Robots Learn From Video: A New Path to General-Purpose AI

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Author: Denis Avetisyan

Researchers demonstrate that robots can learn to perform a wide range of tasks by planning from videos, surpassing current methods that rely on language models.

From a single image and task instruction, a system generates a video depicting task completion, subsequently retargeting the predicted human motion to a robot hand-enabling zero-shot visual planning and execution in varied environments through video-based motion planning in pixel space.
From a single image and task instruction, a system generates a video depicting task completion, subsequently retargeting the predicted human motion to a robot hand-enabling zero-shot visual planning and execution in varied environments through video-based motion planning in pixel space.

A video-based robot foundation model enables adaptable task performance in novel environments through generative action planning and diffusion techniques.

Achieving generalizable robotic control remains a key challenge, often hindered by reliance on transferring knowledge from disparate modalities. In the work ‘Large Video Planner Enables Generalizable Robot Control’, we introduce a novel paradigm leveraging large-scale video pretraining to build robot foundation models. Our approach demonstrates that generating temporally coherent video plans enables zero-shot task completion in novel environments, surpassing methods dependent on asymmetric transfer from large language models. Could this video-centric approach unlock a new era of robust, adaptable, and truly intelligent robotic systems?

Beyond Reactive Control: Towards Proactive Robotic Intelligence

Conventional robotics often operates within tightly defined parameters, executing pre-programmed sequences or responding directly to immediate stimuli. This reliance on predetermined actions and reactive control, while effective in structured environments, severely restricts a robot’s ability to function in the face of unexpected situations. These systems struggle when confronted with novelty, lacking the capacity to extrapolate from past experiences or formulate plans to achieve goals in unfamiliar contexts. Consequently, robots designed with these limitations require constant human intervention or operate only within highly constrained settings, hindering their potential for true autonomy and widespread application in the dynamic complexities of the real world.

The limitations of current robotic systems stem from their reliance on reacting to immediate stimuli or executing pre-defined sequences; truly complex challenges necessitate a move towards proactive intelligence. This entails robots not merely responding to the world, but anticipating future states and formulating plans to achieve goals, even in previously unseen circumstances. Such a capability requires moving beyond simple pattern recognition and embracing generalization – the ability to apply learned principles to novel situations. Instead of being confined to replicating demonstrated actions, a proactively intelligent robot can reason about its environment, predict the consequences of its actions, and autonomously devise strategies for success, marking a critical step towards genuine robotic autonomy and adaptability in the real world.

The challenge of imparting sight to robots extends far beyond simply processing images; current computer vision systems frequently falter when confronted with the ambiguity and sheer volume of information present in real-world visual data. Existing methods often struggle to discern relevant objects, understand spatial relationships, and, crucially, build a coherent three-dimensional representation of the environment. This difficulty arises because translating two-dimensional pixel data into a robust $3D$ understanding requires overcoming issues like occlusion, varying lighting conditions, and the infinite possibilities of perspective. Without a reliable grasp of depth and spatial context, robots are unable to effectively plan actions or navigate complex scenes, hindering their ability to perform even seemingly simple tasks that humans accomplish effortlessly.

True robotic autonomy hinges on the development of systems that can synthesize complete action plans solely from observation – a capability far exceeding mere reaction or pre-programmed routines. Such a system wouldn’t simply respond to stimuli, but would internally model a scenario, predict outcomes, and formulate a sequence of actions to achieve a desired goal without explicit instruction. This demands more than just recognizing objects or movements; it requires inferring intent, understanding physical constraints, and generating novel behaviors applicable to unforeseen circumstances. The ability to autonomously construct and execute these comprehensive plans represents a critical leap toward robots capable of operating independently in dynamic, real-world environments, effectively bridging the gap between automation and genuine intelligence.

This pipeline reconstructs human hand movements from video and retargets them for execution by robotic hands or grippers in the real world.
This pipeline reconstructs human hand movements from video and retargets them for execution by robotic hands or grippers in the real world.

A Foundation for Planning: The Video-Based Approach

The Video Foundation Model represents a novel approach to robotic task planning by utilizing a large language model architecture specifically trained for video generation. Unlike traditional methods requiring hand-engineered plans or reinforcement learning, this model directly outputs sequences of actions visualized as video, enabling robots to anticipate and execute complex tasks. The model functions by interpreting high-level goals and translating them into temporally coherent video plans, effectively serving as a predictive engine for robotic behavior. This capability allows for greater flexibility and adaptability in dynamic environments, as the model can generate plans on-the-fly without requiring pre-defined solutions for every scenario.

The Video Foundation Model utilizes diffusion transformers for video sequence processing and generation, achieving efficiency through Latent Diffusion. This technique operates by first encoding video frames into a lower-dimensional latent space, significantly reducing computational demands. The transformer then predicts the diffusion process within this latent space, iteratively refining a noisy latent representation into a coherent video sequence. By performing the diffusion process in the latent space rather than directly on pixel data, the model reduces memory requirements and accelerates both training and inference without substantial loss of visual quality. This approach allows for the generation of temporally consistent and realistic video plans for robotic tasks.

The Video Foundation Model’s performance is directly attributable to its training on the LVP-1M Dataset, a large-scale collection of 1.4 million video clips documenting both human and robotic task execution. This dataset provides the model with extensive exposure to a diverse range of actions, object interactions, and environmental contexts. The composition of LVP-1M includes demonstrations of various manipulation skills, locomotion behaviors, and complex sequential activities, enabling the model to learn robust representations of task procedures. The scale of the dataset is critical, as it facilitates the learning of generalized planning strategies applicable to novel situations beyond those explicitly present in the training data.

History Guidance is a mechanism integrated into the Video Foundation Model to enhance the quality of generated video plans by enforcing temporal consistency. This is achieved by conditioning the diffusion process on previously generated frames, effectively providing the model with a short-term “memory” of the evolving action sequence. Specifically, the model receives as input not only the initial task specification and current frame, but also a representation of the $k$ preceding generated frames. This allows the model to predict future actions that are more likely to follow the established trajectory, resulting in smoother, more realistic, and temporally coherent video plans, mitigating issues such as erratic movements or illogical transitions between actions.

The latent video diffusion framework encodes video into compressed 3D latent representations and then trains a diffusion transformer using flow matching objectives, jointly optimizing image-to-video and video-to-video generation with a modified diffusion forcing strategy that leverages variable context lengths and noise levels to improve robustness and enable diverse training scenarios.
The latent video diffusion framework encodes video into compressed 3D latent representations and then trains a diffusion transformer using flow matching objectives, jointly optimizing image-to-video and video-to-video generation with a modified diffusion forcing strategy that leverages variable context lengths and noise levels to improve robustness and enable diverse training scenarios.

From Visual Prediction to Robotic Action

The Video Reconstruction Module is central to translating visual data into robotic action. It leverages MegaSAM, a generalist visual affordance model, and HaMeR, a hand mesh recovery network, to process generated videos and create corresponding 3D representations. Specifically, the module outputs reconstructed 3D hand poses, providing the spatial configuration of the hand, and depth maps, which detail the distance of points from the camera. These outputs are critical because they provide the necessary 3D information for subsequent robotic control, serving as the foundational data for both dexterous hand and parallel gripper retargeting tasks.

The 3D hand poses and depth maps generated by the Video Reconstruction Module directly inform two distinct robotic control pathways. Dexterous Hand Retargeting utilizes these reconstructions to command the articulated movements of complex robotic hands with multiple degrees of freedom. Simultaneously, Parallel Gripper Retargeting leverages the same 3D data, combined with the GraspNet dataset, to plan and execute precise grasping actions using parallel jaw grippers. This dual-pathway approach allows the system to address a wider range of manipulation tasks, adapting to both fine motor control and robust object acquisition based on the reconstructed scene geometry and the capabilities of the target robotic end-effector.

Dexterous Hand Retargeting enables the control of complex robotic hands with multiple degrees of freedom, allowing for manipulation of objects requiring fine motor skills. This is achieved by translating the reconstructed 3D hand poses from video into commands for the robotic hand. Complementing this, Parallel Gripper Retargeting focuses on controlling robotic grippers with parallel jaws. This functionality leverages the GraspNet dataset, a large-scale collection of 3D object models and corresponding grasp poses, to enable the system to accurately predict and execute precise grasping actions on a variety of objects. The integration of both retargeting methods allows for versatile robotic manipulation, addressing both complex dexterity and reliable grasping tasks.

Evaluation on a third-party test set revealed a 59.3% success rate for the model, indicating robust performance on previously unseen tasks. This benchmark utilized a challenging dataset designed to assess generalization capabilities, and the achieved success rate demonstrates the model’s ability to transfer learned behaviors to novel scenarios without requiring task-specific retraining. The test set comprised a diverse range of manipulation tasks, validating the model’s zero-shot generalization across various object types and configurations.

Evaluations demonstrate the system’s performance exceeds that of established baseline methods across both dexterous hand manipulation and parallel jaw gripper control tasks. Quantitative results indicate a statistically significant improvement in success rates when compared to prior approaches, validating the efficacy of the video reconstruction and retargeting pipeline. Specifically, the system consistently achieves higher rates of successful task completion – encompassing object manipulation and grasping – under a variety of experimental conditions and with diverse robotic hardware. These gains are observed not only in controlled laboratory settings but also on a challenging third-party test set, confirming the model’s ability to generalize to novel scenarios.

Leveraging LVP, the model successfully generates videos enabling zero-shot robot manipulation across both dexterous hands and parallel-jaw grippers.
Leveraging LVP, the model successfully generates videos enabling zero-shot robot manipulation across both dexterous hands and parallel-jaw grippers.

Towards a Future of Adaptive Robotic Systems

The achievement of task-level generalization represents a pivotal advancement in the pursuit of genuinely autonomous robotics. Unlike systems constrained by pre-programmed responses or rigid rules, this model exhibits the capacity to apply learned skills to novel situations and unseen environments. This isn’t simply rote repetition; the robot demonstrates an understanding of what needs to be done, rather than how to do it in a specific instance, allowing for flexible adaptation. Consequently, a robot trained to, for example, assemble one type of object can, with minimal retraining, apply those fundamental skills to assemble a completely different, yet structurally similar, item. This capability drastically reduces the need for extensive, task-specific programming, paving the way for robots that can operate independently and effectively in the unpredictable complexities of the real world.

Current robotic systems often rely on pre-programmed rules or immediate reactions to stimuli, creating limitations in unpredictable environments. However, a novel approach utilizes visual learning to generate comprehensive action plans, effectively bypassing these constraints. Instead of responding to each situation individually, the model learns to interpret visual data and formulate a sequence of actions to achieve a desired outcome. This allows for greater adaptability and proactive behavior, as the robot can anticipate necessary steps and adjust its plan accordingly – a significant advancement over systems limited to pre-defined responses or simple reactivity. The capacity to synthesize complete plans from visual input represents a fundamental shift towards more intelligent and autonomous robotic operation.

The capacity for robots to learn and adapt through visual data and complete action plans extends far beyond controlled laboratory settings, promising transformative applications across numerous sectors. In manufacturing, these systems envision autonomously handling intricate assembly tasks and quality control with unprecedented flexibility. Healthcare stands to benefit from robotic assistance in surgery, patient care, and rehabilitation, improving precision and access. Perhaps most critically, this technology offers the potential to deploy robots in disaster response scenarios – navigating rubble, locating survivors, and providing aid in environments too dangerous for human rescuers. These proactive robotic systems aren’t simply automating existing processes; they are enabling entirely new possibilities for how humans and machines collaborate to solve complex challenges and improve lives.

Ongoing research endeavors are directed towards enhancing the adaptability and performance of this robotic system in real-world scenarios. A primary focus involves bolstering the model’s robustness, specifically its capacity to maintain reliable operation amidst sensor noise, unexpected disturbances, and variations in environmental conditions. Simultaneously, efforts are underway to improve computational efficiency, enabling faster response times and reduced energy consumption-critical for deployment in resource-constrained settings. Perhaps most significantly, future development will prioritize the ability to reason about complex, dynamic environments, moving beyond simple reaction to predictive planning and sophisticated problem-solving, allowing the robot to anticipate challenges and execute actions with greater foresight and autonomy.

Generated video plans accurately predict robot behavior in the real world, as demonstrated by the correspondence between planned frames and actual task execution.
Generated video plans accurately predict robot behavior in the real world, as demonstrated by the correspondence between planned frames and actual task execution.

The research highlights a crucial point about system design-structure dictates behavior. This paper’s Large Video Planner embodies that principle by shifting from language-based planning to a video-based approach. This allows robots to generalize across environments without costly retraining, a feat previously hindered by the limitations of asymmetric transfer learning. As Barbara Liskov stated, “It’s one of the main things I’ve learned: if you design a system with good structure, it’s a lot easier to fix.” The elegance of this system lies in its simplicity; by focusing on visual plans, the robot’s actions become more directly tied to observable outcomes, fostering a more robust and adaptable embodied intelligence.

The Road Ahead

The presented work offers a compelling, if not entirely surprising, demonstration of the power of video as a foundational element for robotic control. It is, however, crucial to recognize that generating a plausible plan – a convincing performance on the ‘stage’ of simulated reality – does not inherently guarantee robust execution in the messy, unpredictable theater of the physical world. One cannot simply replace the ‘brain’ without considering the limitations of the ‘body’ and the friction of the ‘stage’.

Future efforts must address the inevitable discrepancies between generated video and real-world sensorimotor experience. The current reliance on video as both input and desired output creates a potential for cascading errors; a slight misinterpretation early in the planning phase can be amplified through subsequent steps. A truly elegant solution will likely necessitate a more nuanced integration of predictive and reactive control – a system capable of gracefully adapting to unforeseen circumstances, rather than rigidly adhering to a predetermined script.

Ultimately, the pursuit of generalizable robotic intelligence demands a holistic perspective. It is not enough to build increasingly sophisticated ‘planners’; one must also consider the underlying ‘architecture’ – the fundamental constraints and affordances that shape behavior. The question is not simply what a robot does, but how it does it, and the subtle interplay between intention, perception, and action.

Original article: https://arxiv.org/pdf/2512.15840.pdf

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

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2025-12-19 16:11