Author: Denis Avetisyan
Researchers have unveiled RoboAfford++, a large-scale resource designed to help robots better interpret their surroundings and interact with objects in a more intuitive way.
This work introduces a multimodal dataset and benchmark for affordance learning, leveraging generative AI to improve robotic manipulation and navigation capabilities.
While vision-language models demonstrate promise in high-level reasoning, they often lack the fine-grained understanding of physical interaction necessary for robust robotic behavior. To address this limitation, we introduce RoboAfford++: A Generative AI-Enhanced Dataset for Multimodal Affordance Learning in Robotic Manipulation and Navigation, comprising nearly 2 million multimodal annotations designed to teach robots how to effectively interact with their surroundings. This dataset and accompanying benchmark, RoboAfford-Eval, significantly improve affordance reasoning in existing models, revealing substantial deficiencies in current approaches to robotic manipulation and navigation. Can these advancements pave the way for more intuitive and adaptable robot-human collaboration in complex real-world scenarios?
The Illusion of Robotic Understanding
Contemporary Vision-Language Models, while demonstrating impressive capabilities in image captioning and basic instruction following, falter when confronted with the nuanced demands of robotic manipulation. These models often struggle to move beyond superficial visual recognition, lacking the capacity for the precise spatial reasoning and understanding of object affordances – what an object allows a robot to do – necessary for complex tasks. For example, a VLM might identify a mug, but fail to grasp its handle for lifting, or understand that a flat surface affords placement, hindering successful task completion. This limitation stems from a reliance on correlational learning from large datasets, rather than a deeper comprehension of physics, geometry, and functional relationships within a scene, ultimately restricting their applicability in real-world robotic applications requiring dexterity and adaptability.
The ambition of equipping robots with human-like understanding through Vision-Language Models (VLMs) is currently hampered by a critical lack of training data. Existing datasets, while valuable, often fall short in both scale and the breadth of real-world scenarios they represent. A robot trained solely on curated, laboratory-like conditions struggles to generalize to the messy, unpredictable environments encountered in homes or workplaces. This limitation extends beyond simply recognizing objects; robust robotic manipulation and navigation demand an understanding of diverse object states, varying lighting conditions, and cluttered scenes-all of which require exponentially more data to model effectively. Consequently, VLMs frequently exhibit brittle performance, succeeding in controlled settings but failing when faced with even minor deviations from their training data, highlighting the urgent need for larger, more diverse datasets that capture the full spectrum of real-world complexity.
Effective robotic action hinges on a precise understanding of the environment, but current systems struggle with accurately localizing both objects and the navigable spaces around them. This isn’t simply about identifying “a cup” or “a table”; it requires determining the cup’s precise position, orientation, and crucially, the available space to grasp it without collision. Similarly, a robot must understand not just the presence of a doorway, but the dimensions of the opening and the clear path leading to it. This dual localization challenge – pinpointing objects and free space – creates a significant bottleneck in robotic planning because even slight inaccuracies can lead to failed grasps, collisions, or inefficient navigation. The ability to build a complete and accurate spatial representation is therefore paramount for robots to move beyond pre-programmed routines and achieve truly autonomous operation in complex, dynamic environments.
Cultivating Affordance: The RoboAfford++ Dataset
The RoboAfford++ dataset comprises 2.0 million question-answer pairs designed to overcome limitations found in existing robotic datasets. These QA pairs are meticulously annotated with information regarding both object affordances – the functional properties of objects and how they can be used – and spatial affordances, detailing available space and configurations for object manipulation and placement. This detailed annotation strategy allows for improved training and evaluation of robotic systems focused on complex task planning and execution, exceeding the scope of datasets lacking such granular information.
The creation of the RoboAfford++ dataset benefited from the application of generative AI, specifically large language models such as GPT-4o, to streamline both data augmentation and annotation processes. This approach enabled the rapid generation of question-answer pairs based on existing data and simulated environments, reducing the need for extensive manual labeling. The use of these models facilitated the scaling of the dataset to 2.0 million QA pairs in a timeframe that would have been impractical with traditional methods. GPT-4o was utilized for tasks including paraphrasing questions, generating variations of existing annotations, and validating the consistency of the augmented data, resulting in a substantial acceleration of the overall dataset creation timeline.
The RoboAfford++ dataset integrates data from multiple established sources to maximize the variety of represented robotic scenarios. These include the LVIS Dataset, providing large-scale instance segmentation; the Pixmo-Points Dataset, contributing detailed 3D point cloud annotations; the RoboPoint Dataset, focused on robotic manipulation scenes; the PACO-LVIS Dataset, offering object affordance information alongside instance segmentation; and the AI2Thor Simulator, which generates synthetic data for training and evaluation. This multi-source approach ensures broad coverage of objects, scenes, and potential robotic interactions, improving the generalizability and robustness of models trained on the dataset.
The RoboAfford++ dataset integrates spatial affordance data, specifically information detailing available space for object placement, to enhance robotic task planning and execution capabilities. This data consists of annotations identifying regions within a scene where objects can be successfully placed without collision or obstruction. By explicitly modeling available space, RoboAfford++ facilitates the development of robotic systems capable of more nuanced and efficient task execution, moving beyond simple reachability to consider the feasibility of complete object manipulation and placement within complex environments. The inclusion of this data allows for improved path planning, grasp selection, and overall task success rates in robotic applications.
RoboAfford-Qwen++: A Refinement Through Focused Training
RoboAfford-Qwen++ is a Vision-Language Model (VLM) developed through fine-tuning the Qwen2.5-VL-7B foundational model. This fine-tuning process utilized the RoboAfford++ dataset, a collection specifically designed to train models in understanding robotic affordances – the potential actions an object or environment offers to a robot. By building upon an existing, pre-trained VLM and focusing the training on robotic-specific data, RoboAfford-Qwen++ aims to improve performance in robotic task planning and execution. The selection of Qwen2.5-VL-7B as the base model provides a strong starting point for visual and linguistic understanding, which is then adapted and refined through the RoboAfford++ dataset to address the unique challenges of robotic interaction.
RoboAfford-Qwen++ utilizes the LLaVA-1.5 framework for instruction tuning, a methodology that enables the model to interpret and respond to natural language instructions related to robotic tasks. The training process employs the AdamW optimizer, a variant of the stochastic gradient descent algorithm, which incorporates weight decay regularization to prevent overfitting and improve generalization performance. Specifically, AdamW calculates adaptive learning rates for each parameter, adjusting them based on estimates of first and second moments of the gradients, and applies a decoupled weight decay penalty, resulting in more stable and efficient training compared to standard Adam optimization.
RoboAfford-Qwen++ demonstrates a 63.4% average accuracy on the RoboAfford-Eval benchmark, representing a performance improvement over all previously evaluated baseline models. This accuracy is achieved through the model’s combined focus on recognizing both object affordances – the actions possible with an object – and spatial affordances, which relate to the environment’s geometry and how it supports actions. The RoboAfford-Eval benchmark assesses a model’s ability to correctly identify these affordances, and the reported score indicates RoboAfford-Qwen++’s enhanced capacity for understanding the interactive possibilities within a scene.
Depth perception is a critical component of the RoboAfford-Qwen++ model’s performance in robotic tasks. Evaluations demonstrate a 61.4% success rate in robotic manipulation tasks, where accurate depth understanding is necessary for grasping and interacting with objects. Similarly, the model achieves a 70.0% success rate in robotic navigation tasks, relying on depth perception to effectively map and traverse complex environments, avoid obstacles, and plan collision-free trajectories. These results indicate a strong correlation between the model’s ability to accurately perceive depth and its overall effectiveness in both manipulation and navigation scenarios.
The Echo of Action: Implications for Embodied Intelligence
RoboAfford-Qwen++ represents a notable advancement in robotic intelligence by enabling machines to move beyond merely identifying objects to understanding how those objects can be used. This capability, rooted in affordance reasoning, allows robots to perceive the potential actions associated with an object – whether a handle is for pulling, a surface is for placing, or a pathway is for navigating. The model achieves this through a sophisticated understanding of visual and linguistic cues, enabling it to tackle complex tasks involving manipulation and navigation in unstructured environments. Unlike systems limited to object classification, RoboAfford-Qwen++ empowers robots to plan and execute actions based on a deeper comprehension of the surrounding world, paving the way for more versatile and adaptable robotic systems capable of assisting humans in a wider range of scenarios.
A crucial component of advancing embodied artificial intelligence is rigorous, standardized evaluation, and the RoboAfford-Eval Benchmark directly addresses this need. This benchmark provides a consistent and challenging platform for assessing the ability of Vision-Language Models (VLMs) to reason about affordances – the potential actions an agent can perform with an object or in an environment. By presenting a diverse set of scenarios requiring the identification of possible interactions, RoboAfford-Eval moves beyond simple object recognition and tests a VLM’s understanding of how objects can be used. This standardized framework allows researchers to objectively compare different models and track progress in the field, fostering innovation and accelerating the development of robots capable of more complex and nuanced interactions with the world. The benchmark’s design emphasizes practical, real-world scenarios, ensuring that improvements translate to tangible gains in robotic performance.
A robot’s capacity to navigate and interact with the world hinges on its understanding of spatial affordances – the possibilities for action that an environment offers. This isn’t simply recognizing objects, but grasping how those objects can be used; a chair isn’t just a chair, but a potential seat, a climbing aid, or even a shield. By perceiving these action possibilities, a robot can move beyond pre-programmed routines and dynamically plan actions in response to changing conditions. In cluttered environments, this ability is paramount; a robot must discern pathways through obstacles, identify stable grasping points amidst disarray, and anticipate how its actions will alter the surrounding space. Consequently, advanced robotic systems are increasingly focused on developing this crucial skill, enabling them to operate effectively in the complex, unpredictable realities of human-populated spaces and beyond.
Evaluations reveal that RoboAfford-Qwen++ represents a substantial advancement over previous models, notably RoboPoint. Performance metrics demonstrate an 18.7% increase in accuracy when assessed using the RoboAfford-Eval benchmark, a standardized measure of affordance reasoning. More critically, this translates to a 25.7% improvement in the success rate of actual robotic manipulation tasks. This significant gain isn’t merely an algorithmic refinement; it indicates a heightened capacity for robots to effectively interact with and manipulate objects in real-world scenarios, suggesting the dataset and model are successfully bridging the gap between visual understanding and practical action.
The pursuit of robust robotic systems, as detailed in this work concerning RoboAfford++, echoes a fundamental truth about complexity. The dataset’s ambition-to imbue machines with a deeper understanding of spatial affordances-isn’t about achieving perfect control, but rather about building resilience into the inevitable failures. As John von Neumann observed, “There are no best practices – only survivors.” RoboAfford++ doesn’t promise to solve robotic manipulation and navigation; it propagates a lineage of models capable of enduring the chaotic reality of physical interaction, a system designed to adapt rather than dictate. Order, in this context, is merely a temporary cache against the next unavoidable outage.
What Lies Ahead?
The creation of RoboAfford++ signals not an arrival, but a shifting of the sands. Larger datasets, richer annotations-these are merely expansions of the surface. The true challenge remains stubbornly beneath: the translation of perceived affordance into robust, embodied action. A system can learn to name a grasp, but the physics of execution are less yielding to statistical correlation. Technologies change, dependencies remain.
Future work will inevitably focus on bridging this gap, perhaps through increasingly sophisticated simulation or the integration of proprioceptive feedback. Yet, it is worth remembering that every architectural choice is a prophecy of future failure. The very act of defining “affordance” within a finite schema risks obscuring the infinite possibilities of interaction. A truly adaptable system will need to gracefully navigate the undefined, to learn not just what can be done, but what it hasn’t yet imagined.
The pursuit of multimodal understanding is a necessary, but not sufficient, condition for genuine intelligence. One suspects the limitations will not be in the data, but in the fundamental mismatch between the symbolic world we construct and the messy, analog reality in which robots must operate. The system isn’t a tool; it’s an ecosystem. It grows, or it doesn’t.
Original article: https://arxiv.org/pdf/2511.12436.pdf
Contact the author: https://www.linkedin.com/in/avetisyan/
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2025-11-18 22:59