Generalization in Robotic Manipulation

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48 papers in the last four weeks, up 500% on the four weeks before. 0.5% of all new papers.

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Apr 16, 2026cs.RO

ShapeGen: Robotic Data Generation for Category-Level Manipulation

Manipulation policies deployed in uncontrolled real-world scenarios are faced with great in-category geometric diversity of everyday objects. In order to function robustly under such variations, policies need to work in a category-level manner, i.e. knowing how to interact with any object in a certain category, instead of only a specific one seen during training. This in-category generalizability is usually nurtured with shape-diversified training data; however, manually collecting such a corpus of data is infeasible due to the requirement of intense human labor and large collections of divergent objects at hand. In this paper, we propose ShapeGen, a data generation method that aims at generating shape-variated manipulation data in a simulator-free and 3D manner. ShapeGen decomposes the process into two stages: Shape Library curation and Function-Aware Generation. In the first stage, we train spatial warpings between shapes mapping points to points that correspond functionally, and aggregate 3D models along with the warpings into a plug-and-play Shape Library. In the second stage, we design a pipeline that, leveraging established Libraries, requires only minimal human annotation to generate physically plausible and functionally correct novel demonstrations. Experiments in the real world demonstrate the effectiveness of ShapeGen to boost policies' in-category shape generalizability. Project page: https://wangyr22.github.io/ShapeGen/.

Yirui Wang, Xiuwei Xu, Angyuan Ma +3

Tsinghua University

Robotic Data Collection Generalization in Robotic Manipulation Synthetic Data Generation for Robotics Robotic Manipulation Robot Policy Generalization

Apr 16, 2026cs.LG

$π_{0.7}$ : a Steerable Generalist Robotic Foundation Model with Emergent Capabilities

We present a new robotic foundation model, called

π_{0.7}

, that can enable strong out-of-the-box performance in a wide range of scenarios.

π_{0.7}

can follow diverse language instructions in unseen environments, including multi-stage tasks with various kitchen appliances, provide zero-shot cross-embodiment generalization, for example enabling a robot to fold laundry without seeing the task before, and perform challenging tasks such as operating an espresso machine out of the box at a level of performance that matches much more specialized RL-finetuned models. The main idea behind

π_{0.7}

is to use diverse context conditioning during training. This conditioning information, contained in the prompt, makes it possible to steer the model precisely to perform many tasks with different strategies. It is conditioned not just on a language command that describes what it should do, but on additional multimodal information that also describes the manner or strategy in which it should do it, including metadata about task performance and subgoal images. This enables

π_{0.7}

to use very diverse data, including demonstrations, potentially suboptimal (autonomous) data including failures, and data from non-robot sources. Our experiments evaluate

π_{0.7}

across numerous tasks with multiple robot platforms, on tasks that require speed and dexterity, language following, and compositional task generalization.

Physical Intelligence, Bo Ai, Ali Amin +85

Robot Policy Learning Robot Skill Learning Language-Conditioned Robot Manipulation Robot Foundation Models Generalization in Robotic Manipulation Cross-Embodiment Robot Learning+2

Apr 16, 2026cs.RO

One-Shot Cross-Geometry Skill Transfer through Part Decomposition

Given a demonstration, a robot should be able to generalize a skill to any object it encounters-but existing approaches to skill transfer often fail to adapt to objects with unfamiliar shapes. Motivated by examples of improved transfer from compositional modeling, we propose a method for improving transfer by decomposing objects into their constituent semantic parts. We leverage data-efficient generative shape models to accurately transfer interaction points from the parts of a demonstration object to a novel object. We autonomously construct an objective to optimize the alignment of those points on skill-relevant object parts. Our method generalizes to a wider range of object geometries than existing work, and achieves successful one-shot transfer for a range of skills and objects from a single demonstration, in both simulated and real environments.

Skye Thompson, Ondrej Biza, George Konidaris

Brown University · Northeastern University

Robot Skill Learning Generalization in Robotic Manipulation Robotic Manipulation Robot Imitation Learning

Apr 16, 2026cs.RO

DockAnywhere: Data-Efficient Visuomotor Policy Learning for Mobile Manipulation via Novel Demonstration Generation

Mobile manipulation is a fundamental capability that enables robots to interact in expansive environments such as homes and factories. Most existing approaches follow a two-stage paradigm, where the robot first navigates to a docking point and then performs fixed-base manipulation using powerful visuomotor policies. However, real-world mobile manipulation often suffers from the view generalization problem due to shifts of docking points. To address this issue, we propose a novel low-cost demonstration generation framework named DockAnywhere, which improves viewpoint generalization under docking variability by lifting a single demonstration to diverse feasible docking configurations. Specifically, DockAnywhere lifts a trajectory to any feasible docking points by decoupling docking-dependent base motions from contact-rich manipulation skills that remain invariant across viewpoints. Feasible docking proposals are sampled under feasibility constraints, and corresponding trajectories are generated via structure-preserving augmentation. Visual observations are synthesized in 3D space by representing the robot and objects as point clouds and applying point-level spatial editing to ensure the consistency of observation and action across viewpoints. Extensive experiments on ManiSkill and real-world platforms demonstrate that DockAnywhere substantially improves policy success rates and easily generalizes to novel viewpoints from unseen docking points during training, significantly enhancing the generalization capability of mobile manipulation policy in real-world deployment.

Ziyu Shan, Yuheng Zhou, Gaoyuan Wu +3

Beijing University of Posts and Telecommunications, Beijing, China · Nanyang Technological University, Singapore

Mobile Manipulation Generalization in Robotic Manipulation Visuomotor Policy Learning Synthetic Data Generation for Robotics Robotic Manipulation Robot Policy Generalization

Apr 13, 2026cs.RO

Grounded World Model: Latent Planning with Language Goals

World models such as DINO-WM and LeWM specify the goal with an image, which is difficult to obtain in advance for novel tasks. We present the Grounded World Model (GWM), a latent world model that enables zero-shot planning in the real world from language goals alone. Given a candidate action sequence and the current observation, GWM predicts the future in the visual space of a pretrained video-language embedding model. The frozen readout of this embedding model maps this imagined future and the task description into the same embedding space, where their negative cosine similarity serves as the planning cost. Training GWM requires only offline and task-agnostic video-action pairs and no language labels. In simulated experiments on WISER, planning with GWM, which executes the candidate action of lowest cost, solves 87% of 288 tasks with unseen instructions and visual signals, while ten fine-tuned VLAs average 22%. We then scale GWM up with real robot data, and use it for zero-shot planning in realistic simulation and real scenes. In the IsaacSim evaluation, planning with GWM completes all 70 trials across 14 tasks that require reasoning over referring expressions, matching a modular planner grounded by a frontier VLM, while pi0.5 reaches 37/70. Deployed on a real Franka, the same stack completes 55/60 separately evaluated pick-and-place sub-tasks, comparable to the modular planner's 52/60, with the full system running locally on a single consumer GPU. Project website: https://quanyili.github.io/gwm-wiser/.

Quanyi Li, Lan Feng, Haonan Zhang +4

EPFL · NUS

Zero-Shot Robotic Manipulation World Models for Robotics World Model-Based Planning Action-Conditioned World Models Generalization in Robotic Manipulation Vision-Language Grounding+5

Apr 9, 2026cs.RO

Sumo: Dynamic and Generalizable Whole-Body Loco-Manipulation

This paper presents a sim-to-real approach that enables legged robots to dynamically manipulate large and heavy objects with whole-body dexterity. Our key insight is that by performing test-time steering of a pre-trained whole-body control policy with a sample-based planner, we can enable these robots to solve a variety of dynamic loco-manipulation tasks. Interestingly, we find our method generalizes to a diverse set of objects and tasks with no additional tuning or training, and can be further enhanced by flexibly adjusting the cost function at test time. We demonstrate the capabilities of our approach through a variety of challenging loco-manipulation tasks on a Spot quadruped robot in the real world, including uprighting a tire heavier than the robot's nominal lifting capacity and dragging a crowd-control barrier larger and taller than the robot itself. Additionally, we show that the same approach can be generalized to humanoid loco-manipulation tasks, such as opening a door and pushing a table, in simulation. Project code and videos are available at https://sumo.rai-inst.com/.

John Z. Zhang, Maks Sorokin, Jan Brüdigam +14

MIT · RAI Institute · Cornell University

Generalization in Robotic Manipulation Whole-Body Robotic Manipulation Robotic Manipulation Dynamic Robotic Manipulation

Apr 6, 2026cs.RO

Veo-Act: Enhancing VLA Policies with Frontier Video Models

Video generation models can produce coherent vi- sual sequences depicting object motion and interactions. We in- vestigate how frontier video generation models can complement vision-language-action policies to enhance generalizable robotic manipulation. VLA policies have become a dominant paradigm for robot learning, but their action-oriented adaptation of pretrained VLMs can weaken semantic generalization, limiting robustness in ambiguous or out-of-distribution manipulation scenarios. We use video models as visual planners, motivated by their potential to generalize across complex scenes and their priors over hand motion. However, manipulation methods based on video models often lack the precision and temporal responsiveness needed for low-level dexterous interaction. To address this gap, we present Veo-Act, a hierarchical framework with Veo-3.1 as a high-level motion planner and a VLA policy as the low-level executor. A multi-head inverse dynamics model converts generated frame pairs into actions and learns an interaction gate to trigger the handoff to reactive VLA control. Experiments in simulation and on a real robot show improved instruction following and overall task success over the baseline VLA in novel and semantically complex manipulation settings, supporting the complementary roles of video planning and reactive interaction.

Zhongru Zhang, Chenghan Yang, Qingzhou Lu +4

Tsinghua University

Robotic Control Language-Conditioned Robot Manipulation Generalization in Robotic Manipulation Vision-Language-Action Models Robot Policy Generalization

Apr 1, 2026cs.RO

Planning for Change: Reinforcement Learning Combined with Bounded Extremum Seeking for Robotic Control under Distribution Shift

Reinforcement learning has shown strong performance in robotic manipulation, but learned policies often degrade in performance when test conditions differ from the training distribution. This limitation is especially important for reliable robot planning and control in contact-rich tasks such as pushing and pick-and-place, where changes in goals, contact conditions, or robot dynamics can drive the system out-of-distribution at inference time. In this paper, we investigate a hybrid controller that combines reinforcement learning with bounded extremum seeking (ES) to improve robustness under such conditions. In the proposed approach, deep deterministic policy gradient (DDPG) policies are trained under standard conditions on the robotic pushing and pick-and-place tasks, and are then combined with bounded ES during deployment. The RL policy provides fast manipulation behavior, while bounded ES ensures robustness of the overall controller to time variations when operating conditions depart from those seen during training. The resulting controller is evaluated under several out-of-distribution settings, including time-varying goals and spatially varying friction patches. Crucially, under reasonable assumptions, we prove finite time RL-based convergence of a robotic arm to an object after which bounded ES achieves finite-time convergence to a bounded velocity moving target. We provide analytic formulas for both convergence times, and an ability to tune the convergence times by our choice of gains.

Shaifalee Saxena, Rafael Fierro, Alexander Scheinker

Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87106, USA · Los Alamos National Lab, Los Alamos, NM 87547, USA

Robotic Control Reinforcement Learning Contact-Rich Robotic Manipulation Generalization in Robotic Manipulation Feedback Control Robust RL

Mar 30, 2026cs.RO

ManipArena: Comprehensive Real-world Evaluation of Reasoning-Oriented Generalist Robot Manipulation

Vision-Language-Action (VLA) models and world-action models have emerged as central paradigms for general-purpose robotic intelligence, yet their empirical progress remains constrained by the absence of evaluation protocols that are both physically realistic and diagnostically controlled. Simulator-centric benchmarks provide scale and reproducibility, but cannot fully capture the reality gap induced by perception noise, contact dynamics, latency, calibration error, and hardware constraints. Conversely, real-robot evaluations are often fragmented across platforms, scenes, objects, and scoring rules, making fair comparison and failure attribution difficult. We introduce ManipArena, a standardized real-robot evaluation framework for studying manipulation generalization under matched physical conditions. ManipArena comprises 20 tasks, 10,812 expert trajectories, 13.5M frames, and approximately 188 robot hours across tabletop and mobile manipulation. The framework combines schema-defined task variation, stratified in-domain, visualshift, and semantic-OOD trials, subtask-level partial-credit scoring, three-level language annotations, low-level motor signals, and paired real-to-sim environments reconstructed from physical scenes. Using ManipArena, we evaluate seven tabletop configurations spanning VLA and world-action-model policies. The results show that real-robot conclusions depend not only on architecture, but also on model provenance, fine-tuning regime, data sampling, and annotation granularity. ManipArena thus provides a reproducible and interpretable foundation for diagnosing capability boundaries and failure modes in embodied generalization.

Yu Sun, Meng Cao, Yang Ping +24

Sun Yat-sen University · X Square Robot · MBZUAI +2

Generalization in Robotic Manipulation Robotic Manipulation Robot Manipulation Benchmarks VLMs for Robotics

Mar 24, 2026cs.RO

Grounding Sim-to-Real Generalization in Robotic Manipulation: An Empirical Study with Vision-Language-Action Models

Learning a generalist control policy for robotic manipulation typically relies on large-scale datasets. Given the high cost of real-world data collection, a practical alternative is to generate synthetic data through simulation. However, the resulting synthetic data often exhibits a significant gap from real-world distributions. While many prior studies have proposed algorithms to bridge the Sim-to-Real discrepancy, there remains a lack of principled research that grounds these methods in real-world manipulation tasks, particularly their performance on generalist policies such as Vision-Language-Action (VLA) models. In this study, we empirically examine the primary determinants of Sim-to-Real generalization across four dimensions: multi-level domain randomization, photorealistic rendering, physics-realistic modeling, and reinforcement learning updates. To support this study, we design a comprehensive evaluation protocol to quantify the real-world performance of manipulation tasks. The protocol accounts for key variations in background, lighting, distractors, object types, and spatial features. Through experiments involving over 10k real-world trials, we derive critical insights into Sim-to-Real transfer. To inform and advance future studies, we release both the robotic platforms and the evaluation protocol for public access to facilitate independent verification, thereby establishing a realistic and standardized benchmark for robotic manipulation policies.

Ruixing Jin, Zicheng Zhu, Ruixiang Ouyang +4

School of Data Science, The Chinese University of Hong Kong, Shenzhen · Shenzhen Loop Area Institute

Domain Randomization Robotics Simulation Sim-to-Real Transfer Generalization in Robotic Manipulation Robotic Manipulation Robot Manipulation Benchmarks+1

Mar 16, 2026cs.CV

Towards Generalizable Robotic Manipulation in Dynamic Environments

Vision-Language-Action (VLA) models excel in static manipulation but struggle in dynamic environments with moving targets. This performance gap primarily stems from a scarcity of dynamic manipulation datasets and the reliance of mainstream VLAs on single-frame observations, restricting their spatiotemporal reasoning capabilities. To address this, we introduce DOMINO, a large-scale dataset and benchmark for generalizable dynamic manipulation, featuring 35 tasks with hierarchical complexities, over 110K expert trajectories, and a multi-dimensional evaluation suite. Through comprehensive experiments, we systematically evaluate existing VLAs on dynamic tasks, explore effective training strategies for dynamic awareness, and validate the generalizability of dynamic data. Furthermore, we propose PUMA, a dynamics-aware VLA architecture. By integrating scene-centric historical optical flow and specialized world queries to implicitly forecast object-centric future states, PUMA couples history-aware perception with short-horizon prediction. Results demonstrate that PUMA achieves state-of-the-art performance, yielding a 6.3% absolute improvement in success rate over baselines. Moreover, we show that training on dynamic data fosters robust spatiotemporal representations that transfer to static tasks. All code and data are available at https://github.com/H-EmbodVis/DOMINO.

Heng Fang, Shangru Li, Shuhan Wang +3

Huazhong University of Science and Technology, China · Huawei Technologies Co. Ltd, China

Generalization in Robotic Manipulation Robotic Manipulation Vision-Language-Action Models Dynamic Robotic Manipulation Robot Manipulation Benchmarks Robot Policy Generalization

Mar 5, 2026cs.RO

Task Parameter Extrapolation via Learning Inverse Tasks from Forward Demonstrations

Generalizing skill policies to novel conditions remains a key challenge in robot learning. Imitation learning methods, while data-efficient, are largely confined to the training region and consistently fail on input data outside it, leading to unpredictable policy failures. Alternatively, transfer learning approaches offer methods for trajectory generation robust to both changes in environment and tasks, but they remain data-hungry and lack accuracy in zero-shot generalization. We address these challenges in the context of task inversion learning and propose a novel joint learning approach to achieve accurate and efficient knowledge transfer. Our method constructs a common representation of the forward and inverse tasks, and leverages auxiliary forward demonstrations from novel configurations to successfully execute the corresponding inverse tasks, without any direct supervision. We demonstrate the extrapolation capabilities of our framework through ablation studies and experiments in simulated and real-world environments that require complex manipulation skills with a diverse set of objects and tools, where we outperform diffusion-based and multimodal VAE alternatives.

Serdar Bahar, Fatih Dogangun, Matteo Saveriano +2

Robot Skill Learning Generalization in Robotic Manipulation Robotic Manipulation Robot Imitation Learning Robot Policy Generalization

Mar 4, 2026cs.RO

From Local Corrections to Generalized Skills: Improving Neuro-Symbolic Policies with MEMO

Recent works use a neuro-symbolic framework for general manipulation policies. The advantage of this framework is that -- by applying off-the-shelf vision and language models -- the robot can break complex tasks down into semantic subtasks. However, the fundamental bottleneck is that the robot needs skills to ground these subtasks into embodied motions. Skills can take many forms (e.g., trajectory snippets, motion primitives, coded functions), but regardless of their form skills act as a constraint. The high-level policy can only ground its language reasoning through the available skills; if the robot cannot generate the right skill for the current task, its policy will fail. We propose to address this limitation -- and dynamically expand the robot's skills -- by leveraging user feedback. When a robot fails, humans can intuitively explain what went wrong (e.g., ``no, go higher''). While a simple approach is to recall this exact text the next time the robot faces a similar situation, we hypothesize that by collecting, clustering, and re-phrasing natural language corrections across multiple users and tasks, we can synthesize more general text guidance and coded skill templates. Applying this hypothesis we develop Memory Enhanced Manipulation (MEMO). MEMO builds and maintains a retrieval-augmented skillbook gathered from human feedback and task successes. At run time, MEMO retrieves relevant text and code from this skillbook, enabling the robot's policy to generate new skills while reasoning over multi-task human feedback. Our experiments demonstrate that using MEMO to aggregate local feedback into general skill templates enables generalization to novel tasks where existing baselines fall short. See supplemental material here: https://collab.me.vt.edu/memo

Benjamin A. Christie, Yinlong Dai, Mohammad Bararjanianbahnamiri +2

Collab, Dept. of Mechanical Engineering, Virginia Tech, Blacksburg, USA. · TEA Lab, Dept. of Mechanical Engineering, Virginia Tech, Blacksburg, USA.

Robot Skill Learning Language-Conditioned Robot Manipulation Generalization in Robotic Manipulation Robot Policy Generalization Neurosymbolic Learning Memory-Augmented Agents+1

Feb 18, 2026cs.RO

SimToolReal: An Object-Centric Policy for Zero-Shot Dexterous Tool Manipulation

The ability to manipulate tools significantly expands the set of tasks a robot can perform. Yet, tool manipulation represents a challenging class of dexterity, requiring grasping thin objects, in-hand object rotations, and forceful interactions. Since collecting teleoperation data for these behaviors is challenging, sim-to-real reinforcement learning (RL) is a promising alternative. However, prior approaches typically require substantial engineering effort to model objects and tune reward functions for each task. In this work, we propose SimToolReal, taking a step towards generalizing sim-to-real RL policies for tool manipulation. Instead of focusing on a single object and task, we procedurally generate a large variety of tool-like object primitives in simulation and train a single RL policy with the universal goal of manipulating each object to random goal poses. This approach enables SimToolReal to perform general dexterous tool manipulation at test-time without any object or task-specific training. We demonstrate that SimToolReal outperforms prior retargeting and fixed-grasp methods by 37% while matching the performance of specialist RL policies trained on specific target objects and tasks. Finally, we show that SimToolReal generalizes across a diverse set of everyday tools, achieving strong zero-shot performance over 120 real-world rollouts spanning 24 tasks, 12 object instances, and 6 tool categories.

Kushal Kedia, Tyler Ga Wei Lum, Jeannette Bohg +1

Cornell University · Stanford University

Zero-Shot Robotic Manipulation Robot Skill Learning Sim-to-Real Transfer Generalization in Robotic Manipulation Robotic Tool Use Robotic Manipulation+1

Feb 15, 2026cs.RO

RoboAug: One Annotation to Hundreds of Scenes via Region-Contrastive Data Augmentation for Robotic Manipulation

Enhancing the generalization of robotic learning in diverse unseen environments remains a fundamental challenge. Existing approaches often rely on large-scale pretraining, which is labor-intensive and time-consuming, or semantic data augmentation methods that assume flawless upstream object detection in real-world scenarios. In this work, we propose RoboAug, a novel generative data augmentation framework that reduces reliance on large-scale pretraining and perfect visual recognition by requiring only a single image with bounding box annotations for dataset construction. Leveraging this minimal supervision, RoboAug employs pretrained generative models for precise semantic augmentation and introduces a plug-and-play region-contrastive loss to guide attention toward task-relevant regions, thereby enhancing generalization and task success rates. Extensive real-world experiments on UR-5e, AgileX, and Tian Gong 2.0 demonstrate that RoboAug consistently outperforms state-of-the-art augmentation baselines under background, distractor, and lighting shifts. Our project is available at https://x-roboaug.github.io/.

Xinhua Wang, Kun Wu, Zhen Zhao +10

Beijing Innovation Center of Humanoid Robotics · Computation, Information and Technology, Technical University of Munich · City University of Hong Kong +3

Synthetic Data Augmentation Generalization in Robotic Manipulation Data-Efficient Robot Learning

Jan 29, 2026cs.RO

Spotlighting Task-Relevant Features: Object-Centric Representations for Better Generalization in Robotic Manipulation

The generalization capabilities of robotic manipulation policies are heavily influenced by the choice of visual representations. Existing approaches typically rely on representations extracted from pre-trained encoders, using two dominant types of features: global features, which summarize an entire image via a single pooled vector, and dense features, which preserve a patch-wise embedding from the final encoder layer. While widely used, both feature types mix task-relevant and irrelevant information, leading to poor generalization under distribution shifts, such as changes in lighting, textures, or the presence of distractors. In this work, we explore an intermediate structured alternative: Slot-Based Object-Centric Representations (SBOCR), which group dense features into a finite set of object-like entities. This representation permits to naturally reduce the noise provided to the robotic manipulation policy while keeping enough information to efficiently perform the task. We benchmark a range of global and dense representations against intermediate slot-based representations, across a suite of simulated and real-world manipulation tasks ranging from simple to complex. We evaluate their generalization under diverse visual conditions, including changes in lighting, texture, and the presence of distractors. Our findings reveal that SBOCR-based policies outperform dense and global representation-based policies in generalization settings, even without task-specific pretraining. These insights suggest that SBOCR is a promising direction for designing visual systems that generalize effectively in dynamic, real-world robotic environments.

Alexandre Chapin, Bruno Machado, Emmanuel Dellandréa +1

LIRIS, imagine

Visual Representation Learning Generalization in Robotic Manipulation Object-Centric Representation Learning Robotic Manipulation Robot Policy Generalization

Jan 28, 2026cs.RO

STORM: Slot-based Task-aware Object-centric Representation for robotic Manipulation

Visual foundation models provide strong perceptual features for robotics, but their dense representations lack explicit object-level structure, limiting robustness and controllability in manipulation tasks. We propose STORM (Slot-based Task-aware Object-centric Representation for robotic Manipulation), a lightweight object-centric adaptation module that augments frozen visual foundation models with a small set of task-aware slots for robotic manipulation. Rather than fully tuning large backbones on the task, STORM employs an efficient two-stage training strategy: few layers of object-centric representation are first trained on top of the frozen backbone through visual--semantic pretraining using language embeddings, then jointly adapted with a downstream manipulation policy for task alignement. This staged learning prevents degenerate slot formation and preserves semantic consistency while aligning perception with task objectives. Experiments on object discovery benchmarks and robotic manipulation tasks show that STORM improves control performance and generalization to visual shifts (distractors, textures, lighting) compared to directly using frozen or fine-tuned foundation model features, or existing object-centric representations. STORM serves not only as an efficient mechanism for refining generic foundation model features, but also as a novel way of injecting beneficial structural and semantic bias into policy learning.

Alexandre Chapin, Emmanuel Dellandréa, Liming Chen

LIRIS · Ecole Centrale de Lyon, LIRIS, 69130, Ecully, France

Vision Foundation Model Adaptation Generalization in Robotic Manipulation Object-Centric Representation Learning Robotic Manipulation

Jan 27, 2026cs.RO

Sim-and-Human Co-training for Data-Efficient and Scene-Generalizable Bimanual Manipulation

Real-robot demonstrations are prohibitively expensive, while simulation data and real-world human demonstrations are both scalable but each leaves a distinct gap: simulation suffers from a sim-to-real visual gap, and human data suffers from a human-to-robot embodiment gap. In this work, we identify a natural yet underexplored complementarity between these sources: simulation contributes robot-valid actions absent in human data, while human data provides real-world observations that simulation struggles to render. Building on this insight, we present SimHum, a co-training recipe that extracts kinematic priors from simulation and visual priors from human observations, then fine-tunes on a small real-robot dataset. SimHum exhibits strong scene-generalizable and data-efficient capabilities. With only 80 real-robot episodes per task, it achieves 62.5% success on held-out OOD scenes across four bimanual tabletop tasks, 53.7% higher than Real only in absolute success rate. Moreover, in a controlled data-collection study with matched collection time, SimHum improves over the best single-source pre-training baseline by 35.0% in absolute success rate. Project page: https://kaipengfang.github.io/sim-and-human/

Kaipeng Fang, Weiqing Liang, Yuyang Li +5

University of Electronic Science and Technology of China · Southwest Jiaotong University · Tongji University +1

Robot Skill Learning Bimanual Robotic Manipulation Sim-to-Real Transfer Generalization in Robotic Manipulation Cross-Embodiment Robot Learning Data-Efficient Robot Learning+1

Jan 16, 2026cs.RO

Where to Touch, How to Contact: A Hierarchical RL-MPC Framework for Geometry-Aware Sim-to-Real Manipulation

A key challenge in contact-rich dexterous manipulation is the need to jointly reason over global geometry and nonsmooth contact dynamics. End-to-end policies bypass this complexity, but often require large amounts of data and transfer poorly from simulation to reality. We address the limitations with a simple insight: dexterous manipulation is inherently hierarchical--at a high level, a robot decides where to touch (geometry); at a low level it determines how to move the object through contact dynamics. Building on this insight, we propose a hierarchical RL--MPC framework in which a high-level reinforcement learning (RL) policy predicts a contact intention, a novel object-centric interface that specifies (i) an object-surface contact location and (ii) a post-contact object subgoal pose. Conditioned on the contact intention, a low-level contact-implicit model predictive control (MPC) optimizes local contact modes and real-time (re)plans through contact dynamics to generate robot actions that robustly move the object toward each subgoal. We evaluate the framework on non-prehensile tasks, including geometry-generalized pushing across diverse object shapes, pivoting/flipping-based object reorientation, and environment-assisted object repositioning. It achieves high success rate with substantially reduced data (10 times less than end-to-end baselines), highly robust performance, and zero-shot sim-to-real transfer.

Zhixian Xie, Yu Xiang, Michael Posa +1

Arizona State University · University of Texas at Dallas · University of Pennsylvania

Contact-Rich Manipulation Non-Prehensile Manipulation Contact-Rich Robotic Manipulation Sim-to-Real Transfer Hierarchical RL Generalization in Robotic Manipulation+2

Dec 20, 2025cs.RO

Learning Semantic Atomic Skills for Multi-Task Robotic Manipulation

Scaling imitation learning to diverse multi-task robot manipulation remains challenging due to suboptimal demonstrations, behavioral multi-modality, and destructive interference across tasks. While skill-based methods offer a promising direction by decomposing behaviors into reusable abstractions, existing approaches often learn skills that are either biased toward linguistic structure or lack semantic alignment across tasks, limiting generalization. In this work, we propose AtomSkill, a novel framework that learns a semantically aligned Atomic Skill Space from demonstrations and enables robust long-horizon execution through keypose imagination. Our method introduces: (1) semantic contrastive skill alignment, which partitions demonstrations into variable-length atomic skills and employs a contrastive objective to jointly enforce semantic consistency and temporal coherence, yielding a compact and reusable skill library; and (2) action decoding with keypose imagining, where the policy predicts both a skill's terminal keypose and immediate actions, thereby supporting progress-aware skill transitions. During inference, an atomic skill diffusion sampler generates plausible skill sequences, while predicted keyposes autonomously trigger smooth skill chaining. Extensive experiments in simulation and real-world settings show that AtomSkill consistently outperforms state-of-the-art imitation learning and skill-based baselines. Project page: https://atom-skill.github.io.

Yihang Zhu, Weiqing Wang, Shijie Wu +2

ShanghaiTech University, Shanghai, China · InstAdapt

Long-Horizon Robotic Manipulation Robot Skill Learning Generalization in Robotic Manipulation Multi-Task Robotic Manipulation Robotic Manipulation Robot Imitation Learning+1

Sep 30, 2025cs.RO

RoboPilot: Generalizable Dynamic Robotic Manipulation with Dual-thinking Modes

Despite rapid progress in robotics, complex or long-horizon tasks remain a fundamental challenge. Most current approaches follow an open-loop paradigm with limited reasoning and no feedback, resulting in poor robustness to environmental changes and severe error accumulation. We present RoboPilot, a dual-thinking closed-loop agentic framework for robotic manipulation that supports adaptive reasoning for complex tasks in real-world dynamic environments. RoboPilot leverages primitive actions for structured task planning and flexible action generation as a agentic system, while introducing feedback to enable replanning from dynamic changes and execution errors. Chain-of-Thought reasoning further enhances high-level task planning and guides low-level action generation. The agentic system dynamically switches between fast and slow thinking to balance efficiency and accuracy. To systematically evaluate the robustness of RoboPilot in diverse robot manipulation scenarios, we introduce RoboPilot-Bench, a benchmark spanning 21 tasks across 10 categories, including infeasible-task recognition and dynamic recovery. Experiments show that RoboPilot outperforms state-of-the-art baselines by 11% in task success rate, and the real-world deployment on an industrial robot further demonstrates its robustness.

Xinyi Liu, Mohammadreza Fani Sani, Zewei Zhou +3

Microsoft, Denmark · Technical University of Denmark · University of California, Los Angeles

Robotic Manipulation Dynamic Robotic Manipulation Long-Horizon Robotic Manipulation Robot Task Planning Generalization in Robotic Manipulation Robot Manipulation Benchmarks

Sep 25, 2025cs.RO

BiNoMaP: Learning Category-Level Bimanual Non-Prehensile Manipulation Primitives

Non-prehensile manipulation, encompassing ungraspable actions such as pushing, poking, pivoting, and wrapping, remains underexplored due to its contact-rich and analytically intractable nature. We revisit this problem from two perspectives. First, instead of relying on single-arm setups or favorable environmental supports (e.g., walls or edges), we advocate a generalizable dual-arm configuration and establish a suite of Bimanual Non-prehensile Manipulation Primitives (BiNoMaP). Second, departing from prevailing RL-based approaches, we propose a three-stage, RL-free framework for learning structured non-prehensile skills. We begin by extracting bimanual hand motion trajectories from egocentric video demonstrations. Since these coarse trajectories suffer from perceptual noise and morphological discrepancies, we introduce a geometry-aware post-optimization algorithm to refine them into executable manipulation primitives consistent with predefined motion patterns. To enable category-level generalization, the learned primitives are further parameterized by object-relevant geometric attributes, primarily size, allowing adaptation to unseen instances with significant shape variations. Importantly, BiNoMaP supports cross-embodiment transfer: the same primitives can be deployed on two real-world dual-arm platforms with distinct kinematic configurations, without redesigning skill structures. Extensive real-robot experiments across diverse objects and spatial configurations demonstrate the effectiveness, efficiency, and strong generalization capability of our approach.

Huayi Zhou, Kui Jia

School of Data Science, The Chinese University of Hong Kong, Shenzhen

Bimanual Robotic Manipulation Non-Prehensile Manipulation Contact-Rich Robotic Manipulation Generalization in Robotic Manipulation Robotic Manipulation Robot Imitation Learning

Aug 12, 2025cs.RO

Rational Inverse Reasoning: Few-Shot Imitation by Inferring Intent through Planning

Humans can learn a new manipulation task from one or two demonstrations and then perform it in a new room, with new objects, under new constraints. Modern robot imitation learning, in contrast, typically needs hundreds to thousands of demonstrations and still degrades under modest shifts in layout, geometry, object set or task constraints. We argue this gap is not just about data, but also about the level of abstraction at which learning occurs; generalization requires inferring the latent intent underlying why a demonstrator behaved in a certain way, rather than reproducing how they moved. We present Rational Inverse Reasoning (RIR), which casts few-shot imitation as inference over latent explanation programs: compact, executable descriptions of intent that map an object-centric scene to a structured task-and-motion-planning (TAMP) specification of goals, subgoals and constraints. A vision-language model proposes candidate programs, and a hierarchical planner supplies a bounded-rational likelihood. By combining VLM program proposals, and planner-grounded feedback, RIR iteratively refines the candidate set to approximate a posterior over concise, executable programs. On a 2D reasoning benchmark and a real Franka FR3, RIR recovers transferable task structure from as little as one demonstration. Generalizing to substantially new layouts and object sets, RIR outperforms VLM-planning baselines that lack explicit rationality and planning-grounded inference, increasing downstream success rate by

34

and

28

percentage points in the one- and three-shot settings.

Ben Zandonati, Tomás Lozano-Pérez, Leslie Pack Kaelbling

MIT CSAIL

Robot Skill Learning Generalization in Robotic Manipulation Program Synthesis Few-Shot Learning Robot Imitation Learning Robot Policy Generalization

Oct 24, 2024cs.RO

Data Scaling Laws in Imitation Learning for Robotic Manipulation

Data scaling has revolutionized fields like natural language processing and computer vision, providing models with remarkable generalization capabilities. In this paper, we investigate whether similar data scaling laws exist in robotics, particularly in robotic manipulation, and whether appropriate data scaling can yield single-task robot policies that can be deployed zero-shot for any object within the same category in any environment. To this end, we conduct a comprehensive empirical study on data scaling in imitation learning. By collecting data across numerous environments and objects, we study how a policy's generalization performance changes with the number of training environments, objects, and demonstrations. Throughout our research, we collect over 40,000 demonstrations and execute more than 15,000 real-world robot rollouts under a rigorous evaluation protocol. Our findings reveal several intriguing results: the generalization performance of the policy follows a roughly power-law relationship with the number of environments and objects. The diversity of environments and objects is far more important than the absolute number of demonstrations; once the number of demonstrations per environment or object reaches a certain threshold, additional demonstrations have minimal effect. Based on these insights, we propose an efficient data collection strategy. With four data collectors working for one afternoon, we collect sufficient data to enable the policies for two tasks to achieve approximately 90% success rates in novel environments with unseen objects.

Fanqi Lin, Yingdong Hu, Pingyue Sheng +3

Tsinghua University · Shanghai Qi Zhi Institute · Shanghai Artificial Intelligence Laboratory

Generalization in Robotic Manipulation Robotic Manipulation Robot Imitation Learning Robot Policy Generalization Robot Learning

Date pendingcs.RO

EBench: Elemental Diagnosis of Generalist Mobile Manipulation Policies

We present EBench, a simulation benchmark that diagnoses generalist mobile manipulation policies beyond a single success-rate scalar. EBench comprises 26 diverse and challenging manipulation tasks annotated along 5 capability dimensions and 4 generalization dimensions. We evaluate state-of-the-art generalist manipulation models including

\pi_0

,

\pi_{0.5}

, XVLA, and InternVLA-A1, and reveal that the models exhibit strikingly different capability profiles:

\pi_{0.5}

achieves the highest test success rate, the best train--test retention, and the strongest mobile manipulation performance;

\pi_0

leads on dexterous fixed-base and high-precision tasks; XVLA and InternVLA-A1 exhibit complementary strengths across atomic skills and operating regimes. Beyond capability profiling, EBench analyzes the generalization ability from 4 representative perspectives, identifying the impact of different distribution shift factors. The results reveal strengths and weaknesses of models behind an overall score. We hope this benchmark offers a broad set of diagnostic signals to guide iteration on generalist manipulation models.

Ning Gao, Jinliang Zheng, Xing Gao +22

Shanghai AI Laboratory · Xi’an Jiaotong University · Institute for AI Industry Research (AIR), Tsinghua University +5

Mobile Manipulation Generalization in Robotic Manipulation Robotic Policy Evaluation Multi-Task Robotic Manipulation Robotic Manipulation Robot Manipulation Benchmarks+1