Robot Manipulation and Learning

Latest papers

• Generalizable Geometric Prior and Recurrent Spiking Feature Learning for Humanoid Robot Manipulation

  Xuetao Li, Wenke Huang, Mang Ye, Jifeng Xuan, Bo Du, Sheng Liu, Miao Li · 15 January 2026

  Humanoid robot manipulation is a crucial research area for executing diverse human-level tasks, involving high-level semantic reasoning and low-level action generation. However, precise scene understanding and sample-efficient learning from human demonstrations remain critical challenges, severely h…

• MimicKit: A Reinforcement Learning Framework for Motion Imitation and Control

  Xue Bin Peng · 13 January 2026

  MimicKit is an open-source framework for training motion controllers using motion imitation and reinforcement learning. The codebase provides implementations of commonly-used motion-imitation techniques and RL algorithms. This framework is intended to support research and applications in computer gr…

• MixRI: Mixing Features of Reference Images for Novel Object Pose Estimation

  Xinhang Liu, Jiawei Shi, Zheng Dang, Yuchao Dai · 13 January 2026

  We present MixRI, a lightweight network that solves the CAD-based novel object pose estimation problem in RGB images. It can be instantly applied to a novel object at test time without finetuning. We design our network to meet the demands of real-world applications, emphasizing reduced memory requir…

• DexterCap: An Affordable and Automated System for Capturing Dexterous Hand-Object Manipulation

  Yutong Liang, Shiyi Xu, Yulong Zhang, Bowen Zhan, He Zhang, Libin Liu · 12 January 2026

  Capturing fine-grained hand-object interactions is challenging due to severe self-occlusion from closely spaced fingers and the subtlety of in-hand manipulation motions. Existing optical motion capture systems rely on expensive camera setups and extensive manual post-processing, while low-cost visio…

• Goal Force: Teaching Video Models To Accomplish Physics-Conditioned Goals

  Nate Gillman, Yinghua Zhou, Zitian Tang, Evan Luo, Arjan Chakravarthy, Daksh Aggarwal, Michael Freeman, Charles Herrmann, Chen Sun · 12 January 2026

  Recent advancements in video generation have enabled the development of ``world models'' capable of simulating potential futures for robotics and planning. However, specifying precise goals for these models remains a challenge; text instructions are often too abstract to capture physical nuances, wh…

• Grasp the Graph (GtG) 2.0: Ensemble of Graph Neural Networks for High-Precision Grasp Pose Detection in Clutter

  Ali Rashidi Moghadam, Sayedmohammadreza Rastegari, Mehdi Tale Masouleh, Ahmad Kalhor · 12 January 2026

  Grasp pose detection in cluttered, real-world environments remains a significant challenge due to noisy and incomplete sensory data combined with complex object geometries. This paper introduces Grasp the Graph 2.0 (GtG 2.0) method, a lightweight yet highly effective hypothesis-and-test robotics gra…

• UNIC: Learning Unified Multimodal Extrinsic Contact Estimation

  Zhengtong Xu, Yuki Shirai · 9 January 2026

  Contact-rich manipulation requires reliable estimation of extrinsic contacts-the interactions between a grasped object and its environment which provide essential contextual information for planning, control, and policy learning. However, existing approaches often rely on restrictive assumptions, su…

• Actively Obtaining Environmental Feedback for Autonomous Action Evaluation Without Predefined Measurements

  Hong Su · 9 January 2026

  Obtaining reliable feedback from the environment is a fundamental capability for intelligent agents to evaluate the correctness of their actions and to accumulate reusable knowledge. However, most existing approaches rely on predefined measurements or fixed reward signals, which limits their applica…

• LinkD: AutoRegressive Diffusion Model for Mechanical Linkage Synthesis

  Yayati Jadhav, Amir Barati Farimani · 8 January 2026

  Designing mechanical linkages to achieve target end-effector trajectories presents a fundamental challenge due to the intricate coupling between continuous node placements, discrete topological configurations, and nonlinear kinematic constraints. The highly nonlinear motion-to-configuration relation…

• Physics-Driven Data Generation for Contact-Rich Manipulation via Trajectory Optimization

  Lujie Yang, H. J. Terry Suh, Tong Zhao, Bernhard Paus Graesdal, Tarik Kelestemur, Jiuguang Wang, Tao Pang, Russ Tedrake · 8 January 2026

  We present a low-cost data generation pipeline that integrates physics-based simulation, human demonstrations, and model-based planning to efficiently generate large-scale, high-quality datasets for contact-rich robotic manipulation tasks. Starting with a small number of embodiment-flexible human de…

• Chain-of-Action: Trajectory Autoregressive Modeling for Robotic Manipulation

  Wenbo Zhang, Tianrun Hu, Hanbo Zhang, Yanyuan Qiao, Yuchu Qin, Yang Li, Jiajun Liu, Tao Kong, Lingqiao Liu, Xiao Ma · 7 January 2026

  We present Chain-of-Action (CoA), a novel visuo-motor policy paradigm built upon Trajectory Autoregressive Modeling. Unlike conventional approaches that predict next step action(s) forward, CoA generates an entire trajectory by explicit backward reasoning with task-specific goals through an action-l…

• Movement Primitives in Robotics: A Comprehensive Survey

  Nolan B. Gutierrez, William J. Beksi · 7 January 2026

  Biological systems exhibit a continuous stream of movements, consisting of sequential segments, that allow them to perform complex tasks in a creative and versatile fashion. This observation has led researchers towards identifying elementary building blocks of motion known as movement primitives, wh…

• Closing the Reality Gap: Zero-Shot Sim-to-Real Deployment for Dexterous Force-Based Grasping and Manipulation

  Haoyu Dong, Zhengmao He, Yang Li, Zhibin Li, Xinyu Yi, Zhe Zhao · 7 January 2026

  Human-like dexterous hands with multiple fingers offer human-level manipulation capabilities, but training control policies that can directly deploy on real hardware remains difficult due to contact-rich physics and imperfect actuation. We close this gap with a practical sim-to-real reinforcement le…

• Online Estimation and Manipulation of Articulated Objects

  Russell Buchanan, Adrian R\"ofer, Jo\~ao Moura, Abhinav Valada, Sethu Vijayakumar · 6 January 2026

  From refrigerators to kitchen drawers, humans interact with articulated objects effortlessly every day while completing household chores. For automating these tasks, service robots must be capable of manipulating arbitrary articulated objects. Recent deep learning methods have been shown to predict …

• Value Vision-Language-Action Planning & Search

  Ali Salamatian (Steve), Ke (Steve), Ren, Kieran Pattison, Cyrus Neary · 6 January 2026

  Vision-Language-Action (VLA) models have emerged as powerful generalist policies for robotic manipulation, yet they remain fundamentally limited by their reliance on behavior cloning, leading to brittleness under distribution shift. While augmenting pretrained models with test-time search algorithms…

• Affordance-Guided Coarse-to-Fine Exploration for Base Placement in Open-Vocabulary Mobile Manipulation

  Tzu-Jung Lin, Jia-Fong Yeh, Hung-Ting Su, Chung-Yi Lin, Yi-Ting Chen, Winston H. Hsu · 6 January 2026

  In open-vocabulary mobile manipulation (OVMM), task success often hinges on the selection of an appropriate base placement for the robot. Existing approaches typically navigate to proximity-based regions without considering affordances, resulting in frequent manipulation failures. We propose Afforda…

• Coordinated Humanoid Manipulation with Choice Policies

  Haozhi Qi, Yen-Jen Wang, Toru Lin, Brent Yi, Yi Ma, Koushil Sreenath, Jitendra Malik · 1 January 2026

  Humanoid robots hold great promise for operating in human-centric environments, yet achieving robust whole-body coordination across the head, hands, and legs remains a major challenge. We present a system that combines a modular teleoperation interface with a scalable learning framework to address t…

• ShowUI-$\pi$: Flow-based Generative Models as GUI Dexterous Hands

  Siyuan Hu, Kevin Qinghong Lin, Mike Zheng Shou · 1 January 2026

  Building intelligent agents capable of dexterous manipulation is essential for achieving human-like automation in both robotics and digital environments. However, existing GUI agents rely on discrete click predictions (x,y), which prohibits free-form, closed-loop trajectories (e.g. dragging a progre…

• Dream2Flow: Bridging Video Generation and Open-World Manipulation with 3D Object Flow

  Karthik Dharmarajan, Wenlong Huang, Jiajun Wu, Li Fei-Fei, Ruohan Zhang · 1 January 2026

  Generative video modeling has emerged as a compelling tool to zero-shot reason about plausible physical interactions for open-world manipulation. Yet, it remains a challenge to translate such human-led motions into the low-level actions demanded by robotic systems. We observe that given an initial i…

• Act2Goal: From World Model To General Goal-conditioned Policy

  Pengfei Zhou, Liliang Chen, Shengcong Chen, Di Chen, Wenzhi Zhao, Rongjun Jin, Guanghui Ren, Jianlan Luo · 30 December 2025

  Specifying robotic manipulation tasks in a manner that is both expressive and precise remains a central challenge. While visual goals provide a compact and unambiguous task specification, existing goal-conditioned policies often struggle with long-horizon manipulation due to their reliance on single…

• ReSemAct: Advancing Fine-Grained Robotic Manipulation via Semantic Structuring and Affordance Refinement

  Chenyu Su, Weiwei Shang, Chen Qian, Fei Zhang, Shuang Cong · 30 December 2025

  Fine-grained robotic manipulation requires grounding natural language into appropriate affordance targets. However, most existing methods driven by foundation models often compress rich semantics into oversimplified affordances, preventing exploitation of implicit semantic information. To address th…

• Learning Skills from Action-Free Videos

  Hung-Chieh Fang, Kuo-Han Hung, Chu-Rong Chen, Po-Jung Chou, Chun-Kai Yang, Po-Chen Ko, Yu-Chiang Wang, Yueh-Hua Wu, Min-Hung Chen, Shao-Hua Sun · 24 December 2025

  Learning from videos offers a promising path toward generalist robots by providing rich visual and temporal priors beyond what real robot datasets contain. While existing video generative models produce impressive visual predictions, they are difficult to translate into low-level actions. Conversely…

• Translating Flow to Policy via Hindsight Online Imitation

  Yitian Zheng, Zhangchen Ye, Weijun Dong, Shengjie Wang, Yuyang Liu, Chongjie Zhang, Chuan Wen, Yang Gao · 23 December 2025

  Recent advances in hierarchical robot systems leverage a high-level planner to propose task plans and a low-level policy to generate robot actions. This design allows training the planner on action-free or even non-robot data sources (e.g., videos), providing transferable high-level guidance. Nevert…

• AnyTask: an Automated Task and Data Generation Framework for Advancing Sim-to-Real Policy Learning

  Ran Gong, Xiaohan Zhang, Jinghuan Shang, Maria Vittoria Minniti, Jigarkumar Patel, Valerio Pepe, Riedana Yan, Ahmet Gundogdu, Ivan Kapelyukh, Ali Abbas, Xiaoqiang Yan, Harsh Patel, Laura Herlant, Karl Schmeckpeper · 22 December 2025

  Generalist robot learning remains constrained by data: large-scale, diverse, and high-quality interaction data are expensive to collect in the real world. While simulation has become a promising way for scaling up data collection, the related tasks, including simulation task design, task-aware scene…

• ReinforceGen: Hybrid Skill Policies with Automated Data Generation and Reinforcement Learning

  Zihan Zhou, Animesh Garg, Ajay Mandlekar, Caelan Garrett · 19 December 2025

  Long-horizon manipulation has been a long-standing challenge in the robotics community. We propose ReinforceGen, a system that combines task decomposition, data generation, imitation learning, and motion planning to form an initial solution, and improves each component through reinforcement-learning…