Teaching Robots to Understand Actions

Author: Denis Avetisyan

A new framework distills core action concepts from visual data and language, paving the way for more adaptable and transferable robotic systems.

Latent action distillation optimizes visual-language models by aligning learned action representations from both robotic and human demonstrations-a process simultaneously preserving sub-task planning while enabling continuous action prediction, acknowledging that all systems, even those mimicking life, are subject to the graceful decay of performance over time as represented by $L_{alignment} + L_{preservation}$.

LatBot learns disentangled latent actions through knowledge distillation and physical priors, enhancing generalization in vision-language-action models for robotics.

Despite advances in robotic manipulation, achieving robust generalization across diverse tasks and robot embodiments remains a key challenge. This is addressed in ‘LatBot: Distilling Universal Latent Actions for Vision-Language-Action Models’, which introduces a novel framework for learning disentangled latent actions from visual observations and language instructions. By explicitly modeling action sequences and filtering for robot-specific movements, LatBot distills transferable representations that significantly improve performance in both simulated and real-world robotic settings-even with limited data. Could this approach unlock truly adaptable robots capable of seamlessly executing complex tasks in previously unseen environments?

The Fragility of Action: Bridging the Gap Between Perception and Embodiment

Current vision-language models, while proficient at associating images with descriptive text, often falter when tasked with directing physical robotic actions. This stems from a fundamental disconnect: these models lack an inherent understanding of the physical world and the constraints that govern it. A robot instructed to “stack the blocks” requires not just visual recognition of the blocks, but also an awareness of gravity, friction, and the need for stable configurations – concepts not readily available from text-image datasets. Moreover, the models struggle with action semantics – the nuanced meaning behind verbs like “grasp,” “push,” or “place,” which require an understanding of force, trajectory, and potential outcomes. Consequently, instructions are often misinterpreted, leading to clumsy, inefficient, or even failed attempts at manipulation, highlighting the need for systems that move beyond mere visual recognition to embrace a deeper comprehension of physics and embodied action.

Current robotic control systems frequently conceptualize actions as a series of distinct, pre-defined choices – grasp, lift, place – rather than a fluid, continuous process. This discrete approach limits a robot’s ability to adapt to real-world complexities and unforeseen circumstances, as it struggles with the subtle variations inherent in physical interactions. Effective manipulation demands a far more granular understanding, where actions aren’t simply started and stopped, but rather modulated along a spectrum of force, velocity, and trajectory. Consequently, robots operating on such systems often exhibit rigidity and a lack of finesse, hindering their performance in dynamic environments and preventing them from seamlessly executing tasks requiring delicate adjustments or responding to unexpected changes in object properties or external forces.

Successfully translating human instruction into robotic action demands a sophisticated understanding of both language and physics, yet current systems frequently falter at the interface between the two. The difficulty stems from the inherent disparity in their representation: language operates on abstract concepts and symbolic meaning, while robotic control requires precise, continuous signals for actuators and joints. Bridging this gap necessitates a system capable of inferring the intended physical actions from linguistic commands – not just identifying keywords, but also reasoning about the dynamics of the environment and the constraints on the robot’s movements. This involves disentangling the high-level goal expressed in language from the low-level details of how that goal should be achieved, effectively creating a ‘latent action space’ that allows for flexible and robust manipulation even in the face of uncertainty or unforeseen circumstances.

Our latent action modeling approach learns a disentangled representation to predict both visual and physical outcomes, enabling more accurate and transferable robot control compared to existing methods that conflate robot actions with environmental changes.

Compressing Movement: The Essence of Latent Action

Latent Action Learning utilizes dimensionality reduction techniques to represent complex robot motion semantics within a lower-dimensional latent space. This compression is achieved through methods like autoencoders or variational autoencoders, which learn to encode high-dimensional sensory inputs and motor commands into a compact latent representation. By mapping a potentially infinite range of actions onto a smaller set of latent variables, the system reduces computational demands and storage requirements. Consequently, robot control becomes more efficient, and the learned representations facilitate generalization to novel situations not explicitly encountered during training, as the latent space captures underlying principles of movement rather than specific instances.

Traditional robot control often relies on memorizing specific trajectories for each task, resulting in limited generalization and requiring extensive data collection for new scenarios. Latent action learning addresses this limitation by learning a compressed representation of movement primitives. Instead of storing complete trajectories, the model identifies the underlying principles governing motion – such as velocity profiles, force application, or kinematic relationships – and encodes these as latent variables. This allows the robot to generate novel movements by combining and adapting these learned principles, rather than recalling pre-defined sequences. Consequently, the model’s dimensionality is significantly reduced, improving computational efficiency and enabling generalization to previously unseen situations without requiring memorization of every possible trajectory.

The capacity for minimal retraining represents a significant advantage of latent action learning for robotic systems. Traditional robot control often requires substantial data collection and model adjustments when deployed in novel environments or faced with unexpected situations. Latent action learning, however, enables robots to generalize learned behaviors to new contexts with significantly reduced data requirements. This is achieved by representing actions in a compressed, semantic space; small adjustments to this latent space can then account for variations in the environment or task, rather than necessitating the relearning of entire motor sequences. Consequently, robots utilizing this approach demonstrate improved robustness to perturbations and greater flexibility in adapting to previously unseen conditions, lowering the cost and time associated with deployment and maintenance.

Latent-action distillation improves the visual language model's ability to focus attention on task-relevant objects, as demonstrated by consistently localized attention maps within target regions across real-robot tasks. — Latent-action distillation improves the visual language model’s ability to focus attention on task-relevant objects, as demonstrated by consistently localized attention maps within target regions across real-robot tasks.

Imparting Expertise: Distilling Knowledge into the System

Knowledge distillation facilitates the transfer of learned representations from a pre-trained latent action model – acting as the ‘teacher’ – to a vision-language model, designated as the ‘student’. The teacher model, already possessing understanding of physical dynamics and action semantics, imparts this knowledge to the student without requiring direct supervision on action-related tasks. This transfer is achieved by training the student to mimic the teacher’s outputs or internal representations, effectively embedding physical priors and action understanding into the vision-language model’s framework. The process allows the student to leverage existing knowledge, improving performance on tasks requiring an understanding of how actions relate to the physical world and visual inputs.

The Latent Action Alignment Loss functions by minimizing the discrepancy between the latent action representations produced by the teacher and student models. Specifically, this loss calculates the cosine similarity between the teacher’s and student’s latent action embeddings for a given input and maximizes this similarity. This encourages the student model to not only predict the correct action label but also to develop an internal representation of the action that is consistent with the pre-trained teacher model, effectively transferring the teacher’s understanding of action semantics. The alignment is performed in the latent space, allowing for a more robust transfer of knowledge than direct output matching, and is calculated using a mean squared error loss on the cosine similarity scores.

The Reasoning Preservation Loss functions by minimizing the discrepancy between the student vision-language model’s original instruction-following capability and its performance after incorporating the latent action representation. Specifically, this loss component calculates the cross-entropy between the student’s predicted answer distributions before and after distillation, using the original input prompts. By directly penalizing any degradation in answer accuracy resulting from the knowledge transfer, the Reasoning Preservation Loss ensures that the student model not only learns the action semantics from the teacher but also retains its pre-existing ability to accurately respond to given instructions, effectively balancing knowledge acquisition with the preservation of existing capabilities.

LatBot: A Unified Architecture for Embodied Intelligence

LatBot implements a universal latent action learning framework by consolidating vision, language, and action generation into a single decoding process. This is achieved through a unified decoder architecture which accepts a latent action vector as input and utilizes it to concurrently predict future video frames and generate corresponding actions. Unlike traditional approaches that treat these modalities separately, LatBot’s decoder is trained end-to-end, allowing for shared representations and dependencies to be learned across vision, language, and action spaces. This unified approach facilitates the generation of visually plausible and contextually appropriate actions based on both visual input and linguistic commands, streamlining the learning process and improving performance on complex, multi-modal tasks.

The LatBot framework utilizes a unified decoder, based on the SANA (State Abstraction and Novelty Assessment) architecture, to generate both visual frames and subsequent actions. This decoder accepts a latent action vector as input, which conditions the generation process. Specifically, the latent action guides the reconstruction of future video frames and simultaneously determines the inter-frame actions to be performed. This joint generation allows for visually consistent action sequences, as the visual reconstruction is directly informed by the planned action and vice versa. The decoder predicts future states – both visual and action-based – conditioned on the current state and the latent action, enabling a cohesive and integrated approach to vision, language, and action.

LatBot implements a feedback loop by enabling bidirectional interactions between predicted scene dynamics and action generation. Specifically, the system’s visual reconstruction module provides feedback on the plausibility of predicted scene changes resulting from an action. This feedback, assessed through reconstruction error, is then used to refine the action planning process, adjusting future action selections to produce more visually coherent and realistic outcomes. Conversely, the generated actions influence the subsequent frames reconstructed by the visual module, effectively creating a closed loop where improvements in action planning lead to more accurate visual reconstruction, and vice-versa. This iterative refinement process enhances both the quality of the generated video sequences and the long-term consistency of the planned actions.

The robotic system utilizes multi-view observations to perform complex manipulation tasks-including pick-and-place, insertion, and interactions with diverse real-world objects like brushes, pans, and ovens-requiring precise translational and rotational control.

Beyond Static Performance: Towards Truly Adaptive Systems

Traditional vision-language-action models often struggle with the variability inherent in real-world robotic tasks, limiting their ability to generalize to new situations. This framework addresses this challenge by introducing latent actions – a compressed, efficient representation of possible robot movements – and employing knowledge distillation to transfer learning from a larger, more complex model to a smaller, deployable one. By learning these underlying action patterns, the system moves beyond simply recognizing objects and instructions; it develops a deeper understanding of how to execute tasks. This approach enables the robot to adapt to novel scenarios and effectively perform actions even with imperfect or ambiguous input, representing a significant step towards truly generalizable robotic intelligence and paving the way for more robust and versatile robotic systems.

Recent advancements in robotic intelligence have yielded a framework demonstrating state-of-the-art performance, notably exceeding existing methodologies by as much as 32.3% when assessed on the demanding WidowX robot benchmark. This substantial improvement highlights the efficacy of the approach in executing complex robotic tasks with increased precision and reliability. Rigorous testing against established benchmarks confirms not only a significant quantitative leap but also a qualitative enhancement in robotic capabilities, enabling more robust and adaptable performance in dynamic environments. The results indicate a promising trajectory towards more versatile and intelligent robotic systems capable of tackling increasingly sophisticated challenges.

LatBot demonstrates a significant advancement in robotic task completion, achieving an 87.5% success rate on the challenging WidowX benchmark and a 78.0% success rate on the Google Robot platform. These results represent a substantial improvement over existing open-source models, exceeding their performance by 25.3%. This heightened level of accuracy signifies the framework’s ability to reliably interpret instructions and execute complex maneuvers, indicating a promising step towards more capable and adaptable robotic systems.

Evaluations on the LIBERO benchmark demonstrate a significant advancement in robotic task completion, with LatBot achieving a 98.0% success rate. This performance represents a 3.0% improvement over the established baseline specifically on the LIBERO-Long task, which challenges robots with extended sequences of actions and increased environmental complexity. This result highlights LatBot’s enhanced ability to maintain accuracy and reliability throughout prolonged interactions, suggesting a robust capacity for handling real-world scenarios demanding sustained performance and adaptation to changing conditions. The improvement on the LIBERO-Long task underscores a notable step towards creating robotic systems capable of executing intricate, multi-step procedures with greater consistency and dependability.

Robotic systems, traditionally constrained by rigidly programmed actions, are now poised to navigate the complexities of real-world environments thanks to advancements in capturing subtle action meanings. This capability allows robots to interpret instructions not as strict commands, but as goals achievable through a range of adaptable movements – effectively understanding how to accomplish a task, not just what needs doing. The ability to discern nuanced action semantics is particularly crucial in unstructured settings – environments lacking pre-defined layouts or predictable conditions – where robots must dynamically adjust their strategies. Consequently, systems are emerging that demonstrate an improved capacity to perform complex tasks, such as manipulating diverse objects or navigating cluttered spaces, with a level of robustness previously unattainable, suggesting a future where robots can operate effectively beyond the limitations of controlled laboratories.

The progression of robotic intelligence isn’t simply about tackling increasingly difficult tasks, but fostering a capacity for continuous improvement and adaptation; therefore, ongoing research prioritizes extending the current framework’s reach to genuinely complex, real-world scenarios. This involves not only scaling computational resources to manage greater environmental variability and task intricacy, but also integrating mechanisms for lifelong learning. The aim is to move beyond pre-programmed responses and enable robots to accumulate knowledge through experience, refining their action strategies and generalizing learned skills to entirely novel situations – effectively allowing them to learn and improve throughout their operational lifespan. Such advancements promise a future where robots aren’t just tools for specific jobs, but adaptable, intelligent agents capable of thriving in dynamic and unpredictable environments.

The pursuit of disentangled latent actions, as demonstrated by LatBot, echoes a fundamental principle of resilient systems. Just as a well-designed architecture anticipates and accommodates decay, LatBot strives to distill core action components independent of specific observations. This approach, extracting universal actions from multi-frame data, isn’t merely about improving robotic task completion; it’s about constructing a framework that ages gracefully, retaining functionality even as environmental factors shift. As Paul Erdős once observed, “A mathematician knows how to solve every equation, but some take longer than others.” Similarly, LatBot doesn’t eliminate the complexity of robotic control, but distills it, creating a more efficient and transferable solution-a system designed to endure the test of time and varying conditions.

What Lies Ahead?

The pursuit of disentangled latent actions, as demonstrated by LatBot, is not a conquest of complexity, but a temporary stay of execution. All systems accrue entropy; the elegance of distilling physical priors into actionable representations will inevitably face the erosion of novel, unforeseen circumstances. The current framework excels at transferring knowledge, yet transfer itself is a fragile harmony, a fleeting phase before the inevitable dissonance of real-world application. The true measure of progress will not be the breadth of tasks addressed, but the grace with which the system degrades when confronted with the truly unexpected.

A critical, and often overlooked, aspect remains the grounding of these latent actions. While the framework effectively maps instruction to behavior, the representation remains inherently abstract. Future iterations must confront the question of embodiment – how does this distilled knowledge interact with the imperfect, noisy reality of physical systems? The ideal is not seamless control, but a resilient adaptation to inevitable failure, a capacity to rebuild from the fragments of broken execution.

Ultimately, the limitations of LatBot, and indeed the entire field, are not technical, but philosophical. The search for ‘universal’ actions implies a static universe, while reality is defined by constant flux. The task is not to eliminate uncertainty, but to build systems that can navigate it – not by predicting the future, but by preparing for all possible futures, recognizing that even the most robust infrastructure is, at its core, a beautifully complex form of planned obsolescence.

Original article: https://arxiv.org/pdf/2511.23034.pdf

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