Author: Denis Avetisyan
A new framework automatically generates diverse robotic tasks and datasets, enabling policies trained in simulation to perform reliably in the real world.
AnyTask leverages foundation models and parallel simulation to create large-scale datasets for advancing sim-to-real transfer in reinforcement learning.
Despite advances in robotic learning, creating sufficiently large and diverse datasets for real-world deployment remains a significant bottleneck. This paper introduces AnyTask: an Automated Task and Data Generation Framework for Advancing Sim-to-Real Policy Learning, a system that leverages foundation models and massively parallel simulation to automatically generate robotic manipulation tasks and corresponding data. By employing novel agents-ViPR, ViPR-Eureka, and ViPR-RL-AnyTask synthesizes expert demonstrations across a range of scenarios, enabling policies to generalize to previously unseen object arrangements. Could this automated approach unlock a new era of adaptable and robust robot behavior in complex, real-world environments?
The Data Bottleneck: A Fundamental Challenge in Robotics
The development of truly adaptable robotic systems is often hampered by a fundamental need for extensive datasets to train effective policies. Unlike many machine learning applications that can leverage readily available digital information, robots learn through interaction with the physical world, demanding hours of real-world operation to acquire sufficient examples. This data collection process isn’t merely time-consuming; it’s also profoundly expensive, requiring not only the robots themselves but also the personnel to supervise their learning, maintain equipment, and address unforeseen complications. For complex tasks – such as grasping novel objects, navigating dynamic environments, or collaborating with humans – the quantity of data needed quickly escalates, presenting a significant obstacle to rapid prototyping and deployment. Consequently, the prohibitive cost and logistical challenges of gathering real-world data frequently limit robotic capabilities and slow the pace of innovation in the field.
While robotic simulation presents a compelling pathway to accelerate learning and reduce the need for costly real-world data collection, a significant hurdle remains: the persistent ‘reality gap’. These virtual environments, though increasingly sophisticated, inevitably contain inaccuracies in their modeling of physical properties like friction, material deformation, and sensor noise. Consequently, policies trained exclusively in simulation often fail to generalize effectively when deployed in the unpredictable dynamics of the real world. This discrepancy stems from the difficulty in precisely capturing the complexities of real-world physics and the presence of unmodeled disturbances, necessitating techniques that allow robots to adapt and refine their behavior upon encountering the nuances of a physical environment-a challenge that continues to drive research in areas like domain randomization and sim-to-real transfer learning.
The advancement of robotics, particularly in tackling intricate tasks, is currently constrained by a critical limitation in data acquisition. Existing methodologies for dataset generation often fail to produce the breadth and realism necessary for training robust robotic policies. Simply put, current systems struggle to efficiently create the diverse range of scenarios – variations in lighting, object textures, unexpected disturbances – that a robot will inevitably encounter in the real world. This deficiency leads to policies that perform well in controlled environments but falter when faced with the unpredictable nature of actual deployment. Consequently, robots trained on insufficient or unrepresentative data exhibit limited generalization capabilities, requiring extensive, costly, and time-consuming retraining for each new environment or task. Overcoming this data bottleneck is therefore paramount to unlocking the full potential of robotic automation and achieving truly adaptable, intelligent machines.
AnyTask: Automating Data Generation with Foundation Models
AnyTask is a system designed to address the data bottleneck in robotic learning through automated dataset generation. It leverages foundation models, specifically Large Language Models (LLMs), in conjunction with a massively parallel simulation environment. This framework enables the creation of large-scale, diverse robotic datasets without requiring manual authoring of tasks or environments. The system’s scalability is achieved by automating the process of task specification and environment creation, allowing for the rapid generation of data for a variety of robotic manipulation scenarios. By decoupling task definition from implementation, AnyTask facilitates the efficient exploration of robotic skill spaces and supports the development of more robust and adaptable robotic systems.
AnyTask employs Large Language Models (LLMs) to automate the creation of robotic datasets by generating both natural language task descriptions and the corresponding simulation code necessary to execute those tasks. Specifically, utilizing the o3-mini LLM, the system achieved a 96% code runability rate, indicating a high degree of functional code generation. This performance was obtained through iterative prompt engineering and refinement, optimizing the LLM’s output for compatibility with the simulation environment. The automated code generation process significantly reduces the manual effort required for dataset creation, enabling rapid prototyping and scaling of robotic learning experiments.
AnyTask leverages the synergy between Large Language Models (LLMs) and a parallelized simulation environment to efficiently generate varied robotic task datasets. Quantitative evaluation, specifically a lower Self-BLEU score compared to existing datasets like RoboGen, RLBench, and GenSim2, confirms this capability. Self-BLEU measures the similarity of generated task descriptions to each other; a lower score indicates greater diversity in the generated tasks. This demonstrates that AnyTask is capable of producing a wider range of robotic experiences than these alternative methods, improving the potential for training more generalized and robust robotic systems.
Boosting Efficiency: Replay and Randomization Techniques
AnyTask utilizes state and action replay mechanisms to significantly improve data generation efficiency. This technique stores previously experienced state-action pairs, allowing the system to reuse these trajectories instead of repeatedly simulating from scratch. By effectively caching and revisiting successful or informative experiences, AnyTask minimizes redundant computation and achieves a 4x speedup in data generation, particularly benefiting complex tasks where simulation is computationally expensive. This replay buffer enables the agent to learn more effectively from a fixed computational budget by maximizing the utilization of each simulated step.
Domain randomization enhances the generalization capability of learned policies by training agents across a diverse set of simulated environments. This technique involves systematically varying simulation parameters – including object textures, lighting conditions, physical properties, and geometric variations – during the training process. By exposing the agent to this breadth of simulated conditions, the policy learns to be less sensitive to specific details of any single environment, thereby improving its performance and robustness when deployed in real-world scenarios or unseen simulations. The range of randomization is strategically determined to cover expected variations while avoiding catastrophic disruptions to the learning process.
The implementation of Task and Motion Planning (TAMP)-based agents, specifically ViPR and ViPR-RL, in conjunction with reinforcement learning (RL) techniques, demonstrably improves the quality of generated training data and enhances resultant policy performance. Empirical evaluation indicates an average 13.6% gain in task success rate across 86.4% of evaluated tasks. This improvement stems from the TAMP agents’ ability to generate more feasible and informative trajectories, which are then leveraged by the RL algorithms for more efficient policy optimization. The combined approach addresses challenges inherent in complex manipulation tasks by providing both high-level planning and low-level control refinement.
Dense annotation of simulation states within the AnyTask framework involves the systematic recording of a comprehensive set of contextual data points at each simulation step. This includes, but is not limited to, object positions, orientations, velocities, and relevant physical properties. The resulting rich dataset provides detailed information about the environment and agent interactions, enabling more effective learning by reinforcement learning algorithms and improving generalization capabilities to unseen scenarios. This detailed annotation allows for more accurate reward function shaping, better state representation learning, and the identification of critical features influencing task success, ultimately accelerating the training process and improving policy robustness.
Expanding Robotic Capabilities: Towards Generalization
Robotic proficiency often plateaus due to the limitations of training data; acquiring sufficient examples for diverse real-world scenarios is costly and time-consuming. However, the AnyTask system addresses this challenge with an automated data generation pipeline, enabling robots to learn complex tasks with greater efficiency and adaptability. This pipeline doesn’t rely on manual data collection; instead, it systematically creates a vast and varied dataset of simulated interactions. By leveraging a comprehensive Object Database, the system constructs diverse scenes and scenarios, effectively broadening the robot’s experience and fostering generalization to previously unseen environments. This approach allows robots to move beyond memorizing specific training examples and instead develop a more robust understanding of the underlying principles governing task completion, ultimately enhancing their ability to perform reliably in dynamic and unpredictable settings.
The foundation of AnyTask’s adaptability lies in its extensive Object Database, a curated collection of 3D models representing a wide array of everyday items and environments. This database isn’t merely a repository of shapes; it’s a dynamic tool for generating countless unique scenes and interactions. By randomly combining objects, altering their textures and positions, and simulating diverse lighting conditions, the system creates a virtually limitless training ground for robots. This approach dramatically broadens the scope of robotic learning, moving beyond narrowly defined tasks to encompass the variability inherent in real-world scenarios. Consequently, robots trained with this system demonstrate improved generalization capabilities, enabling them to navigate and manipulate objects in previously unseen environments with greater proficiency.
AnyTask demonstrates a significant advancement in robotic adaptability through the generation of large-scale datasets, facilitating zero-shot transfer learning. This approach allows robots to execute tasks in real-world scenarios without requiring task-specific training in those environments; instead, they leverage knowledge gained from simulated data. Initial evaluations reveal an average success rate of 44% when applying learned skills to unseen, real-world tasks, a considerable achievement in the field of sim-to-real transfer. The ability to perform effectively without further training suggests a path towards more versatile and generally intelligent robotic systems, capable of quickly adapting to new challenges and environments.
Recent advancements in robotic control demonstrate the efficacy of 3D Diffusion Policies for training visuomotor skills in challenging environments. These policies leverage the principles of diffusion modeling, traditionally used in image generation, to create a robust system for robotic control. Instead of directly predicting actions, the policy learns to ‘denoise’ random actions, gradually refining them into effective movements based on visual input. This approach proves particularly effective in complex scenarios where traditional methods struggle with the high dimensionality of visual data and the need for precise control. By learning a distribution over possible actions conditioned on visual observations, the robot can generalize more effectively to novel situations and exhibit greater adaptability, ultimately improving performance in real-world applications.
The work detailed in this paper echoes a sentiment expressed by G. H. Hardy: “A mathematician, like a painter or a poet, is a maker of patterns.” AnyTask, as a framework, isn’t simply about generating data; it’s about crafting a structured ecosystem for robotic learning. The system meticulously builds diverse scenarios, effectively ‘making patterns’ within the simulation environment. This patterned data then becomes the foundation for policies intended to navigate the complexities of the real world. Just as a flawed pattern compromises a mathematical proof, inconsistencies in the generated data can hinder successful sim-to-real transfer, underscoring the importance of AnyTask’s systematic approach to data creation and its emphasis on a holistic, interconnected system.
Where Do We Go From Here?
The pursuit of generalized robotic skills, as exemplified by AnyTask, invariably circles back to the fundamental question of representation. Generating diverse data is useful, certainly, but diversity without underlying structure feels… wasteful. A system capable of producing millions of simulated grasps will still falter if it lacks an understanding of why certain grasps succeed and others fail. The elegance of a solution isn’t measured by its complexity, but by the simplicity with which it captures essential principles. If a design feels clever, it’s probably fragile.
Future work must address the limitations inherent in relying solely on large-scale data generation. The true test won’t be how much data a system can process, but how efficiently it can learn from limited, carefully curated examples. The current paradigm implicitly assumes that sufficient quantity can compensate for a lack of quality – a proposition that history rarely supports. A focus on compositional representations, enabling the reuse of learned skills in novel contexts, will prove crucial.
Ultimately, the goal isn’t simply to bridge the sim-to-real gap, but to fundamentally rethink the nature of robotic intelligence. A system that merely mimics human behavior will always be constrained by human limitations. The potential lies in discovering – or constructing – principles of control that are both robust and adaptable, a system where structure dictates behavior, and simplicity reigns supreme.
Original article: https://arxiv.org/pdf/2512.17853.pdf
Contact the author: https://www.linkedin.com/in/avetisyan/
See also:
- Mobile Legends: Bang Bang (MLBB) Sora Guide: Best Build, Emblem and Gameplay Tips
- Clash Royale Best Boss Bandit Champion decks
- Best Hero Card Decks in Clash Royale
- All Brawl Stars Brawliday Rewards For 2025
- Best Arena 9 Decks in Clast Royale
- Brawl Stars December 2025 Brawl Talk: Two New Brawlers, Buffie, Vault, New Skins, Game Modes, and more
- Clash Royale December 2025: Events, Challenges, Tournaments, and Rewards
- Call of Duty Mobile: DMZ Recon Guide: Overview, How to Play, Progression, and more
- Clash Royale Witch Evolution best decks guide
- Clash of Clans Meltdown Mayhem December 2025 Event: Overview, Rewards, and more
2025-12-23 04:08