Robots That Understand What Things Are For

Author: Denis Avetisyan

A new framework, SAGA, empowers robots to perform complex manipulation tasks in real-world environments by grounding semantic understanding in structured affordances.

SAGAexpresses sophisticated mobile manipulation capabilities through an affordance-based task representation, explicitly grounding objectives as three-dimensional heatmaps within the perceived environment to disentangle semantic intent from visuomotor control, thereby facilitating generalization across varied environments, task goals, and user-defined specifications.

SAGA decouples task representation from low-level control, enabling robust generalization in open-world mobile manipulation via multimodal foundation models.

Despite advances in robotic control, achieving truly generalizable mobile manipulation remains a significant challenge due to the difficulty of bridging semantic understanding and low-level action. This paper introduces SAGA-Structured Affordance Grounding for Open-World Mobile Manipulation-a novel framework that disentangles high-level task intent from visuomotor control by explicitly grounding objectives in observed environmental affordances. Leveraging multimodal foundation models, SAGA learns a structured task representation and translates it into 3D heatmaps, enabling robust performance across diverse tasks specified via language, demonstration, or point selection. Does this approach of structured affordance grounding represent a scalable pathway toward truly generalist robots capable of operating in complex, real-world environments?

The Fragility of Embodied Intelligence

Conventional robotics often falters when confronted with unfamiliar surroundings because of a fundamental dependence on explicitly programmed instructions and restricted sensory input. These systems are typically designed for highly specific tasks within controlled environments, making them brittle and unable to adapt to unexpected changes or novel situations. A robot programmed to assemble a product on a static conveyor belt, for example, would struggle if the belt stopped, the parts were slightly misaligned, or a new object appeared in its workspace. This limitation stems from a reliance on precise, pre-defined movements and an inability to effectively interpret and respond to the nuances of a dynamic, real-world setting – hindering the development of truly versatile and autonomous robotic systems.

The pursuit of genuinely autonomous robots necessitates a shift beyond mere obedience to commands; a robot must grasp the intent behind a request, not just the prescribed actions. This demands a sophisticated internal representation of tasks that encompasses both the desired outcome – what needs to be achieved – and a flexible understanding of how to interact with the environment to accomplish it. Simply put, a robot cannot navigate unforeseen obstacles or adapt to changing conditions if it only knows to move forward, rather than understanding it needs to reach a specific goal, potentially by maneuvering around barriers or utilizing different tools. Such a representation allows for generalization – the ability to apply learned skills to novel situations – a crucial step toward creating robots capable of independent operation in the real world.

The pursuit of truly versatile robots is hampered by limitations in how tasks are currently defined for them. Existing methods typically rely on rigid, pre-defined parameters that struggle when confronted with the unpredictable nature of real-world environments; a robot programmed to stack blocks on a flat surface may fail utterly when presented with an uneven floor or oddly shaped blocks. This inflexibility arises because these representations often focus on how to perform an action-specific motor commands-rather than what the task actually entails – the desired outcome or goal. Consequently, robots struggle to generalize learned skills to novel situations, demanding constant re-programming or extensive training for even minor variations. Bridging this gap requires developing task representations that prioritize goals and allow for adaptable, creative problem-solving, rather than simply executing pre-determined sequences – a crucial step toward building robots capable of true autonomy and broad applicability.

SAGA enables zero-shot and few-shot robotic manipulation in open-world environments by translating language instructions, selected points, or demonstrations into 3D affordance heatmaps that guide action prediction.

Grounding Action in Affordances: A Logical Imperative

Structured Affordance Grounding for Action (SAGA) introduces a framework for open-world mobile manipulation predicated on the explicit definition of tasks as paired affordances and entities. This approach moves beyond traditional action specification by first identifying potential interactions – the affordances, such as “grasp,” “push,” or “place” – and then associating them with specific objects or entities within the environment. By decomposing tasks into these affordance-entity pairs, SAGA enables a more modular and interpretable representation of desired actions, facilitating both task planning and generalization to novel scenarios. The framework fundamentally shifts the focus from directly specifying robotic actions to defining what should be done with which object, allowing for a more flexible and adaptable robotic system.

SAGA utilizes multimodal foundation models to integrate and interpret data from diverse sources – language, vision, and robot state – enabling a more seamless translation of high-level instructions into physical actions. These models are trained on large datasets encompassing language-image-action triplets, allowing them to establish correlations between linguistic commands, visual scene understanding, and the corresponding robotic manipulations required to fulfill the request. This approach circumvents the need for explicitly programmed behaviors for each object or scenario; instead, the robot infers the appropriate action based on its learned understanding of affordances and the context provided by the multimodal input. Consequently, task specification becomes more intuitive, as users can communicate goals using natural language without requiring specialized robotic terminology or precise procedural descriptions.

Traditional robotic manipulation approaches often rely on pre-programmed skills tied to specific objects and environments, limiting adaptability. SAGA, by framing tasks as affordances – the potential actions an object enables – decouples task specification from precise object identity or scene configuration. This representation allows the system to identify and utilize functionally similar interaction possibilities across novel objects and environments without requiring re-training or explicit adaptation. Consequently, a robot operating under the SAGA framework can successfully perform a task – such as “push the object” – with an object it has never encountered before, or in a previously unseen room, by recognizing the relevant affordance and executing the appropriate action based on that affordance.

Affordance Heatmaps, central to the SAGA framework, are spatial probability distributions overlaid onto the robot’s perception of its environment. These heatmaps represent the likelihood of successful interaction at each point in space, based on the identified objects and their potential affordances. Generated using multimodal foundation models, the heatmaps encode information regarding grasp points, manipulation stability, and collision avoidance. During robotic control, these heatmaps function as a costmap for path planning and action selection, guiding the robot towards areas with high interaction probability and simultaneously avoiding obstacles or unstable configurations. The spatial resolution of the heatmaps allows for precise control in cluttered scenes, enabling the robot to adapt its actions based on the specific geometry of the environment and the affordances of nearby objects.

SAGA demonstrates successful and robust mobile manipulation in cluttered, novel environments by effectively grounding task objectives and achieving high success rates across various long-horizon scenarios, as shown by its affordance heatmaps and execution frames.

Conditional Diffusion Policies: A Probabilistic Approach to Control

SAGA utilizes a Conditional Diffusion Policy to generate robotic actions by modeling the probability distribution of actions given both visual inputs and task specifications. This policy is trained to map high-dimensional visual observations, typically from onboard cameras, and encoded task representations – which define the desired goal – to a distribution over possible robot actions. Instead of directly predicting a single action, the diffusion process learns to iteratively refine a random action, conditioned on the visual and task inputs, ultimately generating a diverse set of plausible actions. This probabilistic approach allows the robot to handle ambiguous situations and adapt to variations in the environment, as the generated actions are not limited to a single, deterministic output.

Diffusion-based policies enhance robotic control by modeling action distributions rather than directly predicting discrete actions. This allows the system to sample from a broader, more diverse set of potential actions, increasing the probability of finding a successful solution, particularly in scenarios with high dimensionality or uncertainty. Unlike deterministic policies which can become stuck in local optima, the stochastic nature of diffusion models enables continued exploration and adaptation to novel situations. This characteristic is critical for robustness, as the robot is less susceptible to failures caused by slight variations in initial conditions or unforeseen environmental changes, ultimately leading to more reliable performance across a range of tasks.

The robotic control policy within SAGA utilizes point cloud data as a primary input for environmental perception. Point clouds, comprised of 3D points representing the surface of objects, provide a detailed geometric representation of the robot’s surroundings. This allows the system to directly process raw sensory information, bypassing the need for intermediate representations like meshes or feature vectors. The policy then uses this point cloud data to identify object locations, shapes, and spatial relationships, which are crucial for both navigation and manipulation tasks in complex scenes. Processing point clouds directly enables the robot to adapt to varying lighting conditions and cluttered environments, contributing to improved robustness and performance.

Evaluations of the SAGA system demonstrate a statistically significant performance improvement over four baseline robotic control methods – DP3, CodeDiffuser, SKIL, and VLA Model – across nine previously unseen mobile manipulation tasks. Comparative analysis reveals consistently higher success rates achieved by SAGA in these challenging scenarios, indicating enhanced robustness and adaptability in complex environments. Quantitative results confirm that SAGA outperforms the established benchmarks, establishing its efficacy in generalizing to novel tasks without requiring retraining or fine-tuning on the specific environment or object configurations.

The SAGA policy was trained using 2,410 demonstration trajectories from diverse scenes representing all eight tested affordance types, as illustrated by the example scenes and their corresponding distribution.

Adaptive Learning: The Key to Robust Robotic Intelligence

SAGA’s adaptability hinges on a process called ‘Heatmap Tuning’, a sophisticated method of refining how the system perceives and responds to new tasks. Rather than rigidly applying pre-programmed knowledge, the framework dynamically adjusts its internal representation of a task based on the specifics of its environment. This is achieved by generating ‘heatmaps’ – visual overlays highlighting areas of importance – and iteratively refining them through interaction. The system effectively learns which aspects of a task are most crucial in a given setting, allowing it to prioritize relevant information and ignore distractions. This optimization process isn’t simply about recognizing objects; it’s about understanding the relationship between objects and actions, and tailoring its approach to maximize success in novel situations. Consequently, SAGA can quickly generalize its skills, demonstrating proficiency in unseen scenarios with minimal training data.

The system exhibits a remarkable capacity for skill acquisition, rapidly learning new tasks through a combination of observational learning and natural language guidance. Unlike traditional robotic systems requiring extensive training, this approach enables proficiency with as few as ten demonstrations of a desired behavior. This accelerated learning isn’t simply rote memorization; the system effectively generalizes acquired knowledge, allowing it to successfully perform variations of the learned task in previously unseen scenarios. This ability to extrapolate from limited examples signifies a significant step towards creating robots capable of operating autonomously and adapting to the unpredictable demands of real-world environments, moving beyond pre-programmed routines to embrace flexible and responsive action.

The true potential of robotic systems lies not just in performing pre-programmed actions, but in navigating the inherent unpredictability of real-world environments. Deploying robots beyond controlled laboratory settings demands a capacity to address unforeseen obstacles and dynamic situations – a capability often lacking in traditional robotics. Unexpected variations in lighting, object placement, or even the introduction of entirely novel objects can easily derail a robot reliant on rigid programming. However, systems exhibiting robust adaptability, like SAGA, offer a pathway to overcome these challenges, allowing robots to function reliably amidst the complexities of everyday life and ultimately unlocking their usefulness in diverse and practical applications, from assisting in homes to operating in disaster zones.

The convergence of visual and linguistic understanding within the system promises a future of significantly more natural human-robot collaboration. Rather than requiring precise programming or complex demonstrations, the robot can interpret instructions given in everyday language, coupled with observations of the desired task. This allows for a level of flexibility previously unattainable; a user can, for instance, guide a robotic assistant by saying “pick up the red block,” while simultaneously showing the robot which object is intended, creating a synergistic learning loop. This combined input dramatically reduces ambiguity and accelerates the robot’s ability to grasp new tasks, moving beyond rigid, pre-defined actions and toward a truly adaptable and collaborative partner in a variety of real-world scenarios.

SAGA demonstrates consistent performance gains over baseline methods and a zero-shot model in few-shot adaptation tasks with only 10 demonstrations, as revealed by heatmap tuning.

Towards Generalist Robots: A Logical Progression

SAGA marks a pivotal advancement in robotics, edging closer to the long-held ambition of creating truly generalist robots. Unlike specialized machines designed for single tasks, SAGA demonstrates an ability to perform a diverse array of actions within real-world, unstructured settings – environments characterized by unpredictable layouts and objects. This capability stems from a novel framework that allows the robot to adapt to new situations without extensive retraining, a crucial step towards widespread robotic assistance. By successfully navigating and manipulating objects across multiple tasks, SAGA signifies a move away from narrow artificial intelligence and towards machines that can function with the versatility and adaptability seen in living organisms, promising robots that can seamlessly integrate into and assist within complex human environments.

The development of SAGA signifies not an endpoint, but a launchpad for future robotic capabilities. Current research endeavors are directed towards expanding the system’s operational scope, moving beyond the initial set of demonstrated tasks to encompass more intricate and varied challenges within increasingly complex real-world environments. Simultaneously, a crucial focus lies in bolstering the system’s capacity for efficient learning; the goal is to achieve robust performance with even fewer training examples, thereby reducing the substantial data requirements that often hinder the deployment of advanced robotic systems. This pursuit of data efficiency promises to accelerate the creation of adaptable robots capable of quickly mastering new skills and seamlessly integrating into dynamic, unstructured settings.

The convergence of sophisticated perception, robust reasoning, and precise control systems promises a transformative leap in robotics. These integrated capabilities move beyond pre-programmed routines, enabling robots to interpret complex, real-world scenarios and adapt their actions accordingly. Such advancements aren’t merely about automation; they facilitate genuine collaboration between humans and robots, where machines can anticipate needs, offer assistance, and work alongside people in dynamic environments. This synergistic potential extends to diverse applications, from manufacturing and logistics to healthcare and disaster response, ultimately unlocking a future where robots augment human capabilities and address complex challenges with unprecedented efficiency and adaptability.

The SAGA framework achieves a notable advancement in robotic learning through exceptional data efficiency. Unlike previous generalist robot policies that rely on massive datasets – often exceeding tens of thousands of demonstration trajectories – SAGA successfully learns a diverse range of skills from a comparatively small collection of just 2,410 examples. This two-orders-of-magnitude reduction in data requirements represents a significant leap forward, lowering the barrier to entry for developing adaptable robots and promising faster, more practical deployment in real-world scenarios where gathering extensive training data is costly or impossible. The ability to learn effectively from limited data suggests a more robust and adaptable approach to robotic intelligence, paving the way for robots capable of quickly mastering new tasks with minimal human intervention.

SAGA consistently outperforms existing methods in zero-shot mobile manipulation across nine unseen tasks, demonstrating its robust ability to generalize to new scenarios.

The pursuit of robust mobile manipulation, as demonstrated by SAGA, necessitates a formalization of action and environment interaction. This echoes John von Neumann’s assertion: “If people do not believe that mathematics is simple and elegant and if they are not excited by it, that is no fault of the mathematics.” SAGA’s structured affordance grounding exemplifies this principle; by decoupling semantic understanding from low-level control, the framework achieves a mathematical purity in task representation. This allows for provable generalization across diverse scenarios, moving beyond merely ‘working on tests’ to a solution grounded in verifiable correctness. The elegance lies not simply in achieving functionality, but in the mathematical rigor of the underlying approach.

What’s Next?

The presentation of SAGA, while a demonstrable step toward decoupling semantic reasoning from robotic actuation, does not, of course, resolve the fundamental challenge. The framework relies on grounding affordances – a process which, despite the authors’ efforts, remains a probabilistic approximation. True elegance would demand a provable correspondence between perceived affordance and resultant action – a guarantee, not a high likelihood. Current reliance on multimodal foundation models introduces an opacity that is, from a strictly logical perspective, unacceptable. A ‘black box’ that occasionally succeeds is not a solution; it is a cleverly disguised contingency.

Future work must address the inherent ambiguity in affordance representation. The current paradigm, while flexible, risks conflating possibility with practicality. A chair affords sitting, yes, but not if the robot’s kinematic constraints preclude approach. The field requires a formalization of robotic capability, a rigorous language for describing what a robot can actually do, independent of semantic interpretation. Only then can affordance grounding become a deterministic process.

Ultimately, the pursuit of open-world manipulation is not merely about building robots that appear intelligent. It demands a commitment to mathematical certainty. Until robotic action is founded upon provable algorithms, rather than empirical observation, the promise of truly generalizable intelligence will remain elusive-a sophisticated illusion, rather than a solved problem.

Original article: https://arxiv.org/pdf/2512.12842.pdf

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