Beyond Hands and Feet: Teaching Robots to ‘See’ What They Can Do

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Author: Denis Avetisyan

New research explores how large vision-language models can enable robots with unconventional bodies to understand their potential interactions with the world.

The system maps a robot’s perceptual input and physical characteristics to a dynamic scene representation, identifying objects and their potential affordances-[latex] \langle Object, Affordance, Location \rangle [/latex]-through visual language models and geometric triangulation, while semantic similarity metrics refine object recognition and consolidate variance within the established scene graph.
The system maps a robot’s perceptual input and physical characteristics to a dynamic scene representation, identifying objects and their potential affordances-[latex] \langle Object, Affordance, Location \rangle [/latex]-through visual language models and geometric triangulation, while semantic similarity metrics refine object recognition and consolidate variance within the established scene graph.

This review assesses the capabilities and limitations of vision-language models in inferring semantic affordances for non-humanoid robot morphologies, highlighting biases in spatial reasoning and material understanding.

While vision-language models excel at understanding human-object interactions, their applicability to robots with diverse morphologies remains largely unexplored. This work, ‘Assessing VLM-Driven Semantic-Affordance Inference for Non-Humanoid Robot Morphologies’, investigates whether these models can accurately infer object affordances for non-humanoid robots, revealing promising generalization capabilities alongside a consistent bias towards conservative predictions. Specifically, our analysis of VLM performance across multiple robot forms and object categories highlights a pattern of low false positives but high false negatives, particularly in novel manipulation scenarios. How can we refine VLM-driven affordance inference to mitigate this conservatism and unlock the full potential of these models for versatile robotic systems?

Deconstructing Reality: The Affordance Challenge

For robots to operate successfully in real-world settings, a mere identification of objects isn’t sufficient; effective action demands a deeper comprehension of the environment. While object recognition allows a robot to name what it sees – a chair, a table, a door – it doesn’t explain what can be done with those objects. A truly capable robot needs to move beyond this passive perception, developing an understanding of how objects relate to its own capabilities and the potential interactions within a scene. This necessitates a system that doesn’t simply register ‘a cup’, but instead assesses ‘can this cup be grasped, lifted, and used to pour liquid, given the robot’s arm length and grip strength?’ The ability to anticipate and leverage these possibilities – to understand what the environment affords – is paramount for intelligent, adaptable robotic behavior and moves the field beyond simple visual processing.

Intelligent robotic action hinges not simply on recognizing what is present in an environment, but on discerning what that environment allows the robot to do. This is the essence of ‘affordance’ – the actionable possibilities offered by objects and spaces. A chair, for example, doesn’t merely register as a collection of shapes and materials; it affords sitting, standing upon, or even blocking a pathway. Recognizing these possibilities is critical because it enables a robot to move beyond pre-programmed responses and engage in flexible, goal-directed behavior. This concept moves robotics closer to true intelligence, where action isn’t dictated by explicit instructions, but emerges from an understanding of how an agent can meaningfully interact with its surroundings – effectively transforming perception into a plan for action.

Current approaches to robotic affordance – determining how an object can be used – often falter when confronted with the unexpected. Existing systems, frequently reliant on pre-programmed knowledge or limited datasets, struggle to generalize to novel objects not seen during training. This is particularly true for robots with unusual physical designs – those differing significantly from standard humanoid or wheeled platforms. A gripper designed for delicate manipulation may misinterpret the affordances of a heavy, awkwardly shaped object, while a legged robot may fail to recognize climbing possibilities that a drone would easily perceive. The core limitation lies in the difficulty of bridging the gap between visual perception and actionable possibilities, especially when dealing with the vast variability present in real-world environments and the diverse capabilities of robotic bodies. Consequently, robots often require extensive, object-specific programming, hindering their adaptability and limiting their potential for true autonomous operation.

A robot’s capacity to navigate and interact with the world hinges on its ability to construct a meaningful representation of its surroundings. This extends far beyond merely identifying objects; a truly effective scene representation demands an understanding of an object’s inherent attributes – its size, shape, material, and stability – and, crucially, how those properties enable potential interactions. For instance, a robot doesn’t simply ‘see’ a chair; it infers that the chair affords sitting, pushing, or even climbing, based on its structural characteristics. This predictive capacity, linking perception to action, requires the system to model not just what is present, but also what could be done, creating a dynamic understanding of the environment’s possibilities and allowing for flexible, goal-directed behavior even in unfamiliar situations.

Affordance-object inference F1 scores reveal that while the humanoid baseline excels at identifying graspable objects ([latex]Pick[/latex]), performance across all models diminishes for affordances like [latex]Lift[/latex] and [latex]Push[/latex], with non-humanoid robots showing improvement in [latex]Push[/latex] but Claude exhibiting reduced generalization to non-humanoid embodiments and one robot underperforming on [latex]Lift[/latex] due to limitations in spatial awareness.
Affordance-object inference F1 scores reveal that while the humanoid baseline excels at identifying graspable objects ([latex]Pick[/latex]), performance across all models diminishes for affordances like [latex]Lift[/latex] and [latex]Push[/latex], with non-humanoid robots showing improvement in [latex]Push[/latex] but Claude exhibiting reduced generalization to non-humanoid embodiments and one robot underperforming on [latex]Lift[/latex] due to limitations in spatial awareness.

Bridging the Perception Gap: Vision-Language Models

Large-scale vision-language models (VLMs) represent a significant advancement in robotic perception by integrating visual data with natural language understanding. These models, typically trained on extensive datasets of image-text pairs, learn to associate visual features with corresponding linguistic descriptions of object properties and potential actions – known as affordances. This connection allows VLMs to move beyond simple object recognition to inferring how an object can be used or interacted with. By leveraging pre-trained knowledge from these models, robots can generalize to novel objects and environments without requiring task-specific training data, effectively bridging the gap between visual input and actionable understanding of the world.

Vision-Language Models (VLMs) facilitate zero-shot affordance characterization by leveraging pre-training on extensive datasets of paired images and text. This allows a robot, without specific training for a novel object, to infer potential interactions based on the object’s visual features and associated linguistic descriptions learned during pre-training. The VLM identifies the object and, through its learned associations, predicts plausible actions – such as ‘grasp’, ‘push’, or ‘pour’ – even if the robot has never encountered that specific object before. This capability stems from the model’s ability to generalize learned relationships between visual input and action-related language, effectively bridging the perception-action gap for previously unseen objects and environments.

Semantic-affordance mapping leverages vision-language models (VLMs) to establish a connection between perceived objects in an environment and the actions that can be performed on them. This process involves utilizing the VLM to simultaneously identify objects within a visual scene and predict their associated affordances – the possible actions a robot or agent could take with respect to those objects. The output of this mapping is a structured representation that grounds the semantic understanding of objects – their labels – with actionable information, allowing a system to infer how to interact with the environment. This differs from traditional object recognition by extending identification to include potential uses, facilitating more intelligent robotic manipulation and interaction.

Object localization, a foundational step in semantic grounding pipelines, utilizes models such as GroundingDINO to identify and spatially locate objects within visual data. These models operate by receiving a text query – defining the object of interest – and processing an input image to generate bounding box coordinates indicating the object’s position. GroundingDINO, specifically, employs a denoising diffusion backbone and a modular design, enabling it to perform zero-shot object detection and grounding based solely on textual prompts. The resulting bounding box detections provide the necessary visual input for subsequent stages, such as affordance prediction, by isolating the relevant objects within the scene and providing their spatial extent for further analysis.

Using GroundingDINO, semantic-affordance inference accurately identifies object affordances-demonstrated by purple bounding boxes for true positives, red for false positives, and pink for true negatives-across both synthetic videos and real-world scenarios involving household items and construction materials.
Using GroundingDINO, semantic-affordance inference accurately identifies object affordances-demonstrated by purple bounding boxes for true positives, red for false positives, and pink for true negatives-across both synthetic videos and real-world scenarios involving household items and construction materials.

Unveiling the System’s Biases: Refinement Through Observation

Vision-Language Models (VLMs) demonstrate a tendency towards conservative bias in affordance inference, meaning they are optimized to minimize false positive detections at the expense of potentially missing true positive affordances. This prioritization results from the training data and loss functions used, which often penalize incorrect predictions more heavily than omissions. Consequently, VLMs may under-report available interaction possibilities, particularly in scenarios involving complex or ambiguous objects or environments, leading to incomplete or inaccurate robot action planning. This bias impacts performance metrics by reducing recall, even if precision remains high, and necessitates strategies to balance the trade-off between minimizing errors and maximizing opportunity detection.

Accurate affordance inference is directly correlated with a vision-language model’s (VLM) capacity for spatial reasoning and material property understanding. These capabilities enable the VLM to move beyond simple object recognition and assess how an object’s physical characteristics – including shape, size, and material composition – interact with its surrounding environment and potential agent actions. Specifically, understanding spatial relationships allows the model to determine if an object is reachable, graspable, or obstructs movement, while material understanding informs the feasibility of interactions such as pushing, lifting, or cutting. The integration of these components results in a more complete and reliable prediction of potential affordances, improving the VLM’s ability to support robotic task planning and execution.

The performance of semantic-affordance mapping is directly impacted by the specific objectives and limitations of the robotic system. Affordance inference is not a universally applicable process; rather, it necessitates tailoring to the robot’s intended use and operational environment. Variations in robot morphology, degrees of freedom, and sensor capabilities introduce constraints that influence which actions are feasible and therefore represent valid affordances. Consequently, successful implementation requires defining task-specific parameters, including acceptable error margins, prioritization of certain actions over others, and adaptation to the robot’s physical characteristics to accurately map semantic understanding to actionable possibilities.

Experimental results indicate that providing Vision-Language Models (VLMs) with brief physical descriptions of non-humanoid robots significantly improves affordance inference performance. Specifically, the mean F1 score for non-humanoid robot affordance detection reached 0.50 when these descriptions were included, a marked improvement over results obtained without such descriptions which were negatively impacted by human-centric biases. Furthermore, supplementing the physical description with a concise task description yielded an additional performance gain, increasing the mean F1 score by 0.03 to 0.10.

Evaluation of the Claude large language model on a humanoid robot affordance inference baseline yielded a mean F1 score of 0.53. This score represents performance on tasks involving identifying potential interactions between a humanoid robot and its environment. The metric, F1 score, balances precision and recall, providing a comprehensive assessment of the model’s ability to correctly identify both present and absent affordances. This baseline performance serves as a comparative point for evaluating the impact of interventions, such as providing physical descriptions or task constraints, on affordance inference accuracy.

Confusion matrices reveal that the VLMs generally prioritize correctly identifying affordances (true positives) or exhibit a conservative bias towards false negatives, with the 'Lift' and 'Cut' affordances for non-humanoid robots being notable exceptions.
Confusion matrices reveal that the VLMs generally prioritize correctly identifying affordances (true positives) or exhibit a conservative bias towards false negatives, with the ‘Lift’ and ‘Cut’ affordances for non-humanoid robots being notable exceptions.

Beyond Mimicry: Towards Truly Intelligent Machines

Recent advancements in robotics leverage the synergy between Vision-Language Models (VLMs) and robust semantic-affordance mapping to dramatically enhance a robot’s capacity for environmental interaction. This integration allows robots to not merely see an object, but to understand what actions are possible with it – whether a door can be opened, a chair can be sat upon, or a tool can be wielded. By grounding visual perception in semantic understanding of affordances – the possibilities for action – robots move beyond rigid, pre-programmed responses. The system enables flexible adaptation to unfamiliar objects and situations, fostering intelligent behavior in dynamic environments and paving the way for more intuitive and versatile human-robot collaboration. This capability represents a significant step towards robots that can genuinely understand and meaningfully engage with the world around them.

Traditional robotics often relies on explicitly programmed sequences, limiting a robot’s performance to well-defined scenarios. However, a shift towards adaptable intelligence allows robots to transcend these limitations by inferring how to interact with objects and environments in real-time. This capability isn’t about memorizing responses, but rather understanding the potential for action – what an object affords the robot. By leveraging visual learning models and semantic mapping, robots can now analyze novel situations, deduce appropriate actions, and execute complex tasks without prior instruction. This move beyond pre-programmed behaviors is critical for deployment in dynamic, unstructured environments – from assisting in disaster relief to performing intricate assembly work – where predictability is low and adaptability is paramount.

Expanding the scope of robotic intelligence beyond humanoid designs is now becoming increasingly feasible through generalized affordance understanding. Traditionally, robots have been programmed with specific actions tied to their morphology – a gripper designed for grasping, wheels for rolling. However, this new approach decouples the understanding of what an object allows – its affordances, like ‘supportable’ or ‘rollable’ – from the robot’s physical form. This means a robot resembling a drone, a quadruped, or even a modular assembly can interpret the same environmental cues and act accordingly. Consequently, applications extend far beyond tasks suited to human-like robots; consider automated agricultural systems utilizing diverse robotic platforms, adaptable search-and-rescue teams with varied locomotion, or even reconfigurable manufacturing cells where robots dynamically assemble based on task requirements – all unified by a shared understanding of how objects can be interacted with, regardless of the robot performing the action.

Current research endeavors are directed towards embedding this semantic understanding within sophisticated long-term planning frameworks, effectively bridging the gap between perception and action for robotic systems. The integration aims to move beyond immediate responses to environmental cues, enabling robots to formulate and execute complex sequences of actions to achieve extended goals. This involves developing algorithms that can not only identify potential interactions with objects – grasping, pushing, assembling – but also strategically prioritize them based on predicted outcomes and overall task objectives. Ultimately, this pursuit seeks to create genuinely autonomous agents capable of independent problem-solving, adaptation, and sustained operation within dynamic and unpredictable environments, mirroring the cognitive flexibility observed in biological intelligence.

The study meticulously dismantles the assumption that large language-vision models inherently grasp the relationship between objects and action possibilities, revealing a surprising fragility in their spatial and material reasoning. This echoes Blaise Pascal’s sentiment: “The eloquence of the body is more impressive than the eloquence of the tongue.” Just as mere articulation doesn’t guarantee comprehension, a VLM’s ability to describe an affordance doesn’t ensure it understands the physical implications for a non-humanoid robot. The researchers demonstrate, through rigorous testing, that these models often prioritize superficial cues over genuine physical constraints, a limitation that necessitates the development of more robust, task-conditioned prompting strategies and datasets-essentially, teaching the ‘body’ of the model to align with physical reality.

Beyond the Horizon

The demonstrated capacity of large language-vision models to tentatively grasp robotic affordances, even for morphologies diverging significantly from the humanoid norm, feels less like a solution and more like a carefully illuminated problem. The study subtly reveals that these models don’t so much understand interaction as they statistically correlate visual cues with linguistic descriptions-a trick, admittedly, that has served evolution quite well. But the biases inherent in those correlations-spatial misinterpretations, material naiveté-aren’t bugs, they’re the architecture of prediction itself, exposed.

Future inquiry shouldn’t focus solely on refining prompts, though task-conditioned guidance is a logical next step. The more compelling direction lies in systematically breaking the models. Purposefully constructing scenarios that highlight these failures-ambiguous material properties, deceptive geometries, interactions demanding true physical simulation-will reveal the fault lines in their “understanding”. This isn’t about achieving perfect prediction; it’s about mapping the boundaries of what’s currently unthinkable for these systems.

Ultimately, the value may not reside in replicating human intuition, but in forging a distinctly non-human approach to interaction. If robots are to navigate a world built for us, they needn’t mirror our cognitive limitations. Instead, the challenge is to leverage their unique perspective-unburdened by preconceived notions-to discover affordances we’ve overlooked, connections we’ve failed to see. Chaos, after all, is not an enemy, but a mirror of architecture reflecting unseen connections.

Original article: https://arxiv.org/pdf/2604.19509.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-04-22 13:44