Can Robots ‘See’ What They Can Do? Bridging the Perception Gap for Non-Humanoid Machines

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Author: Denis Avetisyan

New research explores how large vision-language models can help robots understand their interaction possibilities with the world, even with unconventional body plans.

A robotic system constructs a semantic map of its surroundings by integrating visual perception with natural language understanding of its own physical capabilities, identifying objects and their potential affordances-such as manipulability-through a vision-language model, localizing these affordances in 3D space using odometry and bounding box detection, and refining the map by consolidating similar objects within a defined distance to reduce labeling inconsistencies.
A robotic system constructs a semantic map of its surroundings by integrating visual perception with natural language understanding of its own physical capabilities, identifying objects and their potential affordances-such as manipulability-through a vision-language model, localizing these affordances in 3D space using odometry and bounding box detection, and refining the map by consolidating similar objects within a defined distance to reduce labeling inconsistencies.

This review assesses the potential and limitations of using vision-language models to infer semantic affordances for non-humanoid robots, highlighting biases in spatial reasoning and material understanding.

Despite advances in robotic perception, reliably inferring how robots can interact with objects-a robot’s ‘affordances’-remains a challenge, particularly for non-humanoid designs. This work, ‘Assessing VLM-Driven Semantic-Affordance Inference for Non-Humanoid Robot Morphologies’, investigates the capacity of large vision-language models (VLMs) to predict these affordances for robots with diverse morphologies. Our analysis reveals that while VLMs demonstrate promising generalization, they exhibit a tendency toward conservative predictions-high false negative rates-especially when encountering novel object manipulations. This raises a critical question: how can we augment VLMs to unlock more robust and adaptable robotic behaviors without compromising inherent safety?

Perceiving Potential: Beyond Object Recognition

For robots to truly interact with the world, simply identifying what objects are present isn’t enough; they must grasp how those objects can be used. A robust environmental understanding demands a shift beyond basic object recognition toward discerning actionable possibilities – what an object allows the robot to do. This necessitates perceiving not just a chair as a ‘chair’, but as something that can be sat upon, climbed, or used as a temporary shield. Current robotic systems often falter when faced with unfamiliar scenarios precisely because they lack this deeper contextual awareness, hindering their ability to adapt and execute tasks effectively in dynamic, real-world settings. The focus, therefore, is moving towards enabling machines to perceive environments not as static collections of objects, but as landscapes of potential actions.

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 – a concept known as affordance. This moves beyond identifying an object as, for example, a chair, to understanding that the chair affords sitting, standing upon, or even pushing. Affordances are therefore relational properties, determined by both the object itself and the capabilities of the agent perceiving it; a staircase affords climbing to a human, but might present an impassable barrier to a wheeled robot. Consequently, a robust understanding of affordances is critical for enabling robots to navigate and interact with complex environments in a flexible and goal-directed manner, representing a significant leap beyond pre-programmed responses and towards true environmental intelligence.

Current approaches to robotic affordance – determining how an object can be used – frequently falter when confronted with the unexpected. Existing systems, often reliant on pre-programmed knowledge or limited datasets, struggle to generalize beyond familiar objects and predictable scenarios. This presents a significant hurdle, as real-world environments are inherently dynamic and contain countless novel items. Moreover, a robot’s ability to perceive affordances is inextricably linked to its own physical capabilities; a design optimized for one morphology may be wholly inadequate for another. Consequently, a gripper adept at manipulating spheres will perceive drastically different actionable possibilities than a robot with legs and a pushing mechanism, highlighting the need for methods that account for both environmental features and the agent’s embodiment to achieve truly robust and adaptable behavior.

A robot’s ability to navigate and interact with the world hinges on constructing a comprehensive scene representation, extending far beyond simply identifying objects present. This representation must encompass not only what is there – recognizing a chair, for instance – but also its attributes – its size, material, and stability – and, critically, the potential interactions it affords. A robust system understands a chair isn’t just a static form; it’s something that can be sat upon, climbed, or even used as a shield. Successfully modeling these possibilities requires moving beyond purely visual data to incorporate physical properties and contextual reasoning, allowing the robot to predict how objects will behave and how its own actions will affect them, ultimately enabling more flexible and intelligent behavior in dynamic environments.

Affordance inference F1 scores, averaged over five trials, reveal that while the humanoid baseline excels at identifying graspable objects ([latex]Pick[/latex]), its tendency to predict human-like affordances for actions like lifting and pushing limits overall performance, whereas non-humanoid robots show improved performance on pushing but diminished generalization in identifying graspable objects (Claude) or exhibit spatial awareness issues when lifting.
Affordance inference F1 scores, averaged over five trials, reveal that while the humanoid baseline excels at identifying graspable objects ([latex]Pick[/latex]), its tendency to predict human-like affordances for actions like lifting and pushing limits overall performance, whereas non-humanoid robots show improved performance on pushing but diminished generalization in identifying graspable objects (Claude) or exhibit spatial awareness issues when lifting.

Bridging Perception and Action: Vision-Language Models

Large-scale vision-language models (VLMs) represent a significant advancement in robotic affordance perception by integrating visual input with linguistic understanding. These models, typically trained on extensive datasets of paired images and text, learn to associate visual features with semantic concepts and, crucially, potential actions. This connection allows VLMs to move beyond simple object recognition to infer what an object allows a robot to do – its affordance. The models achieve this by leveraging the relationships learned during training to predict relevant action words or phrases given a visual input, effectively bridging the gap between perception and action planning. This approach contrasts with traditional methods reliant on manually defined object-action mappings, offering greater generalization and adaptability to novel objects and environments.

Vision-Language Models (VLMs) facilitate zero-shot affordance characterization by leveraging pre-training on extensive datasets of paired images and text. This allows robots to infer potential interactions with objects without requiring task-specific training data for those particular objects. The models achieve this by associating visual features with linguistic descriptions of actions; when presented with a novel object, the VLM can identify the object and, based on its learned associations, predict plausible actions – such as ‘grasp’, ‘push’, or ‘pour’ – that could be performed on it. This capability is distinct from traditional robotic approaches that rely on pre-defined action primitives or require extensive re-training for each new object encountered.

Semantic-affordance mapping leverages vision-language models to establish a correspondence between perceived objects in an environment and the actions that can be performed on them. This process involves utilizing a VLM to identify objects within a visual scene and then querying the same model to determine the potential affordances – or actionable properties – associated with those objects. The output is a semantic representation that ‘grounds’ the object’s function within the environment, effectively linking visual perception with linguistic understanding of possible interactions. This allows a system to infer, for example, that a ‘chair’ can be ‘sat on’ or a ‘door’ can be ‘opened’ without explicit programming for each instance.

Object localization, a foundational step in semantic grounding pipelines, utilizes models such as GroundingDINO to identify and delineate objects within visual data. These models employ techniques like bounding box prediction to pinpoint object locations, providing the spatial coordinates necessary for subsequent affordance inference. GroundingDINO, specifically, leverages a transformer-based architecture trained on large datasets of image-text pairs, enabling it to associate textual queries with corresponding visual regions. The output of object localization is typically a set of bounding boxes, each associated with a confidence score and a predicted object class, which then serves as input for vision-language models to determine potential interactions or affordances related to the identified objects.

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

Refining Perception: Addressing Bias and Context

Vision-Language Models (VLMs) demonstrate a tendency towards conservative bias in affordance prediction, meaning they are calibrated to minimize false positive identifications at the expense of potentially overlooking valid affordances – resulting in a higher rate of false negatives. This prioritization stems from the model’s training data and objective functions, which often emphasize avoiding incorrect predictions over comprehensively identifying all possible interactions. Consequently, while VLMs may reliably avoid suggesting impossible actions, they may fail to recognize all feasible interactions with an object or environment, limiting their utility in applications requiring complete affordance awareness.

Accurate affordance inference is directly correlated with a vision-language model’s capacity for spatial reasoning and material understanding. These capabilities allow the model to move beyond simple object recognition and instead interpret the physical properties of objects and their relationship to the robot’s potential actions. Specifically, understanding an object’s geometry, size, and material composition – coupled with an assessment of the surrounding environment – enables the model to determine not only what an object is, but how it can be interacted with. Deficiencies in either spatial or material perception will result in incomplete or inaccurate affordance predictions, limiting the robot’s ability to successfully perform tasks requiring manipulation or navigation.

The performance of semantic-affordance mapping is directly contingent on the specific task the robot is intended to perform and its operational parameters. Affordance inference is not a universally applicable process; rather, it requires tailoring to the robot’s goals and the constraints of its environment. Variations in task requirements, such as manipulating specific objects or navigating particular terrains, necessitate adjustments to the mapping process. Operational parameters, including sensor range, degrees of freedom, and payload capacity, further define the feasible set of affordances and influence the accuracy of their prediction. Consequently, a generalized semantic-affordance map is insufficient; adaptation and refinement based on task and operational context are essential for effective robot behavior.

Experimentation with Vision-Language Models (VLMs) indicates that providing concise physical descriptions of non-humanoid robots significantly improves affordance inference accuracy. Without such descriptions, results are demonstrably skewed by human-centric biases inherent in the training data. Specifically, the mean F1 score for non-humanoid robot affordance detection reached 0.50 when provided with a physical description, representing a substantial improvement over baseline performance. Furthermore, incorporating a simple task description alongside the physical description yielded an additional improvement in the mean F1 score, ranging from 0.03 to 0.10, indicating that contextual information further refines affordance prediction.

Performance of the Claude large language model established a humanoid baseline F1 score of 0.53 during affordance inference experimentation. This score represents the model’s ability to correctly identify potential interactions with humanoid robots without the addition of specific physical or task descriptions. The metric was calculated based on precision and recall, evaluating the balance between minimizing false positives and maximizing true positives in identifying affordances. This baseline provides a comparative measure against which the impact of contextual prompting – specifically, concise physical descriptions and task constraints – can be assessed when applied to non-humanoid robot affordance inference.

Confusion matrices reveal that across all visual language models, performance is largely biased towards correctly identifying affordances (green) or conservatively failing to identify them (orange), with the 'Lift' and 'Cut' affordances for non-humanoid robots showing the most significant errors.
Confusion matrices reveal that across all visual language models, performance is largely biased towards correctly identifying affordances (green) or conservatively failing to identify them (orange), with the ‘Lift’ and ‘Cut’ affordances for non-humanoid robots showing the most significant errors.

Towards Robust Robotic Intelligence

Recent advancements in robotics leverage the synergy between Vision-Language Models (VLMs) and robust semantic-affordance mapping to facilitate more nuanced environmental interaction. This integration allows robots to perceive not just what objects are, but also how they can be used – understanding, for instance, that a chair affords sitting, or a doorknob affords turning. By grounding visual perception in semantic understanding of affordances, robots move beyond pre-defined action sequences and begin to exhibit flexible behavior in response to diverse and unpredictable scenarios. The system creates a dynamic representation of the environment, enabling robots to infer potential interactions and select appropriate actions based on context, ultimately fostering a higher degree of intelligence and adaptability in complex, real-world settings.

Traditional robotics often relies on meticulously pre-programmed behaviors, limiting a robot’s capacity to respond effectively to unforeseen circumstances. This new methodology circumvents those limitations by equipping robots with the capacity for adaptive reasoning. Rather than executing a fixed sequence of actions, the system enables robots to interpret their surroundings and deduce appropriate responses – even in entirely novel situations. This isn’t simply about recognizing objects; it’s about understanding how those objects can be manipulated and integrated into a solution for a given task. Consequently, robots can now tackle complex challenges that demand improvisation and flexibility, moving beyond the constraints of rigid, pre-defined routines and exhibiting a degree of intelligence previously unattainable.

Expanding the scope of robotic intelligence beyond humanoid forms is proving crucial for widespread application, and a key element lies in generalized affordance understanding. Traditionally, robots have been taught to interact with objects based on pre-defined parameters, limiting their adaptability. However, by enabling robots – regardless of their morphology, be it quadrupedal, aerial, or entirely novel – to perceive how an object can be used – its affordances – this system unlocks a dramatically expanded operational space. This means a robot designed for agricultural tasks can identify and manipulate a diverse array of tools and produce, while a drone can assess and interact with objects in a disaster zone, all without specific pre-programming for each scenario. Consequently, the development of generalized affordance understanding isn’t simply about improving existing robotic capabilities; it’s about enabling a future where robots can seamlessly integrate into, and assist within, a far wider range of real-world environments and tasks.

Current research endeavors are directed toward embedding this semantic understanding within comprehensive, long-term planning frameworks, ultimately striving to forge genuinely autonomous robotic agents. This integration transcends simple reactive behaviors, enabling robots to not only perceive affordances but also to strategically sequence actions over extended periods to achieve complex goals. The envisioned systems will leverage predictive modeling and reinforcement learning to anticipate future states, evaluate potential outcomes, and refine decision-making processes – effectively granting robots the capacity for foresight and adaptability. By coupling immediate perceptual understanding with prospective reasoning, these advancements promise to unlock applications requiring sustained, independent operation in dynamic and unpredictable environments, moving beyond narrowly defined tasks toward generalized intelligence.

The pursuit of robotic affordance, as outlined in the paper, often leads to unnecessarily complex systems. Researchers attempt to imbue machines with human-like understanding, forgetting that effective action rarely demands comprehensive cognition. They called it a framework to hide the panic, this eagerness to model the world completely. Alan Turing observed, “Sometimes people who are unhappy tend to look at the world as if through a grey veil.” This seems apt; the limitations of current VLMs in spatial reasoning and material understanding-biases the paper diligently exposes-are not failures of the technology itself, but reflections of an overzealous attempt to replicate the full spectrum of human perception rather than focusing on the essential cues for action. Simplicity, it appears, remains a distant ideal.

Future Directions

The observed performance of large language-vision models in inferring affordances for non-humanoid robotics reveals a predictable truth: correlation is not comprehension. The models demonstrate a capacity to associate visual cues with pre-existing linguistic structures, but this does not equate to a robust understanding of physics, material properties, or spatial relations. The limitations are not surprising; they are inherent to a system built upon pattern recognition, not causal inference.

Future work must address the fragility of these systems. Task-conditioned prompting offers a potential, if temporary, amelioration. However, true progress necessitates datasets that prioritize verifiable affordances – not merely plausible ones – and a move beyond purely visual inputs. Incorporating tactile sensing, force feedback, and even rudimentary proprioception could ground the models in a more physically-consistent reality.

Ultimately, the pursuit of robotic intelligence through scaled models of language and vision may prove a distraction. The problem is not a lack of data, but a fundamental mismatch between the architecture of the system and the demands of embodied cognition. Emotion, it should be remembered, is merely a side effect of structure. Clarity, therefore, is compassion for cognition, and the path forward demands a ruthless pruning of complexity.

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 12:07