Robot Manipulation and Learning

Latest papers

• Information-Theoretic Graph Fusion with Vision-Language-Action Model for Policy Reasoning and Dual Robotic Control

  Shunlei Li, Longsen Gao, Jin Wang, Chang Che, Xi Xiao, Jiuwen Cao, Yingbai Hu, Hamid Reza Karimi · 10 February 2026

  Teaching robots dexterous skills from human videos remains challenging due to the reliance on low-level trajectory imitation, which fails to generalize across object types, spatial layouts, and manipulator configurations. We propose Graph-Fused Vision-Language-Action (GF-VLA), a framework that enabl…

• STEP: Warm-Started Visuomotor Policies with Spatiotemporal Consistency Prediction

  Jinhao Li, Yuxuan Cong, Yingqiao Wang, Hao Xia, Shan Huang, Yijia Zhang, Ningyi Xu, Guohao Dai · 10 February 2026

  Diffusion policies have recently emerged as a powerful paradigm for visuomotor control in robotic manipulation due to their ability to model the distribution of action sequences and capture multimodality. However, iterative denoising leads to substantial inference latency, limiting control frequency…

• Learning Human-Like Badminton Skills for Humanoid Robots

  Yeke Chen, Shihao Dong, Xiaoyu Ji, Jingkai Sun, Zeren Luo, Liu Zhao, Jiahui Zhang, Wanyue Li, Ji Ma, Bowen Xu, Yimin Han, Yudong Zhao, Peng Lu · 10 February 2026

  Realizing versatile and human-like performance in high-demand sports like badminton remains a formidable challenge for humanoid robotics. Unlike standard locomotion or static manipulation, this task demands a seamless integration of explosive whole-body coordination and precise, timing-critical inte…

• Coupled Local and Global World Models for Efficient First Order RL

  Joseph Amigo, Rooholla Khorrambakht, Nicolas Mansard, Ludovic Righetti · 9 February 2026

  World models offer a promising avenue for more faithfully capturing complex dynamics, including contacts and non-rigidity, as well as complex sensory information, such as visual perception, in situations where standard simulators struggle. However, these models are computationally complex to evaluat…

• Humanoid Manipulation Interface: Humanoid Whole-Body Manipulation from Robot-Free Demonstrations

  Ruiqian Nai, Boyuan Zheng, Junming Zhao, Haodong Zhu, Sicong Dai, Zunhao Chen, Yihang Hu, Yingdong Hu, Tong Zhang, Chuan Wen, Yang Gao · 9 February 2026

  Current approaches for humanoid whole-body manipulation, primarily relying on teleoperation or visual sim-to-real reinforcement learning, are hindered by hardware logistics and complex reward engineering. Consequently, demonstrated autonomous skills remain limited and are typically restricted to con…

• Learning to Plan & Schedule with Reinforcement-Learned Bimanual Robot Skills

  Weikang Wan, Fabio Ramos, Xuning Yang, Caelan Garrett · 6 February 2026

  Long-horizon contact-rich bimanual manipulation presents a significant challenge, requiring complex coordination involving a mixture of parallel execution and sequential collaboration between arms. In this paper, we introduce a hierarchical framework that frames this challenge as an integrated skill…

• Differentiable Inverse Graphics for Zero-shot Scene Reconstruction and Robot Grasping

  Octavio Arriaga, Proneet Sharma, Jichen Guo, Marc Otto, Siddhant Kadwe, Rebecca Adam · 6 February 2026

  Operating effectively in novel real-world environments requires robotic systems to estimate and interact with previously unseen objects. Current state-of-the-art models address this challenge by using large amounts of training data and test-time samples to build black-box scene representations. In t…

• Internalizing LLM Reasoning via Discovery and Replay of Latent Actions

  Zhenning Shi, Yijia Zhu, Junhan Shi, Xun Zhang, Lei Wang, Congcong Miao · 6 February 2026

  The internalization of chain-of-thought processes into hidden states has emerged as a highly efficient paradigm for scaling test-time compute. However, existing activation steering methods rely on static control vectors that fail to adapt to the non-stationary evolution of complex reasoning tasks. T…

• Geometry-aware 4D Video Generation for Robot Manipulation

  Zeyi Liu, Shuang Li, Eric Cousineau, Siyuan Feng, Benjamin Burchfiel, Shuran Song · 4 February 2026

  Understanding and predicting dynamics of the physical world can enhance a robot's ability to plan and interact effectively in complex environments. While recent video generation models have shown strong potential in modeling dynamic scenes, generating videos that are both temporally coherent and geo…

• Towards Exploratory and Focused Manipulation with Bimanual Active Perception: A New Problem, Benchmark and Strategy

  Yuxin He, Ruihao Zhang, Tianao Shen, Cheng Liu, Qiang Nie · 3 February 2026

  Recently, active vision has reemerged as an important concept for manipulation, since visual occlusion occurs more frequently when main cameras are mounted on the robot heads. We reflect on the visual occlusion issue and identify its essence as the absence of information useful for task completion. …

• SoMA: A Real-to-Sim Neural Simulator for Robotic Soft-body Manipulation

  Mu Huang, Hui Wang, Kerui Ren, Linning Xu, Yunsong Zhou, Mulin Yu, Bo Dai, Jiangmiao Pang · 3 February 2026

  Simulating deformable objects under rich interactions remains a fundamental challenge for real-to-sim robot manipulation, with dynamics jointly driven by environmental effects and robot actions. Existing simulators rely on predefined physics or data-driven dynamics without robot-conditioned control,…

• Bridging the Sim-to-Real Gap with multipanda ros2: A Real-Time ROS2 Framework for Multimanual Systems

  Jon \v{S}kerlj, Seongjin Bien, Abdeldjallil Naceri, Sami Haddadin · 3 February 2026

  We present $multipanda\_ros2$, a novel open-source ROS2 architecture for multi-robot control of Franka Robotics robots. Leveraging ros2 control, this framework provides native ROS2 interfaces for controlling any number of robots from a single process. Our core contributions address key challenges in…

• HumanX: Toward Agile and Generalizable Humanoid Interaction Skills from Human Videos

  Yinhuai Wang, Qihan Zhao, Yuen Fui Lau, Runyi Yu, Hok Wai Tsui, Qifeng Chen, Jingbo Wang, Jiangmiao Pang, Ping Tan · 3 February 2026

  Enabling humanoid robots to perform agile and adaptive interactive tasks has long been a core challenge in robotics. Current approaches are bottlenecked by either the scarcity of realistic interaction data or the need for meticulous, task-specific reward engineering, which limits their scalability. …

• Offline Discovery of Interpretable Skills from Multi-Task Trajectories

  Chongyu Zhu, Mithun Vanniasinghe, Jiayu Chen, Chi-Guhn Lee · 3 February 2026

  Hierarchical Imitation Learning is a powerful paradigm for acquiring complex robot behaviors from demonstrations. A central challenge, however, lies in discovering reusable skills from long-horizon, multi-task offline data, especially when the data lacks explicit rewards or subtask annotations. In t…

• CLAMP: Contrastive Learning for 3D Multi-View Action-Conditioned Robotic Manipulation Pretraining

  I-Chun Arthur Liu, Krzysztof Choromanski, Sandy Huang, Connor Schenck · 3 February 2026

  Leveraging pre-trained 2D image representations in behavior cloning policies has achieved great success and has become a standard approach for robotic manipulation. However, such representations fail to capture the 3D spatial information about objects and scenes that is essential for precise manipul…

• PRISM: Performer RS-IMLE for Single-pass Multisensory Imitation Learning

  Amisha Bhaskar, Pratap Tokekar, Stefano Di Cairano, Alexander Schperberg · 3 February 2026

  Robotic imitation learning typically requires models that capture multimodal action distributions while operating at real-time control rates and accommodating multiple sensing modalities. Although recent generative approaches such as diffusion models, flow matching, and Implicit Maximum Likelihood E…

• Hypersolid: Emergent Vision Representations via Short-Range Repulsion

  Esteban Rodr\'iguez-Betancourt, Edgar Casasola-Murillo · 30 January 2026

  A recurring challenge in self-supervised learning is preventing representation collapse. Existing solutions typically rely on global regularization, such as maximizing distances, decorrelating dimensions or enforcing certain distributions. We instead reinterpret representation learning as a discrete…

• Task-Centric Policy Optimization from Misaligned Motion Priors

  Ziang Zheng, Kai Feng, Yi Nie, Shentao Qin · 28 January 2026

  Humanoid control often leverages motion priors from human demonstrations to encourage natural behaviors. However, such demonstrations are frequently suboptimal or misaligned with robotic tasks due to embodiment differences, retargeting errors, and task-irrelevant variations, causing na\"ive imitatio…

• PEAfowl: Perception-Enhanced Multi-View Vision-Language-Action for Bimanual Manipulation

  Qingyu Fan, Zhaoxiang Li, Yi Lu, Wang Chen, Qiu Shen, Xiao-xiao Long, Yinghao Cai, Tao Lu, Shuo Wang, Xun Cao · 27 January 2026

  Bimanual manipulation in cluttered scenes requires policies that remain stable under occlusions, viewpoint and scene variations. Existing vision-language-action models often fail to generalize because (i) multi-view features are fused via view-agnostic token concatenation, yielding weak 3D-consisten…

• DeltaDorsal: Enhancing Hand Pose Estimation with Dorsal Features in Egocentric Views

  William Huang, Siyou Pei, Leyi Zou, Eric J. Gonzalez, Ishan Chatterjee, Yang Zhang · 23 January 2026

  The proliferation of XR devices has made egocentric hand pose estimation a vital task, yet this perspective is inherently challenged by frequent finger occlusions. To address this, we propose a novel approach that leverages the rich information in dorsal hand skin deformation, unlocked by recent adv…

• ForeDiffusion: Foresight-Conditioned Diffusion Policy via Future View Construction for Robot Manipulation

  Weize Xie, Yi Ding, Ying He, Leilei Wang, Binwen Bai, Zheyi Zhao, Chenyang Wang, F. Richard Yu · 21 January 2026

  Diffusion strategies have advanced visual motor control by progressively denoising high-dimensional action sequences, providing a promising method for robot manipulation. However, as task complexity increases, the success rate of existing baseline models decreases considerably. Analysis indicates th…

• Beyond Task and Motion Planning: Hierarchical Robot Planning with General-Purpose Skills

  Benned Hedegaard, Yichen Wei, Ahmed Jaafar, Stefanie Tellex, George Konidaris, Naman Shah · 21 January 2026

  Task and motion planning is a well-established approach for solving long-horizon robot planning problems. However, traditional methods assume that each task-level robot action, or skill, can be reduced to kinematic motion planning. We address the challenge of combining motion planning with closed-lo…

• Shape Completion with Prediction of Uncertain Regions

  Matthias Humt, Dominik Winkelbauer, Ulrich Hillenbrand · 21 January 2026

  Shape completion, i.e., predicting the complete geometry of an object from a partial observation, is highly relevant for several downstream tasks, most notably robotic manipulation. When basing planning or prediction of real grasps on object shape reconstruction, an indication of severe geometric un…

• Combining Shape Completion and Grasp Prediction for Fast and Versatile Grasping with a Multi-Fingered Hand

  Matthias Humt, Dominik Winkelbauer, Ulrich Hillenbrand, Berthold B\"auml · 21 January 2026

  Grasping objects with limited or no prior knowledge about them is a highly relevant skill in assistive robotics. Still, in this general setting, it has remained an open problem, especially when it comes to only partial observability and versatile grasping with multi-fingered hands. We present a nove…

• Learning Semantic-Geometric Task Graph-Representations from Human Demonstrations

  Franziska Herbert, Vignesh Prasad, Han Liu, Dorothea Koert, Georgia Chalvatzaki · 19 January 2026

  Learning structured task representations from human demonstrations is essential for understanding long-horizon manipulation behaviors, particularly in bimanual settings where action ordering, object involvement, and interaction geometry can vary significantly. A key challenge lies in jointly capturi…