Teaching Robots to Understand Praise

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Author: Denis Avetisyan

New research introduces a dataset and benchmark for training robots to interpret natural language feedback, paving the way for more intuitive human-robot interaction.

RoboReward offers a general-purpose approach to vision-language reward modeling, significantly improving reward accuracy and reinforcement learning performance in robotic tasks.

Effective reinforcement learning hinges on well-defined rewards, yet obtaining these in real-world robotics often necessitates tedious human labeling or brittle, hand-engineered objectives. This work introduces RoboReward: General-Purpose Vision-Language Reward Models for Robotics, a dataset and benchmark designed to evaluate vision-language models (VLMs) as automatic reward functions for robotic tasks. Through targeted data augmentation-including the generation of calibrated negative examples-and fine-tuning, we demonstrate that VLMs can achieve substantial improvements in reward accuracy and, crucially, enhance downstream reinforcement learning performance-even surpassing results obtained with larger, specialized models and narrowing the gap to human-provided rewards. Can these advancements pave the way for more adaptable and autonomous robotic systems capable of learning from complex, unstructured environments?

Addressing the Reality Gap in Robotic Learning

Robotic learning frequently encounters limitations due to its dependence on meticulously crafted reward functions or signals that are only occasionally triggered. This reliance on a priori human design restricts a robot’s ability to generalize to novel situations and adapt to unforeseen circumstances. Hand-engineered rewards, while seemingly straightforward, often fail to capture the nuanced complexities of real-world tasks, leading to brittle behaviors and a lack of robustness. Sparse rewards, where feedback is only provided upon successful completion of a task, present an even greater challenge, as the robot must navigate a vast search space with limited guidance. Consequently, these approaches impede the development of truly autonomous robots capable of operating effectively in dynamic and unpredictable environments, highlighting the need for more flexible and adaptable reward mechanisms.

Despite recent advances, vision-language models frequently falter when evaluating the nuances of robotic performance in authentic environments. These models, trained largely on static datasets, struggle to interpret the complexities of dynamic, real-world actions, often misjudging the effectiveness of a robot’s maneuvers. This misinterpretation stems from a disconnect between the model’s learned representations and the subtle cues indicative of successful task completion – a robot might, for instance, achieve a goal through an unconventional, yet effective, motion that the model deems incorrect. Consequently, policies generated based on these flawed assessments are often suboptimal, leading to inefficient or unsuccessful robotic behavior and hindering the development of truly adaptable, intelligent machines.

Introducing RoboReward: A Foundation for Grounded Robotic Learning

The RoboReward dataset comprises 700,000+ real-robot demonstrations collected from the Open X-Embodiment (OXE) and RoboArena platforms. These demonstrations cover a broad spectrum of robotic tasks, including manipulation, locomotion, and navigation, performed by a variety of robot morphologies and in diverse environments. Data is captured using both visual and proprioceptive sensors, providing a multi-modal representation of robot states and actions. The dataset’s scale and diversity are intended to facilitate the training of robust and generalizable reward models for reinforcement learning applications, overcoming limitations associated with simulated or limited-scope datasets.

The RoboReward dataset facilitates the development of reward models by providing paired robot state and scalar reward signals derived from human-generated demonstrations. These trained reward models function as learned value functions, effectively replacing or augmenting manually designed reward functions in reinforcement learning (RL) pipelines. By learning from demonstrated successful behaviors, the reward model can accurately evaluate the quality of new robot actions, enabling RL algorithms to converge more quickly and reliably, particularly in complex or high-dimensional robotic control tasks where specifying a suitable reward function is challenging. This approach circumvents the reward engineering bottleneck often encountered in traditional RL applications and allows for the transfer of learned reward signals across different robotic platforms and environments.

Refining Reward Accuracy Through Strategic Data Augmentation

Negative examples data augmentation increases the robustness of reward models by specifically adding instances of undesirable states or actions to the training dataset. Techniques like Counterfactual Relabeling generate these negative examples by perturbing existing successful trajectories and relabeling the resulting failures, while Temporal Clipping focuses on identifying and including segments of trajectories leading to negative outcomes. This process addresses a common limitation of reward model training, where the dataset is often biased towards successful outcomes, hindering the model’s ability to accurately discriminate between good and bad behavior. By explicitly exposing the model to critical failure cases, data augmentation improves its capacity to generalize and provide more reliable reward signals during reinforcement learning.

Incorporating data augmented with negative examples demonstrably improves the accuracy of reward models used in Reinforcement Learning (RL). Increased accuracy directly translates to more reliable reward signals during policy optimization, allowing algorithms to better differentiate between successful and unsuccessful actions. This refined discrimination enables faster convergence and improved performance of learned policies, as the agent receives more precise feedback on its behavior. Consequently, training with augmented datasets yields reward models that generalize more effectively to unseen states and actions, leading to robust and efficient learning in complex RL environments.

Demonstrating Impact: Scaling Vision-Language Reward Models for Superior Performance

The development of robust vision-language models hinges on the availability of comprehensive datasets, and the RoboReward Dataset addresses this need by providing a rich resource for training reward models. This dataset enabled the creation of RoboReward 4B and RoboReward 8B, models that demonstrably exceed the performance of existing alternatives. These models aren’t simply larger; they exhibit a heightened capacity to accurately assess robotic task completion based on both visual input and natural language instructions. Through careful curation and scale, the RoboReward Dataset facilitates a nuanced understanding of desired outcomes, allowing the resulting reward models to provide more effective guidance for robotic agents and ultimately driving improvements in robotic task success rates.

Rigorous evaluation of the RoboReward 8B model, conducted using the RoboRewardBench benchmark, reveals a Mean Absolute Error (MAE) of just 0.665. This represents a substantial advancement in vision-language model accuracy, demonstrably outperforming all currently evaluated frontier models, including Google’s Gemini 2.5 Pro which achieved an MAE of 0.902 on the same benchmark. The lower the MAE, the closer the model’s predictions are to the ground truth, signifying RoboReward 8B’s superior ability to accurately assess robotic task performance based on visual and textual inputs. This precision is critical for effectively guiding robotic agents and optimizing their behavior through reward signals.

The integration of vision-language reward models with reinforcement learning algorithms has yielded substantial gains in robotic task completion. Specifically, policies utilizing these reward models, such as Diffusion Policy, demonstrate a marked improvement in real-world success rates. Prior to this advancement, the Pick-and-place task achieved a mere 5% success rate; however, with the implementation of these reward models, that figure has surged to 50%. Similarly, the Open drawer task, previously successful in only 10% of attempts, now boasts an 80% success rate. These results indicate that effectively translating natural language instructions into quantifiable rewards significantly enhances a robot’s ability to learn and execute complex manipulation tasks, paving the way for more intuitive and reliable human-robot interaction.

Evaluations conducted on the RoboRewardBench reveal a significant performance advantage for RoboReward 8B, achieving a Mean Absolute Error (MAE) of 0.665. This result establishes a clear benchmark, notably surpassing the performance of Gemini Robotics-ER 1.5, which registered an MAE of 0.906 on the same dataset. The lower MAE indicates that RoboReward 8B more accurately assesses the quality of robotic actions based on language instructions, suggesting a heightened ability to discern successful task completion. This enhanced precision is crucial for effective reinforcement learning, as it provides a more reliable signal for training robots to perform complex tasks with greater consistency and accuracy.

The pursuit of generalized robotic intelligence, as explored in this work with RoboReward, necessitates a holistic understanding of reward function design. It’s not simply about maximizing a score, but about creating a system where structure dictates behavior over time. As Bertrand Russell observed, “The difficulty lies not so much in developing new ideas as in escaping from old ones.” This rings true in reinforcement learning; clinging to traditional reward structures can impede progress towards truly generalizable robotic systems. The paper demonstrates how targeted data augmentation-a method of escaping existing data limitations-improves reward accuracy, highlighting the importance of challenging established norms to foster innovation. A well-defined reward model isn’t a static target, but an evolving component of a complex, interconnected system.

Future Directions

The creation of RoboReward, and the demonstrated gains from targeted data augmentation, highlight a recurring tension in applied reinforcement learning: the brittleness of reward functions. While vision-language models offer a compelling path toward more intuitive and flexible reward specification, this work implicitly acknowledges the inherent difficulty of distilling human intent into a scalar signal. The improvements achieved are not merely about scaling model size, but about curating data to expose the model to critical edge cases – a process that feels less like artificial intelligence and more like carefully designed tutoring.

Future work must move beyond accuracy metrics on curated datasets. True generalization will require evaluating these reward models in complex, dynamic environments with unforeseen circumstances. The current focus on counterfactual relabeling, while promising, feels like treating the symptom rather than the disease. A deeper exploration of the structure of reward signals – what makes a good reward, and how can this be encoded into the model architecture – is essential.

Ultimately, the field needs to confront the question of what constitutes “intelligence” in a robotic system. Is it simply the ability to maximize a reward, or something more nuanced? Good architecture is invisible until it breaks, and only then is the true cost of decisions visible.

Original article: https://arxiv.org/pdf/2601.00675.pdf

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

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2026-01-05 10:40