RoboValue: A Fine-Grained Sim-and-Real Benchmark for Unified Evaluation of Robotic Value Models

AI summary

Overview

Research area: Robotics — specifically, evaluation of robotic value models, the components that judge how well a robot is executing a task from visual observations and instructions.

Technical level: Advanced. The paper assumes familiarity with robot manipulation, policy learning, and simulation-to-real transfer, though the core idea (better testing of robot "progress judges") is explained here in plain terms.

Scope: The paper introduces RoboValue, a benchmark that combines simulated and real-world manipulation tasks to test robotic value models across four distinct capability dimensions, and reports that current models fail in specific, diagnosable ways.

What This Paper Is About

Robots that learn from demonstrations or reinforcement learning often rely on a "value model" that watches what the robot is doing and estimates how well the task is going. This feedback is used both to improve the policy and to monitor execution in real time. The problem is that existing tests of these models check only a few narrow aspects of execution understanding, so nobody knows how reliable the feedback really is. RoboValue's goal is to test value models much more thoroughly and in a standardized, comparable way, using both simulated and real robot tasks.

Key Contributions

1. A unified benchmark framework. RoboValue evaluates robotic value models through shared interfaces across four complementary capability dimensions: task-state understanding, temporal progress monitoring, failure and recovery reasoning, and value consistency.

2. A combined simulation and real-world dataset. RoboValue-Dataset covers 15 simulation tasks and 20 real-world manipulation tasks, and provides both expert demonstrations for training and annotated trajectories for fine-grained evaluation.

3. A diagnostic evaluation protocol. The benchmark uses diagnostic trajectories and instruction variations to check whether value judgments actually track task requirements and how those requirements change during execution, and it evaluates representative models in zero-shot and one-shot settings under standard conditions as well as under separate shifts in robot embodiment and environment.

4. A public leaderboard and website. These are offered to support continued, comparable assessment of feedback reliability, alongside the dataset.

Main Findings

• Instruction grounding is weak at fine granularity: The evaluation reveals errors in fine-grained instruction grounding, meaning models do not reliably connect specific parts of an instruction to what is happening in the scene.

• Progress tracking is history-dependent: Models show failures in tracking progress when the judgment depends on what happened earlier in the trajectory, not just the current frame.

• Recovery assessment is unreliable: Models struggle to reason correctly about failures and recovery, an ability the benchmark tests as its own dimension.

• Strong headline scores can hide these failures: The abstract states that these errors appear even in models that score well on outcome and forward-progress measures, suggesting coarse metrics have been masking weaker capabilities.

• Value consistency is a problem: The same semantic subtask can receive substantially different value gains depending on the order in which valid execution steps occur, indicating that value judgments are not stable across equivalent executions.

• Concrete targets identified: The authors frame these findings as specific directions for improving how value models are supervised and trained. The abstract does not report numeric scores, model counts, or per-dimension result tables.

Methodology in Plain English

The researchers built a test suite rather than a single score. Because value models come in different designs, they defined shared interfaces so different models can be plugged into the same evaluation and compared fairly. They then assembled tasks in simulation and on real robots, recording expert demonstrations (so a model can be trained or given an example) and annotating trajectories so judgments can be checked at a fine level, not just "did the task succeed."

To probe specific weaknesses, they constructed diagnostic trajectories and varied the instructions given to the model, checking whether the model's value estimates respond correctly to those changes. They tested models with no task-specific examples (zero-shot) and with a single example (one-shot), both in normal conditions and under deliberate shifts — a different robot body or a different environment — to see whether the feedback still holds up. Results are aggregated on a public leaderboard.

Why This Matters

Value models sit in the feedback loop of robot learning and deployment; if their judgments are unreliable, the policies they train and the monitors they drive are unreliable too. By testing four dimensions separately and including both simulation and real hardware, RoboValue makes it possible to see where a model is weak instead of only whether it is weak overall.

Real-world applications:

• Manipulation policy improvement: Using value estimates as reward or feedback signals when learning from demonstrations, where bad feedback silently degrades the learned behavior.
• Execution monitoring and failure detection: Catching when a robot has gone off track in warehouse, manufacturing, or service settings.
• Recovery and intervention: Triggering a retry or human handoff when a failure is detected, which requires the recovery reasoning the benchmark tests.
• Sim-to-real deployment: Checking that value judgments survive a change of robot embodiment or environment before relying on them outside the lab.

Industry relevance: Any organization deploying learned manipulation — logistics, assembly, household robotics — depends on trustworthy self-assessment, and a shared benchmark plus leaderboard gives buyers and developers a common way to compare feedback reliability across models.

Future Directions

• Better value supervision: Design training signals that specifically target fine-grained instruction grounding, history-aware progress tracking, and recovery reasoning, the three areas where the abstract reports errors.

• Order- and history-aware models: Address why equivalent subtasks receive different value gains under different valid execution orders, so that value consistency becomes a property models are built for rather than an afterthought.

• Broader coverage and shift testing: Extend beyond the current 15 simulated and 20 real tasks and probe further embodiment and environment shifts, since robustness under distribution change is already part of the protocol.

• Closed-loop validation: Connect benchmark scores to actual downstream outcomes — whether better value feedback translates into better policies and more reliable monitoring in deployment.

Target Audience

Researchers and engineers working on robot learning, imitation learning, and reinforcement learning who use value functions or reward models; benchmark and evaluation researchers interested in fine-grained, sim-and-real protocols; and practitioners deploying manipulation systems who need to judge whether a robot's self-assessment can be trusted. Readers wanting a quick orientation to the problem will find the four-dimension framing useful, while specialists will get the most from the dataset and leaderboard.

Authors’ abstract

Robotic value models assess task execution from visual observations and instructions, providing feedback for policy learning and execution monitoring. However, existing evaluations cover only selected aspects of execution understanding, leaving the reliability of value feedback insufficiently tested. We introduce RoboValue, a fine-grained sim-and-real benchmark for unified evaluation of robotic value models. Its framework evaluates four complementary capability dimensions through shared interfaces: task-state understanding, temporal progress monitoring, failure and recovery reasoning, and value consistency. Diagnostic trajectories and instruction variations test whether value judgments accurately reflect task requirements and changes throughout execution. RoboValue-Dataset covers 15 simulation and 20 real-world manipulation tasks, providing expert demonstrations for training and annotated trajectories for fine-grained evaluation. We evaluate representative robotic value models in zero-shot and one-shot settings under standard conditions and separate shifts in robot embodiment and environment. Our results reveal errors in fine-grained instruction grounding, history-dependent progress tracking, and recovery assessment, even in models with strong outcome and forward-progress scores. The same semantic subtask can also receive substantially different value gains across valid execution orders. These findings identify concrete targets for value supervision, while the dataset and public leaderboard support continued assessment of feedback reliability. The website is available at https://robovalue-benchmark.github.io/.