Robots Learn by Doing: Bridging the Gap with Self-Generated Training Data

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Author: Denis Avetisyan

A new framework combines imitation learning and reinforcement learning to empower robots to autonomously generate training data, leading to more robust and efficient manipulation skills.

ReinforceGen establishes a cyclical process of learning, beginning with the expansion of limited human demonstrations into a synthetic dataset, then training an agent to navigate through both planned waypoints and directly learned control, and finally refining its performance through continuous interaction with the environment-a strategy acknowledging that robust systems are not built, but iteratively sculpted by experience.
ReinforceGen establishes a cyclical process of learning, beginning with the expansion of limited human demonstrations into a synthetic dataset, then training an agent to navigate through both planned waypoints and directly learned control, and finally refining its performance through continuous interaction with the environment-a strategy acknowledging that robust systems are not built, but iteratively sculpted by experience.

ReinforceGen leverages automated data generation and hybrid policies to improve long-horizon robotic manipulation tasks.

Long-horizon robotic manipulation remains a formidable challenge due to the complexity of coordinating multi-step actions. This paper introduces ReinforceGen: Hybrid Skill Policies with Automated Data Generation and Reinforcement Learning, a novel framework that synergistically combines imitation learning, reinforcement learning, and automated data generation to address this limitation. ReinforceGen achieves robust performance by decomposing tasks into learned skills, connecting them with motion planning, and iteratively refining the entire system through online adaptation-demonstrating an 80% success rate on challenging Robosuite benchmarks. Could this approach unlock more adaptable and efficient robotic systems capable of tackling increasingly complex real-world tasks?

The Inevitable Challenge of Skill Acquisition

Conventional robotic control systems frequently encounter difficulties when executing tasks that extend over extended periods and necessitate both accuracy and the capacity to adjust to changing circumstances. These systems, often reliant on pre-programmed sequences or reactive responses to immediate sensor data, struggle with the inherent complexities of long-horizon planning. Successfully navigating these scenarios requires not just precise movements, but also the ability to anticipate future states, recover from unexpected disturbances, and generalize learned behaviors to novel situations. The rigidity of traditional methods limits their effectiveness in dynamic, real-world environments where tasks often demand a nuanced understanding of physical interactions and the capacity to adapt to incomplete information – a significant hurdle in achieving truly versatile robotic capabilities.

Many contemporary robotic systems struggle to perform reliably in real-world scenarios due to limitations in their ability to perceive and react to incomplete information. Current approaches to skill acquisition often rely on fully observable environments or meticulously curated datasets, which are rarely available outside of controlled laboratory settings. This reliance hinders a robot’s capacity to generalize learned skills to novel situations where sensors may be obstructed, lighting conditions change, or unexpected disturbances occur. Consequently, robots frequently fail to adapt when faced with even minor variations from their training data, exhibiting brittle behavior and requiring extensive retraining for each new environment or task. Addressing this challenge requires developing algorithms that can effectively reason under uncertainty and extrapolate learned knowledge to unseen conditions, ultimately enabling robots to operate with the robustness and flexibility characteristic of biological systems.

The acquisition of contact-rich skills, such as grasping, assembly, or surgical manipulation, presents a unique challenge for robotic systems due to the infinite variability inherent in physical interactions. Traditional machine learning approaches often require vast amounts of data to achieve acceptable performance, a significant limitation when dealing with complex, real-world scenarios. Furthermore, these systems frequently struggle to adapt to novel situations or changes in the environment – a dropped object, a slightly misaligned part, or unexpected surface textures can quickly derail performance. Overcoming these hurdles necessitates the development of algorithms that prioritize data efficiency, perhaps through techniques like imitation learning, reinforcement learning with carefully designed reward functions, or the incorporation of prior knowledge about physics and material properties. Ultimately, a truly robust system must not only learn how to perform a contact-rich skill, but also adapt its strategy on the fly, generalizing from limited experience to reliably handle the unpredictable nature of physical contact.

ReinforceGen agents reliably achieve high-precision skills with consistently high success rates.
ReinforceGen agents reliably achieve high-precision skills with consistently high success rates.

Deconstructing Complexity: The ReinforceGen Framework

ReinforceGen addresses complex skill acquisition through hierarchical decomposition, breaking down tasks into a sequence of stages executed by a hybrid skill policy. This policy integrates both learned skills and primitive actions, allowing for flexible behavior and efficient exploration of the task space. The framework doesn’t rely on monolithic skill learning; instead, it learns skills incrementally, building upon simpler, previously mastered stages. This staged approach reduces the complexity of the learning problem and facilitates generalization to novel situations by composing known skills in new arrangements. The hybrid nature of the policy allows the system to leverage the strengths of both pre-defined actions for safety and learned skills for adaptability.

ReinforceGen achieves improved data efficiency and adaptability by integrating imitation learning and reinforcement learning. Initially, the system utilizes demonstration data through imitation learning to establish a foundational policy, reducing the exploration space required for subsequent learning. This pre-trained policy then serves as a starting point for reinforcement learning, enabling the agent to refine its behavior and generalize to unseen scenarios. The combination minimizes the sample complexity typically associated with reinforcement learning from scratch, while the reinforcement learning component overcomes the limitations of imitation learning in adapting to dynamic or partially observable environments. This hybrid approach leverages the benefits of both paradigms – the speed of imitation and the robustness of reinforcement – resulting in a more efficient and versatile skill acquisition process.

ReinforceGen utilizes task and motion planning as a hierarchical guidance system to ensure skill execution remains both safe and efficient. The task planner generates a sequence of waypoints defining the desired goal states, while the motion planner computes collision-free trajectories to reach those waypoints. This two-tiered approach allows the agent to navigate complex environments while adhering to safety constraints; the task planner provides the ‘what’ – the desired objective – and the motion planner determines the ‘how’ – the physically feasible path. By decoupling high-level goal specification from low-level control, the framework minimizes the risk of unsafe actions and optimizes trajectories for speed and resource utilization, ultimately improving the robustness and reliability of skill acquisition.

Each ReinforceGen stage utilizes a pose predictor, skill policy, and termination predictor-all initially learned from generated data and subsequently refined online-to dynamically plan and execute motions towards a defined goal.
Each ReinforceGen stage utilizes a pose predictor, skill policy, and termination predictor-all initially learned from generated data and subsequently refined online-to dynamically plan and execute motions towards a defined goal.

Expanding the Dataset: Generation Through Transformation

ReinforceGen utilizes object-centric data generation by decomposing demonstrated actions into interactions with individual objects. This allows for the creation of new training data through transformations applied to object poses within the original demonstrations. Specifically, the system replays demonstrated trajectories while altering the positions and orientations of key objects, effectively generating variations of the original task. This approach ensures that the generated data maintains realistic physical interactions while increasing the diversity of training scenarios without requiring new demonstrations. The system tracks object states and applies these transformations while respecting kinematic and dynamic constraints, resulting in a dataset that is both varied and physically plausible.

MimicGen, SkillMimicGen, and DexMimicGen build upon object-centric data generation by introducing variations to demonstrated behaviors, specifically targeting the expansion of training datasets for robotic skill learning. MimicGen focuses on replaying demonstrations with randomized object positions and orientations. SkillMimicGen extends this by incorporating skill primitives, allowing for the generation of more complex and varied sequences. DexMimicGen further refines the process, concentrating on dexterous manipulation tasks and generating data that emphasizes fine motor control. These methods systematically alter initial conditions and action parameters within demonstrated trajectories, producing large-scale datasets suitable for training and refining reinforcement learning policies and imitation learning models.

ReinforceGen incorporates online exploration via reinforcement learning to iteratively refine the generated dataset. This process utilizes a reward function to incentivize the agent to generate demonstrations that expand coverage of the state-action space and improve data quality, measured by metrics such as diversity and success rate. The agent interacts with a simulated environment, generating new data points and evaluating their contribution to the overall dataset. This feedback loop allows the system to autonomously identify and address gaps in the existing data, leading to a continuous improvement in the robustness and generalizability of learned policies. The exploration strategy is designed to balance exploitation of successful demonstrations with exploration of novel states and actions, preventing premature convergence on suboptimal solutions.

Our system improves performance by fine-tuning a pose predictor with observational updates, employing residual reinforcement learning for skill refinement, and filtering spurious predictions from a termination predictor.
Our system improves performance by fine-tuning a pose predictor with observational updates, employing residual reinforcement learning for skill refinement, and filtering spurious predictions from a termination predictor.

Predictive Control: Enhancing Robustness and Adaptability

ReinforceGen significantly improves the precision and dependability of robotic skill execution through the implementation of predictive capabilities. The system doesn’t simply react to current states; it actively forecasts future states using both pose prediction – anticipating the object’s configuration – and termination prediction, which estimates when a skill is nearing completion. By looking ahead, the robot can proactively adjust its actions, mitigating potential errors before they occur and ensuring smoother, more reliable performance. This predictive approach allows the system to navigate complex scenarios with greater robustness, particularly in tasks demanding precise manipulation and contact, ultimately leading to a substantial improvement in overall skill success rates.

The ReinforceGen framework leverages advanced algorithms, notably DrQ-v2, to refine skill execution and significantly improve adaptability to new and varied environments. This optimization process goes beyond initial training, allowing the system to learn from its experiences and generalize effectively to previously unseen scenarios. DrQ-v2, a model-based reinforcement learning technique, enables the agent to anticipate the consequences of its actions, leading to more robust and reliable performance. This fine-tuning process is crucial, as it not only boosts task completion rates by $24.41\%$ but also delivers a $50\%$ relative performance increase compared to baseline methods lacking state observability, demonstrating a substantial improvement in the system’s ability to handle the complexities of real-world manipulation tasks.

The culmination of these advancements in prediction and refinement strategies results in an impressive 80% success rate when executing complex, multi-stage manipulation tasks that require precise contact interactions. This figure nearly doubles the performance achieved by previously established state-of-the-art methods, as demonstrated in recent work (garrett2024skillmimicgen). Beyond overall success, focused skill fine-tuning demonstrably enhances task completion, yielding a 24.41% improvement, and delivers a substantial 50% relative performance gain when compared to a baseline Hierarchical Skill Policy (HSP) operating without state observability. These results highlight the potential for robust and reliable robotic manipulation in dynamic and uncertain environments.

ReinforceGen demonstrates significantly greater robustness to action noise compared to HSP-Priv across most tested tasks.
ReinforceGen demonstrates significantly greater robustness to action noise compared to HSP-Priv across most tested tasks.

The pursuit of robust robotic manipulation, as demonstrated by ReinforceGen, echoes a fundamental truth about all complex systems. This framework’s integration of imitation learning, reinforcement learning, and automated data generation isn’t merely about achieving a functional policy; it’s about fostering a system capable of adapting and enduring. As Blaise Pascal observed, “All of humanity’s problems stem from man’s inability to sit quietly in a room alone.” This observation, while seemingly unrelated, highlights the core of ReinforceGen’s success – allowing the system to ‘sit quietly’ and explore through generated data, building resilience through iterative learning rather than demanding immediate, flawless performance. The framework acknowledges that systems learn to age gracefully, and sometimes observing the process is better than trying to speed it up.

What Lies Ahead?

The framework detailed within represents not a solution, but a strategic deceleration of inevitable entropy. Every bug encountered in the generated data, every instance of policy failure, is a moment of truth in the timeline of robotic skill acquisition. ReinforceGen offers a compelling method for delaying the onset of diminishing returns in long-horizon manipulation, but it does not erase the fundamental limitations of any system operating within a finite state space. The elegance of hybrid policies, combining imitation and reinforcement learning, merely shifts the burden – from initial programming to the ongoing cost of exploration and adaptation.

Future iterations will undoubtedly focus on automating the curriculum – a perpetual attempt to anticipate the system’s weaknesses before they manifest. However, a crucial, often overlooked aspect remains: the very definition of ‘skill’. Is it simply successful task completion, or does it encompass robustness, adaptability, and graceful degradation? Technical debt, in this context, is the past’s mortgage paid by the present’s computational resources. A more holistic approach would acknowledge that every optimization introduces new vulnerabilities, and that true progress lies not in eliminating error, but in building systems capable of absorbing it.

The field edges toward more sophisticated data generation methods, but the ultimate constraint isn’t algorithmic – it’s the inherent complexity of the physical world. The next phase will likely necessitate a deeper engagement with principles of embodiment, morphology, and the often-ignored role of passive dynamics. ReinforceGen is a valuable step, but it is merely a single beat in the long, unfolding rhythm of machine learning’s aging process.

Original article: https://arxiv.org/pdf/2512.16861.pdf

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

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2025-12-20 03:46