation｜https://arxiv.org/abs/2410.05191

• Title: ClutterGen: A Cluttered Scene Generator for Robot Learning｜https://arxiv.org/abs/2407.05425

• Title: Efficient Tactile Simulation with Differentiability for Robotic Manipulation｜https://openreview.net/pdf?id=6BIffCl6gsM,

• Title: Open X-Embodiment: Robotic Learning Datasets and RT-X Models｜https://arxiv.org/abs/2310.08864,

• Title: DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset｜https://arxiv.org/abs/2403.12945,

• Title: BridgeData V2: A Dataset for Robot Learning at Scale｜https://arxiv.org/abs/2308.12952,

• Title: ManipVQA: Injecting Robotic Affordance and Physically Grounded Information into Multi-Modal Large Language Models｜https://arxiv.org/abs/2403.11289,

• Title: OpenEQA: Embodied Question Answering in the Era of Foundation Models｜https://open-eqa.github.io/assets/pdfs/paper.pdf,

3）Cross-Embodiment Benchmarks

• Title: All Robots in One: A New Standard and Unified Dataset for Versatile, General-Purpose Embodied Agents｜https://arxiv.org/abs/2408.10899,
• Title: Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence?｜https://arxiv.org/abs/2408.10899,

Awesome Techniques

• Title: Awesome-Implicit-NeRF-Robotics: Neural Fields in Robotics: A Survey｜https://arxiv.org/abs/2410.20220,
• Title: Awesome-Video-Robotic-Papers,
• Title: Awesome-Generalist-Robots-via-Foundation-Models: Neural Fields in Robotics: A Survey｜https://arxiv.org/abs/2312.08782,
• Title: Awesome-Robotics-3D,
• Title: Awesome-Robotics-Foundation-Models: Foundation Models in Robotics: Applications, Challenges, and the Future｜https://arxiv.org/abs/2312.07843,
• Title: Awesome-LLM-Robotics,

Vision-Language Models

3D

• Title:  Title

• Title: Learning 2D Invariant Affordance Knowledge for 3D Affordance Grounding｜https://arxiv.org/abs/2408.13024,

• Title: Mamba3D: Enhancing Local Features for 3D Point Cloud Analysis via State Space Model｜https://arxiv.org/abs/2404.14966,

• Title: PointMamba: A Simple State Space Model for Point Cloud Analysis｜https://arxiv.org/abs/2402.10739,

• Title: Point Transformer V3: Simpler, Faster, Stronger｜https://arxiv.org/abs/2312.10035,

• Title: Point Transformer V2: Grouped Vector Attention and Partition-based Pooling｜https://arxiv.org/abs/2210.05666,

• Title: Point Transformer｜https://arxiv.org/abs/2402.10739,

• Title: PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space｜https://arxiv.org/abs/1706.02413,

• Title: PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation｜https://arxiv.org/abs/1612.00593,

• Title: LERF: Language Embedded Radiance Fields｜https://arxiv.org/abs/2303.09553,

• Title: 3D Gaussian Splatting for Real-Time Radiance Field Rendering｜https://arxiv.org/abs/2308.04079,

• Title: LangSplat: 3D Language Gaussian Splatting｜https://arxiv.org/abs/2312.16084,