Robots Learn by Breaking Down Tasks

Author: Denis Avetisyan

New research introduces a framework for teaching robots reusable skills from demonstrations, paving the way for more adaptable and versatile manipulation abilities.

AtomSkill decomposes complex tasks into temporally aligned, semantically labeled skill segments-identified through expert demonstrations and a vision-language model-and then structures this skill space to train both a skill-guided policy and a diffusion-based sampler, ultimately enabling robust inference through action chunking with keyposes for smooth, chained skill execution.

AtomSkill learns semantically-grounded, atomic skills from robot demonstrations using diffusion models and vision-language models to improve multi-task manipulation performance.

Scaling robot learning to complex, multi-task manipulation remains challenging due to issues with demonstration quality and behavioral variability. This work introduces ‘Learning Semantic Atomic Skills for Multi-Task Robotic Manipulation’, a novel framework, AtomSkill, that learns reusable and semantically-grounded skills from robot demonstrations. By partitioning actions into variable-length, composable “atomic skills” and leveraging vision-language models, AtomSkill enables robust skill chaining and improved generalization across diverse tasks. Could this approach unlock more adaptable and intelligent robotic systems capable of seamlessly performing a wider range of real-world manipulations?

The Inevitable Limits of Robotic Skill: A Familiar Story

Conventional robotic systems frequently encounter difficulties when transferring learned skills to new situations, a limitation stemming from their reliance on precise, pre-programmed instructions and a lack of adaptability. A robot expertly stacking blocks in a controlled laboratory setting may fail completely when presented with slightly different blocks, altered lighting, or even a minor shift in the table’s position. This inflexibility arises because these systems typically operate on the assumption that the world remains consistent with their initial training – any deviation necessitates laborious reprogramming. Unlike humans, who effortlessly generalize from experience, robots struggle to recognize the underlying principles governing a task, hindering their ability to apply previously learned skills to novel, yet related, scenarios. Consequently, developing robots capable of robust generalization remains a central challenge in the field of robotics, demanding innovative approaches to perception, learning, and control.

Imitation learning, a promising avenue for teaching robots new skills, frequently demands an overwhelming amount of demonstration data to achieve even moderate success. This data hunger stems from the need to cover the vast space of possible scenarios and variations a robot might encounter. More critically, these methods often falter when confronted with tasks requiring a sequence of actions – a robot might learn to grasp an object, but struggle to then place it in a specific location as part of a larger, multi-stage procedure. The inherent difficulty lies in disentangling the necessary steps and generalizing the learned behaviors to novel situations within the complex task; simply mirroring demonstrations isn’t enough when the environment or task parameters shift, or when the robot encounters an unforeseen circumstance requiring improvisation. Consequently, current imitation learning approaches struggle to create truly adaptable robotic systems capable of handling the nuances of real-world complexity.

The difficulty robots face in adapting to new situations stems not simply from a lack of individual skill proficiency, but from a fundamental challenge in skill composition. Current approaches often treat each robotic action as isolated, requiring substantial retraining when even minor environmental changes or task variations occur. A truly adaptable robot necessitates a framework where learned skills can be treated as modular building blocks – akin to composing with musical phrases or assembling LEGO bricks. This would allow for the creation of complex behaviors from simpler, reusable components, dramatically reducing the need for exhaustive re-programming with each new scenario. The development of such a framework demands not only the ability to learn individual skills effectively, but also to represent those skills in a way that facilitates their flexible combination and adaptation – a significant hurdle in achieving genuinely generalized robotic intelligence.

AtomSkill improves upon existing skill-based imitation learning by segmenting demonstrations into distinct, temporally aligned skills, unlike prior methods that rely on fixed-length windows and produce ambiguous boundaries.

AtomSkill: Another Brick in the Wall?

AtomSkill constructs its skillset through multi-task imitation learning, a method where a robotic system learns a diverse set of fundamental actions concurrently by observing demonstrations. This approach differs from learning individual skills in isolation; instead, the robot is exposed to a broad range of tasks, enabling the development of an ‘Atomic Skill Library’ comprising primitive movements and manipulations. Data from multiple demonstrations are aggregated and utilized to train a single policy capable of executing a variety of basic robotic actions, such as grasping, pushing, and placing, which can then be combined to accomplish more complex tasks. The resulting library aims to provide reusable building blocks for robotic task execution, increasing adaptability and reducing the need for task-specific training.

AtomSkill integrates a Vision-Language Model (VLM) to bridge the gap between visual perception and natural language instructions. This VLM processes both visual input from the robot’s sensors and textual task descriptions, enabling the system to understand the semantic meaning of commands. Specifically, the VLM is responsible for encoding the task instructions into a vector representation that can be used to condition the robot’s skill execution. This semantic grounding allows AtomSkill to interpret high-level goals, such as “pick up the red block,” and translate them into a sequence of atomic actions, even with variations in phrasing or environmental conditions. The VLM’s output serves as a crucial input to the skill selection and execution process, enabling the robot to perform tasks based on what is asked, not just how it is phrased.

AtomSkill utilizes contrastive learning to improve the transferability of learned robotic skills by minimizing the distance between skill embeddings and their corresponding semantic descriptions. This is achieved by constructing positive and negative pairs; positive pairs consist of a robotic skill and its associated language instruction, while negative pairs combine a skill with an unrelated instruction. The framework then trains an embedding space where positive pairs are drawn closer together and negative pairs are pushed further apart. This alignment process ensures that the learned skills are not only effective in performing actions but are also semantically consistent with human instructions, leading to improved generalization to novel tasks and environments. The resulting skill embeddings effectively represent the meaning of the skill, enabling the system to retrieve and apply appropriate skills based on semantic similarity.

A t-SNE visualization reveals that latent features learned by both QueST and AtomSkill effectively capture the structure of a real-world bimanual manipulation task.

Orchestrating the Chaos: Dynamic Skill Composition

AtomSkill employs a ‘Keypose Imagination’ technique to enhance multi-skill action planning by predicting the resulting terminal states of actions. This involves generating future keyposes – representative configurations of the agent – and evaluating their potential outcomes before execution. By simulating these states, the system can anticipate the consequences of skill sequences and optimize plans for achieving desired goals. The framework utilizes these predicted terminal states to refine action selection, enabling more efficient and robust task completion across complex, multi-step procedures. This predictive capability is crucial for navigating uncertain environments and adapting to unforeseen circumstances during skill execution.

The AtomSkill framework employs a Skill Diffusion Sampler to create high-level skill embeddings, representing skills as points in a latent space. This approach enables flexible task adaptation by allowing the system to generate novel skill combinations and modify existing skills based on task requirements. The diffusion process involves iteratively adding noise to a skill embedding and then learning to reverse this process, effectively learning a distribution over skills. Generating embeddings rather than relying on discrete skill selection improves planning efficiency by facilitating smoother transitions between actions and enabling the system to explore a continuous space of possible behaviors. These embeddings serve as input for downstream planning algorithms, allowing for the creation of complex and adaptable action sequences.

Action Chunking within the AtomSkill framework facilitates the creation of extended, coherent action sequences by linking pre-defined skills at specific keyframes. This process involves identifying critical points in a task where transitioning between skills is optimal, and then chaining those skills together to form a larger, more complex action. By operating at the keyframe level, the system minimizes unnecessary computation and maintains temporal consistency throughout the sequence. This contrasts with planning at every timestep, and enables the execution of tasks requiring multiple coordinated skills without requiring exhaustive re-planning between each individual action.

AtomSkill leverages diffusion-based and vector quantization techniques to improve skill learning. Specifically, the framework investigates ‘Diffusion Policy’, a method utilizing diffusion models for policy generation, and ‘VQ-BeT’ (Vector Quantized Behavior Transformer). A core component of this approach is the ‘Residual VQ-VAE’, which learns a discrete latent space representation of observed behaviors. This allows for efficient skill representation and facilitates the generation of diverse skill embeddings, ultimately enhancing both the speed and variety of learned skills by operating on a compressed, discrete state space rather than continuous observations.

This illustration demonstrates the robot's ability to perform complex bimanual tasks requiring both precise spatial localization and extended action sequences. — This illustration demonstrates the robot’s ability to perform complex bimanual tasks requiring both precise spatial localization and extended action sequences.

The Usual Metrics: Performance and Comparison

Evaluations conducted on the challenging ‘RLBench’ platform reveal AtomSkill consistently surpasses existing methods in robotic task completion. This improvement is quantified through two key metrics: ‘Success Rate’, indicating the proportion of tasks completed successfully, and ‘Average Task Progress’, which measures how far a robot advances toward a goal even if full completion isn’t achieved. AtomSkill’s composable skill library enables more robust and adaptable behavior, resulting in demonstrably higher performance across a diverse range of household manipulation tasks. These gains highlight the efficacy of the approach in creating robots capable of reliably executing complex, real-world procedures, and suggest a step forward in imitation learning for robotics.

Evaluations consistently reveal AtomSkill’s robust capabilities when contrasted with the large-scale Vision-Language-Action model, RDT. This performance highlights the effectiveness of the composable skill library at the heart of AtomSkill’s design, enabling it to achieve greater efficiency and adaptability than a monolithic approach. By breaking down complex tasks into reusable atomic skills, the system demonstrates a capacity for generalization and problem-solving that surpasses that of RDT, particularly in dynamic and unpredictable environments. These results underscore the benefits of a modular architecture for robotic manipulation and provide a strong argument for its continued development as a key component of advanced automation systems.

AtomSkill’s innovative approach to robotic task learning distinguishes it from existing imitation learning techniques. Methods like ACT and QueST, while capable, rely on monolithic policies that struggle with the complexity and variability of real-world scenarios. In contrast, AtomSkill leverages a composable skill library – a collection of modular, reusable action primitives. This allows the system to break down complex tasks into simpler, more manageable components, and then recombine these skills in novel ways to achieve desired outcomes. Empirical results demonstrate that this modularity provides a significant advantage, consistently outperforming ACT and QueST in benchmarks like RLBench, suggesting that a skill-based approach is crucial for building robust and adaptable robotic systems.

AtomSkill exhibits a noteworthy capacity for robotic task completion, achieving an average task progress of 0.68 within simulated environments and translating effectively to real-world applications. Rigorous evaluations on the RLBench platform reveal substantial performance gains over existing methodologies; notably, success rates improve by as much as 28 percentage points and Average Task Progress increases by over 13 percentage points when compared to baseline models. These results highlight AtomSkill’s robust skill composability and its ability to generalize learned behaviors, demonstrating an advancement in robotic manipulation and autonomous task execution.

RLBench tasks assess both consistent motion reproduction and spatial accuracy, mirroring the demands of real-world scenarios requiring both precise localization and extended action sequences.

The Long View: Towards Perpetual Learning

The continued development of robotic capabilities hinges on the expansion of what is known as the ‘Atomic Skill Library’ – a repository of fundamental actions that robots can utilize and combine. Future work centers on enabling these robots to learn continuously throughout their operational lifespan, refining existing skills and acquiring new ones without explicit reprogramming. This ‘lifelong learning’ approach moves beyond pre-programmed behaviors, allowing robots to adapt to changing environments and unforeseen circumstances. By leveraging techniques like reinforcement learning and imitation learning, robots can autonomously improve their performance on established tasks and generalize these learnings to novel situations. A robust and ever-growing Atomic Skill Library, fueled by continuous refinement, promises to unlock a new era of robotic versatility and autonomy, moving beyond specialized applications towards truly adaptable and intelligent machines.

The true potential of foundational robotic skills, such as those developed within the AtomSkill framework, lies in their synergy with higher-level cognitive abilities. Current research focuses on integrating these skills with advanced reasoning and planning algorithms, moving beyond simple execution to enable robots to autonomously decompose complex tasks into manageable sequences of atomic actions. This integration allows a robot to not merely perform a skill, but to choose the optimal skill, at the optimal time, based on a nuanced understanding of its environment and goals. By combining the reliability of learned low-level skills with the flexibility of symbolic planning – for example, utilizing techniques like hierarchical task networks or probabilistic reasoning – robots can address tasks demanding adaptability, foresight, and problem-solving, ultimately bridging the gap between pre-programmed behavior and genuine intelligence in dynamic, real-world scenarios.

Transitioning AtomSkill from simulation to physical embodiment necessitates deployment on robotic platforms like the ALOHA-style system, a crucial step towards realizing practical applications. This involves addressing the inherent challenges of real-world perception, actuation, and unforeseen environmental factors – discrepancies often absent in controlled virtual environments. Successfully integrating the skill library with a physical robot allows for iterative refinement through real-world interaction, enabling the system to learn robust and adaptable behaviors. This deployment isn’t merely about execution; it’s about gathering empirical data to validate the learned skills, identify failure cases, and ultimately improve the generalization capabilities of the AtomSkill framework, opening doors to applications in areas like automated assistance, manufacturing, and disaster response.

The ultimate ambition driving this research lies in the creation of truly autonomous robotic systems – machines capable of continuous learning and adaptation throughout their operational lifespan. These robots wouldn’t simply execute pre-programmed instructions, but rather independently acquire new skills, refine existing ones, and generalize knowledge to tackle unforeseen challenges with minimal reliance on human guidance. This vision extends beyond mere task completion; it envisions robots proactively identifying opportunities for improvement, optimizing performance in dynamic environments, and ultimately functioning as versatile, self-sufficient agents capable of handling a remarkably diverse spectrum of tasks. Such a capability promises to revolutionize industries ranging from manufacturing and logistics to healthcare and disaster response, ushering in an era where robots augment human capabilities and operate effectively in complex, real-world scenarios.

Our real-world experiments were conducted using the depicted robotic setup.

The pursuit of elegant robotic manipulation frameworks feels, predictably, like building castles on quicksand. AtomSkill, with its focus on semantic atomic skills, attempts to decompose complex tasks into reusable components – a reasonable approach, though history suggests fragmentation rarely solves fundamental brittleness. The system learns from demonstrations, hoping to sidestep the combinatorial explosion of possible actions. One anticipates the bug tracker will eventually fill with edge cases, the inevitable friction between theory and the messiness of production environments. As Edsger W. Dijkstra observed, “It’s not enough to have good intentions, you also need to do them.” The framework might learn how to grasp, but grappling with unpredictable real-world scenarios will always be the true test. It doesn’t deploy – it lets go.

The Road Ahead

The notion of ‘atomic’ skills, neatly packaged and semantically labeled, feels…familiar. Previous attempts at modularity suffered predictable fates: edge cases proliferated, combinations proved brittle, and the cost of maintaining the skill library invariably exceeded the benefits. This work, while elegantly leveraging recent advances in vision-language models and diffusion, will undoubtedly encounter the same realities. The demonstrations used to seed these skills are, after all, inherently biased, and the ‘semantic grounding’ is only as robust as the data it’s built upon. Expect production deployments to reveal unforeseen dependencies and emergent behaviors.

A crucial, and often overlooked, challenge lies not in learning the skills, but in composing them. Current approaches tend to treat skill sequencing as a separate problem, addressed after the skills themselves are established. A truly general system will require a unified framework where skill learning and planning are intrinsically linked. Furthermore, the focus on imitation learning, while pragmatic, begs the question of how these systems will adapt to novel situations not present in the training data. The ability to generalize beyond the demonstrations remains the ultimate test.

The pursuit of reusable skills is laudable, yet one suspects that the ‘infinite scalability’ promised by such frameworks will once again prove elusive. The true complexity of robotic manipulation isn’t in the individual actions, but in the infinite variety of contexts in which those actions must be performed. If all tests pass, it’s because they test nothing of practical relevance. The cycle continues.

Original article: https://arxiv.org/pdf/2512.18368.pdf

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