Robotic Manipulation

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Robotic manipulation studies how machines use sensing and motion to change objects and environments, from picking and assembly to tool use and handling deformable materials. Approaches combine geometric planning and force or impedance control with imitation learning, reinforcement learning, diffusion policies, and vision-language-action models; world models, memory, tactile feedback, and human-video demonstrations are also used. Methods are tested in simulation and on real robots using suites such as LIBERO, RoboTwin, ManiSkill, and RLBench, across clutter, bimanual coordination, mobile manipulation, and precision contact tasks. A central challenge is turning benchmark performance into safe, reliable long-horizon behavior across new objects, cameras, robot bodies, and real-world dynamics, while limiting data collection and inference costs.

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1,685 papers341 papers in the last four weeks, up 29% on the four weeks before. 2.0% of all new papers.

Slot-MPC: Goal-Conditioned Model Predictive Control with Object-Centric Representations
May 14, 2026Jonathan Spieler, Angel Villar-Corrales, Sven BehnkeWorld Models for Robotics World Model-Based Planning
Autonomous Intelligent Systems, Computer Science Institute VI - Intelligent Systems and Robotics, Center for Robotics and the Lamarr Institute for Machine Learning and Artificial Intelligence, University of Bonn, Germany
Behavior Cloning for Active Perception with Low-Resolution Egocentric Vision
May 13, 2026Anthony Bilic, Chen Chen, Ladislau BölöniActive Vision for Robot Manipulation Active Sensing
Department of Computer Science, University of Central Florida, Orlando, FL, USA
WarmPrior: Straightening Flow-Matching Policies with Temporal Priors
May 13, 2026Sinjae Kang, Chanyoung Kim, Kaixin Wang +2Flow Matching Visuomotor Policy Learning
KAIST · Microsoft Research
RoboEvolve: Co-Evolving Planner-Simulator for Robotic Manipulation with Limited Data
May 13, 2026Harold Haodong Chen, Sirui Chen, Yingjie Xu +2Robot Skill Learning Synthetic Data Generation for Robotics
The Hong Kong University of Science and Technology (Guangzhou) · The Hong Kong University of Science and Technology
AttenA+: Rectifying Action Inequality in Robotic Foundation Models
May 13, 2026Daojie Peng, Fulong Ma, Jiahang Cao +7Robot Policy Learning Long-Horizon Robotic Manipulation
1HKUST(GZ) · 2HKU · 3USTC +3
SID: Sliding into Distribution for Robust Few-Demonstration Manipulation
May 13, 2026Yicheng Ma, Wei Yu, Zhian Su +2Generalization in Robotic Manipulation Visuomotor Policy Learning
1Grasp Lab, Zhejiang University · 2Torch Kernel Co., Ltd.
BlockVLA: Accelerating Autoregressive VLA via Block Diffusion Finetuning
May 13, 2026Ruiheng Wang, Shuanghao Bai, Haoran Zhang +2Robotic Manipulation Vision-Language-Action Models
Xi'an Jiaotong University
Object Manipulation of the Variable Topology Truss system
May 13, 2026Andrew Jang-Ho Bae, Myeongjin Choi, Haorui Li +2Robotic Control Robotics
Department of Mechanical Engineering University of Nevada, Las Vegas Las Vegas, NV 89154 · Advanced Robotics Research Center Research Institute of AI Robotics Korea Institute of Machinery and Materials Daejeon 34103 · RealMan Robotics Co., Ltd. Beijing, 100043 +2
When Absolute State Fails: Evaluating Proprioceptive Encodings for Robust Manipulation
May 13, 2026Maxime Alvarez, Ryo Watanabe, Paul Crook +4Robot Policy Learning Generalization in Robotic Manipulation
TELEXISTENCE Inc, Foundation Model Division, Japan. · The University of Tokyo, Japan.
SafeManip: A Property-Driven Benchmark for Temporal Safety Evaluation in Robotic Manipulation
May 12, 2026Chengyue Huang, Khang Vo Huynh, Mellon M. Zhang +3Robot Safety Robotic Manipulation
Department of Machine Learning, Georgia Institute of Technology · Department of Computer Science, University of Virginia
GuidedVLA: Specifying Task-Relevant Factors via Plug-and-Play Action Attention Specialization
May 12, 2026Xiaosong Jia, Bowen Yang, Zuhao Ge +17VLM Robustness Robotic Manipulation
Institute of Trustworthy Embodied AI (TEAI), Fudan University · 2Shanghai Key Laboratory of Multimodal Embodied AI · Shanghai Jiao Tong University +1
From Imagined Futures to Executable Actions: Mixture of Latent Actions for Robot Manipulation
May 12, 2026Yajie Li, Bozhou Zhang, Chun Gu +5Latent Action Learning Long-Horizon Robotic Manipulation
School of Data Science, Fudan University · Shanghai Innovation Institute · Imperial College London +1
X-Imitator: Spatial-Aware Imitation Learning via Bidirectional Action-Pose Interaction
May 12, 2026Kai Xiong, Hongjie Fang, Lixin Yang +1Robot Skill Learning Visuomotor Policy Learning
Shanghai Jiao Tong University
From Reaction to Anticipation: Proactive Failure Recovery through Agentic Task Graph for Robotic Manipulation
May 12, 2026Sheng Xu, Ruixing Jin, Huayi Zhou +6Long-Horizon Robotic Manipulation Bimanual Robotic Manipulation
School of Data Science, The Chinese University of Hong Kong, Shenzhen · DexForce Technology, 3Shenzhen Loop Area Institute.
Learning Action Manifold with Multi-view Latent Priors for Robotic Manipulation
May 12, 2026Junjin Xiao, Dongyang Li, Yandan Yang +9Multi-View Learning Robotic Manipulation
Shenzhen University of Advanced Technology
DreamAvoid: Critical-Phase Test-Time Dreaming to Avoid Failures in VLA Policies
May 12, 2026Xianzhe Fan, Yuxiang Lu, Shenyuan Gao +4Robotics Robotic Manipulation
HKU · HKUST · Northwestern University
Rainbow Deep Q-Learning with Kinematics-Aware Design for Cooperative Delta and 3-RRS Parallel Robot Insertion
May 12, 2026Hassen Nigatu, Gaokun Shi, Jituo Li +2Dexterous Manipulation Deep Q-Learning
Robotics Institute of Zhejiang University (Yuyao Robotics Research Centre), Yuyao Technology Innovation Center, No. 479, Yuyao, Ningbo 315400, Zhejiang, China
Forecast-aware Gaussian Splatting for Predictive 3D Representation in Language-Guided Pick-and-Place Manipulation
May 11, 2026Kaixin Jia, Jiacheng Xu3D Gaussian Splatting 3D Scene Prediction
KTH Royal Institute of Technology, Stockholm, Sweden.
SEVO: Semantic-Enhanced Virtual Observation for Robust VLA Manipulation via Active Illumination and Data-Centric Collection
May 11, 2026Tianchonghui Fang, Yuan Zhuang, Fei MiaoRobotic Data Collection Robotic Manipulation
School of Computing, University of Connecticut, Storrs, CT 06268, USA
HarmoWAM: Harmonizing Generalizable and Precise Manipulation via Adaptive World Action Models
May 11, 2026Qiuxuan Feng, Jiale Yu, Jiaming Liu +8World Models for Robotics Action-Conditioned World Models
State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University · The Chinese University of Hong Kong · Simplexity Robotics
UniSteer: Unified Noise Steering for Efficient Human-Guided VLA Adaptation
May 11, 2026Junjie Lu, Xinyao Qin, Yuhua Jiang +6RL for Robotics Robot Policy Adaptation
University of Technology Sydney · Work conducted at Microsoft Research. · Tsinghua University +1
VEGA: Visual Encoder Grounding Alignment for Spatially-Aware Vision-Language-Action Models
May 11, 2026Hao Wang, Xiaobao Wei, Jingyang He +103D Spatial Reasoning Spatial Grounding for VLA Models
1Peking University, Beijing, China · 2Beijing Innovation Center of Humanoid Robotics, Beijing, China
HeteroGenManip: Generalizable Manipulation For Heterogeneous Object Interactions
May 11, 2026Zhenhao Shen, Zeming Yang, Yue Chen +5Robotic Grasping Robot Foundation Models
1Peking University · 2Tianjin University
Data-Asymmetric Latent Imagination and Reranking for 3D Robotic Imitation Learning
May 11, 2026Lianghao Luo, Xizhou Bu, Ruyan Liu +5Robot Skill Learning World Models
Morphi
Retrieve-then-Steer: Online Success Memory for Test-Time Adaptation of Generative VLAs
May 11, 2026Jianchao Zhao, Huoren Yang, Yusong Hu +6Continual Test-Time Adaptation Robotic Manipulation
College of Artificial Intelligence, Xi’an Jiaotong University · 2One Robotics · 3Shenzhen University of Advanced Technology
StereoPolicy: Improving Robotic Manipulation Policies via Stereo Perception
May 11, 2026Evans Han, Yunfan Jiang, Yingke Wang +6Robot Policy Learning Visuomotor Policy Learning
1Stanford University · 2Northwestern University · 3Lambda, Inc
Zero-Shot Sim-to-Real Robot Learning: A Dexterous Manipulation Study on Reactive Catching
May 10, 2026Kejia Ren, Gaotian Wang, Andrew S. Morgan +1Domain Randomization Robot Skill Learning
Department of Computer Science, Rice University · Robotics and AI Institute
SABER: A Scalable Action-Based Embodied Dataset for Real-World VLA Adaptation
May 10, 2026Narsimha Menga, Parikshit Sakurikar, Amirreza Rouhi +6Robot Foundation Models Vision-Language-Action Models
DreamVu1
HyDRA Scorpion: A Cost-effective and Modular ROV for Real-Time Underwater Inspection, Intervention, and Object Detection
May 9, 2026Anika Tabassum Orchi, Md Farhan Zaman, Md Darain Khan +16Robotics Robotic Manipulation
Department of Computer Science and Engineering, United International University, United City, Madani Ave, Dhaka 1212, Bangladesh · Department of Electrical and Electronic Engineering, United International University, United City, Madani Ave, Dhaka 1212, Bangladesh
Preserving Foundational Capabilities in Flow-Matching VLAs through Conservative SFT
May 9, 2026Tianyi Zhang, Shaopeng Zhai, Haoran Zhang +2Supervised Fine-Tuning Continual Learning for VLA Models
Shanghai Artificial Intelligence Laboratory

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Slot-MPC: Goal-Conditioned Model Predictive Control with Object-Centric Representations

Jonathan Spieler, Angel Villar-Corrales, Sven Behnke

Autonomous Intelligent Systems, Computer Science Institute VI - Intelligent Systems and Robotics, Center for Robotics and the Lamarr Institute for Machine Learning and Artificial Intelligence, University of Bonn, Germany

World Models for Robotics World Model-Based Planning Action-Conditioned World Models Object-Centric Representation Learning Model Predictive Control Robotic Manipulation

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Predictive world models enable agents to model scene dynamics and reason about the consequences of their actions. Inspired by human perception, object-centric world models capture scene dynamics using object-level representations, which can be used for downstream applications such as action planning. However, most object-centric world models and reinforcement learning (RL) approaches learn reactive policies that are fixed at inference time, limiting generalization to novel situations. We propose Slot-MPC, an object-centric world modeling framework that enables planning through Model Predictive Control (MPC). Slot-MPC leverages vision encoders to learn slot-based representations, which encode individual objects in the scene, and uses these structured representations to learn an action-conditioned object-centric dynamics model. At inference time, the learned dynamics model enables action planning via MPC, allowing agents to adapt to previously unseen situations. Since the learned world model is differentiable, we can use gradient-based MPC to directly optimize actions, which is computationally more efficient than relying on gradient-free, sampling-based MPC methods. Experiments on simulated robotic manipulation tasks show that Slot-MPC improves both task performance and planning efficiency compared to non-object-centric world model baselines. In the considered offline setting with limited state-action coverage, we find that gradient-based MPC performs better than gradient-free, sampling-based MPC. Our results demonstrate that explicitly structured, object-centric representations provide a strong inductive bias for controllable and generalizable decision-making. Code and additional results are available at https://slot-mpc.github.io.