Generalization in Robotic Manipulation

Ask about this topic

Overview

The field studies whether robots can manipulate objects reliably when their shape, position, appearance, surroundings, or physical properties differ from training, and whether skills transfer across tasks and robot bodies. Common approaches use object-centric geometry and affordances, reusable skills and action primitives, symmetry-aware policies, and vision-language-action or world models that connect instructions and predicted outcomes to control. Researchers also expand training through simulation, human videos, data augmentation, and diverse demonstrations, then test policies in settings such as LIBERO, RLBench, and real-world object manipulation. A central challenge is that high benchmark success does not always mean a robot has learned physical or compositional generalization; evaluations must distinguish genuine transfer from memorized patterns and expose failures under combined shifts.

Latest papers

CardsList

355 papers45 papers in the last four weeks, down 22% on the four weeks before. 0.3% of all new papers.

Feb 25, 2026

DexRepNet++: Learning Dexterous Robotic Manipulation with Geometric and Spatial Hand-Object Representations

Robotic dexterous manipulation is a challenging problem due to high degrees of freedom (DoFs) and complex contacts of multi-fingered robotic hands. Many existing deep reinforcement learning (DRL) based methods aim at improving sample efficiency in high-dimensional output action spaces. However, existing works often overlook the role of representations in achieving generalization of a manipulation policy in the complex input space during the hand-object interaction. In this paper, we propose DexRep, a novel hand-object interaction representation to capture object surface features and spatial relations between hands and objects for dexterous manipulation skill learning. Based on DexRep, policies are learned for three dexterous manipulation tasks, i.e. grasping, in-hand reorientation, bimanual handover, and extensive experiments are conducted to verify the effectiveness. In simulation, for grasping, the policy learned with 40 objects achieves a success rate of 87.9% on more than 5000 unseen objects of diverse categories, significantly surpassing existing work trained with thousands of objects; for the in-hand reorientation and handover tasks, the policies also boost the success rates and other metrics of existing hand-object representations by 20% to 40%. The grasp policies with DexRep are deployed to the real world under multi-camera and single-camera setups and demonstrate a small sim-to-real gap.

Qingtao Liu, Zhengnan Sun, Yu Cui +5

the College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China · the College of Control Science and Engineering and the State Key Laboratory of Industrial Control Technology, Zhejiang University, China · the Department of Computer Science, National University of Singapore, 15 Computing Dr, 117418, Singapore

Dexterous ManipulationRepresentation LearningRobotic GraspingGeneralization in Robotic ManipulationRobotic RLBimanual Robotic Manipulation
AskPDF
Feb 24, 2026

Learning Dynamic Rope Manipulation Using Task-Level Iterative Learning Control

We introduce a Task-Level Iterative Learning Control method for dynamic manipulation of ropes. We demonstrate this method on a non-planar rope manipulation task called the flying knot. Using a single human demonstration and a simplified rope model, the method learns directly on hardware without reliance on large amounts of demonstration data or massive amounts of simulation. At each iteration, the algorithm inverts a model of the robot and rope by solving a quadratic program to propagate task-space errors into action updates. We evaluate performance across 7 different kinds of ropes, including chain, latex surgical tubing, and braided and twisted ropes, ranging in thicknesses of 7--25,mm and densities of 0.013--0.5,kg/m. Learning achieves a 100% success rate within 10 trials on all ropes. Furthermore, the method can successfully transfer between most rope types in 2--5 trials. https://flying-knots.github.io

Krishna Suresh, Chris Atkeson

Carnegie Mellon University

Iterative Learning ControlDynamic Robotic ManipulationDeformable Object ManipulationAdaptive ControlGeneralization in Robotic Manipulation
AskPDF
Feb 19, 2026

Attachment Anchors: A Novel Framework for Laparoscopic Grasping Point Prediction in Colorectal Surgery

Accurate grasping point prediction is a key challenge for autonomous tissue manipulation in minimally invasive surgery, particularly in complex and variable procedures such as colorectal interventions. Due to their complexity and prolonged duration, colorectal procedures have been underrepresented in current research. At the same time, they pose a particularly interesting learning environment due to repetitive tissue manipulation, making them a promising entry point for autonomous, machine learning-driven support. Therefore, in this work, we introduce attachment anchors, a structured representation that encodes the local geometric and mechanical relationships between tissue and its anatomical attachments in colorectal surgery. This representation reduces uncertainty in grasping point prediction by normalizing surgical scenes into a consistent local reference frame. We demonstrate that attachment anchors can be predicted from laparoscopic images and incorporated into a grasping framework based on machine learning. Experiments on a dataset of 90 colorectal surgeries demonstrate that attachment anchors improve grasping point prediction compared to image-only baselines. There are particularly strong gains in out-of-distribution settings, including unseen procedures and operating surgeons. These results suggest that attachment anchors are an effective intermediate representation for learning-based tissue manipulation in colorectal surgery.

Dennis N. Schneider, Lars Wagner, Daniel Rueckert +1

Technical University of Munich, TUM School of Medicine and Health, TUM University Hospital rechts der Isar, Department of Surgery, Research Group MITI, Munich, Germany · Technical University of Munich, TUM School of Medicine and Health, TUM University Hospital rechts der Isar, Chair for AI in Healthcare and Medicine Munich, Munich, Germany · Department of Computing, Imperial College London, London, UK

Laparoscopic SurgeryRobotic GraspingSurgical RoboticsGeneralization in Robotic Manipulation
AskPDF
Feb 19, 2026

Grasp Synthesis Matching From Rigid To Soft Robot Grippers Using Conditional Flow Matching

A representation gap exists between grasp synthesis for rigid and soft grippers. Anygrasp [1] and many other grasp synthesis methods are designed for rigid parallel grippers, and adapting them to soft grippers often fails to capture their unique compliant behaviors, resulting in data-intensive and inaccurate models. To bridge this gap, this paper proposes a novel framework to map grasp poses from a rigid gripper model to a soft Fin-ray gripper. We utilize Conditional Flow Matching (CFM), a generative model, to learn this complex transformation. Our methodology includes a data collection pipeline to generate paired rigid-soft grasp poses. A U-Net autoencoder conditions the CFM model on the object's geometry from a depth image, allowing it to learn a continuous mapping from an initial Anygrasp pose to a stable Fin-ray gripper pose. We validate our approach on a 7-DOF robot, demonstrating that our CFM-generated poses achieve a higher overall success rate for seen and unseen objects (34% and 46% respectively) compared to the baseline rigid poses (6% and 25% respectively) when executed by the soft gripper. The model shows significant improvements, particularly for cylindrical (50% and 100% success for seen and unseen objects) and spherical objects (25% and 31% success for seen and unseen objects), and successfully generalizes to unseen objects. This work presents CFM as a data-efficient and effective method for transferring grasp strategies, offering a scalable methodology for other soft robotic systems.

Tanisha Parulekar, Ge Shi, Josh Pinskier +2

Robotic GraspingGeneralization in Robotic Manipulation
AskPDF
Feb 18, 2026

MALLVI: A Multi-Agent Framework for Integrated Generalized Robotics Manipulation

Task planning for robotic manipulation with large language models (LLMs) is an emerging area. Prior approaches rely on specialized models, fine tuning, or prompt tuning, and often operate in an open loop manner without robust environmental feedback, making them fragile in dynamic settings. MALLVI presents a Multi Agent Large Language and Vision framework that enables closed-loop feedback driven robotic manipulation. Given a natural language instruction and an image of the environment, MALLVI generates executable atomic actions for a robot manipulator. After action execution, a Vision Language Model (VLM) evaluates environmental feedback and decides whether to repeat the process or proceed to the next step. Rather than using a single model, MALLVI coordinates specialized agents, Decomposer, Localizer, Thinker, and Reflector, to manage perception, localization, reasoning, and high level planning. An optional Descriptor agent provides visual memory of the initial state. The Reflector supports targeted error detection and recovery by reactivating only relevant agents, avoiding full replanning. Experiments in simulation and real-world settings show that iterative closed loop multi agent coordination improves generalization and increases success rates in zero shot manipulation tasks. Code available at https://github.com/iman1234ahmadi/MALLVI .

Mehrshad Taji, Arad Mahdinezhad Kashani, Iman Ahmadi +3

Department of Electrical Engineering Sharif University of Technology

Large Language Model-Based Robot PlanningRobotic ManipulationZero-Shot Robotic ManipulationGeneralization in Robotic ManipulationMulti-Agent PlanningVLMs for Robotics
AskPDF
Feb 18, 2026

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 ManipulationRobot Skill LearningSim-to-Real TransferGeneralization in Robotic ManipulationRobotic Tool UseRobotic Manipulation+1
AskPDF
Feb 18, 2026

Learning to unfold cloth: Scaling up world models to deformable object manipulation

Learning to manipulate cloth is both a paradigmatic problem for robotic research and a problem of immediate relevance to a variety of applications ranging from assistive care to the service industry. The complex physics of the deformable object makes this problem of cloth manipulation nontrivial. In order to create a general manipulation strategy that addresses a variety of shapes, sizes, fold and wrinkle patterns, in addition to the usual problems of appearance variations, it becomes important to carefully consider model structure and their implications for generalisation performance. In this paper, we present an approach to in-air cloth manipulation that uses a variation of a recently proposed reinforcement learning architecture, DreamerV2. Our implementation modifies this architecture to utilise surface normals input, in addition to modiying the replay buffer and data augmentation procedures. Taken together these modifications represent an enhancement to the world model used by the robot, addressing the physical complexity of the object being manipulated by the robot. We present evaluations both in simulation and in a zero-shot deployment of the trained policies in a physical robot setup, performing in-air unfolding of a variety of different cloth types, demonstrating the generalisation benefits of our proposed architecture.

Jack Rome, Stephen James, Subramanian Ramamoorthy

University of Edinburgh · Imperial College London

Deformable Object ManipulationGeneralization in Robotic ManipulationWorld Models for RoboticsModel-Based RL
AskPDF
Feb 18, 2026

RoboGene: Diversity-Driven Agentic Task Generation Framework for VLA Pre-training

The pursuit of general-purpose robotic manipulation is hindered by the scarcity of diverse, real-world interaction data. Unlike large-scale web vision-language data, robotic data collection incurs prohibitive physical costs. The key challenge is thus not merely to collect more robot data, but to obtain informative training data that broadens behavioral coverage across objects, scenes, and skills. Yet automatically curating task proposals to maximize the utility of collected data remains under-explored. We introduce RoboGene, an agentic framework for generating diverse, informative manipulation tasks across single-arm, dual-arm, and mobile robots. RoboGene combines (1) diversity-driven sampling for broad task coverage, (2) agentic self-reflection for task-quality assessment, and (3) human-verified feedback memory for execution-grounded validation and continual refinement. Through quantitative analysis and large-scale real-world experiments, we collect 18k trajectories and introduce metrics for task feasibility and diversity. RoboGene significantly outperforms frontier foundation models, including GPT-4o and Gemini 2.5 Pro. VLA models trained on RoboGene-generated data achieve higher success rates and stronger generalization, highlighting the value of high-quality task generation. Project page: https://robogene-boost-vla.github.io/. Datasets: Hugging Face (https://huggingface.co/datasets/X-Humanoid/RoboGene) and ModelScope (https://modelscope.cn/datasets/X-Humanoid/RoboGene_tasks).

Yixue Zhang, Kun Wu, Zhi Gao +12

Beijing Innovation Center of Humanoid Robotics · The School of Advanced Manufacturing and Robotics, Peking University · Beijing Institute of Technology +2

Synthetic Data Generation for RoboticsRobotic Data CollectionVision-Language-Action ModelsGeneralization in Robotic Manipulation
AskPDF
Feb 16, 2026

Affordance Transfer Across Object Instances via Semantically Anchored Functional Map

Traditional learning from demonstration (LfD) generally demands a cumbersome collection of physical demonstrations, which can be time-consuming and challenging to scale. Recent advances show that robots can instead learn from human videos by extracting interaction cues without direct robot involvement. However, a fundamental challenge remains: how to generalize demonstrated interactions across different object instances that share similar functionality but vary significantly in geometry. In this work, we propose Semantic Anchored Functional Maps (SemFM), a framework for transferring affordances across objects from a single visual demonstration. Starting from a coarse mesh reconstructed from an image, our method identifies semantically corresponding functional regions between objects, selects mutually exclusive semantic anchors, and propagates these constraints over the surface using a functional map to obtain a dense, semantically consistent correspondence. This enables demonstrated interaction regions to be transferred across geometrically diverse objects in a lightweight and interpretable manner. Experiments on synthetic object categories and real-world robotic manipulation tasks show that our approach enables accurate affordance transfer with modest computational cost, making it well-suited for practical robotic perception-to-action pipelines.

Xiaoxiang Dong, Weiming Zhi

Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA. · School of Computer Science, The University of Sydney, Australia. · Australian Centre for Robotics, The University of Sydney, Australia. +1

Functional Maps3D Shape MatchingGeneralization in Robotic ManipulationTransfer LearningRobotic Manipulation
AskPDF
Feb 15, 2026

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 AugmentationGeneralization in Robotic ManipulationData-Efficient Robot Learning
AskPDF
Jan 29, 2026

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 LearningGeneralization in Robotic ManipulationObject-Centric Representation LearningRobotic ManipulationRobot Policy Generalization
AskPDF
Jan 28, 2026

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 AdaptationGeneralization in Robotic ManipulationObject-Centric Representation LearningRobotic Manipulation
AskPDF
Jan 27, 2026

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 LearningBimanual Robotic ManipulationSim-to-Real TransferGeneralization in Robotic ManipulationCross-Embodiment Robot LearningData-Efficient Robot Learning+1
AskPDF
Jan 16, 2026

Skill-Aware Diffusion for Generalizable Robotic Manipulation

Robust generalization in robotic manipulation is crucial for robots to adapt flexibly to diverse environments. Existing methods usually improve generalization by scaling data and networks, but model tasks independently and overlook skill-level information. Observing that tasks within the same skill share similar motion patterns, we propose Skill-Aware Diffusion (SADiff), which explicitly incorporates skill-level information to improve generalization. SADiff learns skill-specific representations through a skill-aware encoding module with learnable skill tokens, and conditions a skill-constrained diffusion model to generate object-centric motion flow. A skill-retrieval transformation strategy further exploits skill-specific trajectory priors to refine the mapping from 2D motion flow to executable 3D actions. Furthermore, we introduce IsaacSkill, a high-fidelity dataset containing fundamental robotic skills for comprehensive evaluation and sim-to-real transfer. Experiments in simulation and real-world settings show that SADiff achieves good performance and generalization across various manipulation tasks. Dataset and videos are available at https://sites.google.com/view/sa-diff.

Aoshen Huang, Jiaming Chen, Jiyu Cheng +3

School of Control Science and Engineering, Shandong University, Jinan 250061, China. · Department of Computer Science, The University of Manchester, M13 9PL Manchester, U.K.

Robotic ManipulationGeneralization in Robotic ManipulationRobot Skill Learning
AskPDF
Jan 16, 2026

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 ManipulationNon-Prehensile ManipulationContact-Rich Robotic ManipulationSim-to-Real TransferHierarchical RLGeneralization in Robotic Manipulation+2
AskPDF
Dec 20, 2025

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 ManipulationRobot Skill LearningGeneralization in Robotic ManipulationMulti-Task Robotic ManipulationRobotic ManipulationRobot Imitation Learning+1
AskPDF
Dec 1, 2025

Guardian: Detecting Robotic Planning and Execution Errors with Vision-Language Models

Robust robotic manipulation requires reliable failure detection and recovery. Although recent Vision-Language Models (VLMs) show promise in robot failure detection, their generalization is severely limited by the scarcity and narrow coverage of failure data. To address this bottleneck, we propose an automatic framework for generating diverse robotic planning and execution failures across both simulated and real-world environments. Our approach perturbs successful manipulation trajectories to synthesize failures that reflect realistic failure distributions, and leverages VLMs to produce structured step-by-step reasoning traces. This yields GuardianFail-36k, a large-scale failure reasoning dataset built upon the RLBench simulator and the BridgeDataV2 real-robot dataset. Using GuardianFail-36k, we train Guardian, a multi-view reasoning VLM for unified planning and execution verification. Guardian achieves state-of-the-art performance on three unseen real-world benchmarks: RoboFail, RoboVQA, and our newly introduced UR5-Fail. When integrated with a state-of-the-art LLM-based manipulation policy, it consistently boosts task success rates in both simulation and real-world deployment. These results demonstrate that scaling high-quality failure reasoning data is critical for improving generalization in robotic failure detection. Code, Data, and Models are available at https://www.di.ens.fr/willow/research/guardian/.

Paul Pacaud, Ricardo Garcia, Shizhe Chen +1

Inria, École normale supérieure, CNRS, PSL Research University

Failure Detection for VLA ModelsRobot Manipulation DatasetsSynthetic Data Generation for RoboticsGeneralization in Robotic ManipulationReasoning for VLA Models
AskPDF
Nov 21, 2025

METIS: Multi-Source Egocentric Training for Integrated Dexterous Vision-Language-Action Model

Building a generalist robot that can perceive, reason, and act across diverse tasks remains an open challenge, especially for dexterous manipulation. A major bottleneck lies in the scarcity of large-scale, action-annotated data for dexterous skills, as teleoperation is difficult and costly. Human data, with its vast scale and diverse manipulation behaviors, provides rich priors for learning robotic actions. While prior works have explored leveraging human demonstrations, they are often constrained by limited scenarios and a large visual gap between human and robots. To eliminate these limitations, we propose METIS, a vision-language-action (VLA) model for dexterous manipulation pretrained on multi-source egocentric datasets. We first construct EgoAtlas, which integrates large-scale human and robotic data from multiple sources, all unified under a consistent action space. We further extract motion-aware dynamics, a compact and discretized motion representation, which provides efficient and expressive supervision for VLA training. Built upon them, METIS integrates reasoning and acting into a unified framework, enabling effective deployment to downstream dexterous manipulation tasks. Our method demonstrates exceptional dexterous manipulation capabilities, achieving highest average success rate in six real-world tasks. Experimental results also highlight the superior generalization and robustness to out-of-distribution scenarios. These findings emphasize METIS as a promising step toward a generalist model for dexterous manipulation.

Yankai Fu, Ning Chen, Junkai Zhao +5

State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University · Beijing Academy of Artificial Intelligence

Robotic ManipulationVLMs for RoboticsRobot LearningGeneralization in Robotic ManipulationCross-Embodiment Robot LearningRobustness of VLA Models
AskPDF
Oct 14, 2025

Actron3D: Learning Actionable Neural Functions from Videos for Transferable Robotic Manipulation

We present Actron3D, a framework that enables robots to acquire transferable 6-DoF manipulation skills from monocular, uncalibrated, RGB-only human demonstration videos. Our key idea is to represent manipulation knowledge within a video as a continuous neural function over object space. At the core of Actron3D lies the Neural Affordance Function, which distills geometry, visual features, contact priors, and action flows from diverse demonstration videos into a compact 3D neural representation. During deployment, we adopt a hierarchical pipeline that retrieves the matched affordance function and transfers encoded manipulation knowledge to novel objects through coarse-to-fine differentiable optimization. Leveraging the continuous nature of Neural Affordance Function, the framework performs spatial queries over multimodal features to align demonstrations with observations and generates precise 6-DoF manipulation policy. Experiments in both simulation and the real-world demonstrate that Actron3D significantly outperforms prior methods, achieving a 14.9 percentage point improvement in the average success rate across 13 tasks while requiring only 2-3 demonstration videos per task.

Anran Zhang, Hanzhi Chen, Yannick Burkhardt +4

ETH Zurich · Technical University of Munich

Robot Learning from VideoAffordance-Based Robotic ManipulationGeneralization in Robotic ManipulationRobotic Affordance Inference
AskPDF
Oct 1, 2025

AFFORD2ACT: How Far Can Sparse Keypoints Go for Robot Manipulation?

Vision-based manipulation policies are commonly learned from rich visual observations, typically using dense visual features extracted from full resolution images. It remains unclear how much of this visual information is really needed for effective policy learning. In this paper, we investigate the opposite end of the spectrum: how effective can manipulation policies be if they use as input only a compact visual representation. Sparse 2D keypoints provide a deliberately compact representation, giving the policy spatial information while removing direct access to appearance, texture, and background. We design AFFORD2ACT (Affordance-Guided Keypoints to Act) to study how much manipulation capability can be retained under this representation constraint. An off-the-shelf language-conditioned affordance model identifies the task-relevant region, from which up to 15 object keypoints are selected, together with 4 end-effector keypoints. The policy input is therefore reduced to only 19 2D keypoints (38 coordinates). A lightweight gating further decides which keypoints to pay attention to during execution. Across six real-robot manipulation tasks, this compact state outperforms the evaluated RGB, RGB-D, and dense-affordance behavior cloning baselines, degrades less as the number of demonstrations is reduced, and achieves 82% average success in held-out settings. Replacing learned gating with uniform weights reduces success from 92% to 52%. At the same time, the representation depends on reliable affordance localization and keypoint tracking, and remains vulnerable to incorrect masks, occlusion, tracking drift, and distractors.

Anukriti Singh, Kasra Torshizi, Khuzema Habib +3

Department of Computer Science, University of Maryland, College Park, MD, USA.

Robotic ManipulationData-Efficient Robot LearningAffordance-Based Robotic ManipulationGeneralization in Robotic Manipulation
AskPDF
Sep 30, 2025

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 ManipulationDynamic Robotic ManipulationLong-Horizon Robotic ManipulationRobot Task PlanningGeneralization in Robotic ManipulationRobot Manipulation Benchmarks
AskPDF
Sep 25, 2025

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

Non-Prehensile Robotic ManipulationBimanual Robotic ManipulationNon-Prehensile ManipulationContact-Rich Robotic ManipulationGeneralization in Robotic ManipulationRobotic Manipulation+1
AskPDF
Aug 12, 2025

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 3434 and 2828 percentage points in the one- and three-shot settings.

Ben Zandonati, Tomás Lozano-Pérez, Leslie Pack Kaelbling

MIT CSAIL

Robot Skill LearningGeneralization in Robotic ManipulationProgram SynthesisFew-Shot LearningRobot Imitation LearningRobot Policy Generalization
AskPDF
Mar 9, 2025

AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems

We explore how scalable robot data can address real-world challenges for generalized robotic manipulation. Introducing AgiBot World, a large-scale platform comprising over 1 million trajectories across 217 tasks in five deployment scenarios, we achieve an order-of-magnitude increase in data scale compared to existing datasets. Accelerated by a standardized collection pipeline with human-in-the-loop verification, AgiBot World guarantees high-quality and diverse data distribution. It is extensible from grippers to dexterous hands and visuo-tactile sensors for fine-grained skill acquisition. Building on top of data, we introduce Genie Operator-1 (GO-1), a novel generalist policy that leverages latent action representations to maximize data utilization, demonstrating predictable performance scaling with increased data volume. Policies pre-trained on our dataset achieve an average performance improvement of 30% over those trained on Open X-Embodiment, both in in-domain and out-of-distribution scenarios. GO-1 exhibits exceptional capability in real-world dexterous and long-horizon tasks, achieving over 60% success rate on complex tasks and outperforming prior RDT approach by 32%. By open-sourcing the dataset, tools, and models, we aim to democratize access to large-scale, high-quality robot data, advancing the pursuit of scalable and general-purpose intelligence.

AgiBot World Contributors, Qingwen Bu, Jisong Cai +49

Robot Manipulation DatasetsGeneralization in Robotic ManipulationRobotic Data CollectionLong-Horizon Robotic Manipulation
AskPDF
Oct 24, 2024

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 ManipulationRobotic ManipulationRobot Imitation LearningRobot Policy GeneralizationRobot Learning
AskPDF