BOSS: Benchmark for Observation Space Shift in Long-Horizon Task

2025-02-21

AI Summary

This paper identifies Observation Space Shift (OSS)—a fundamental challenge in vision-based robotic servoing for long-horizon tasks—where execution of prerequisite skills induces distributional shifts in the observation space, severely degrading downstream skill generalization. To address this, we introduce BOSS, the first dedicated OSS benchmark, which systematically defines, isolates, and validates the OSS phenomenon. We formalize three orthogonal challenges: single-predicate, cumulative-predicate, and skill-chain OSS, enabling quantitative evaluation of their impact. A controllable simulation environment is designed to precisely inject and decouple OSS effects. Experiments show that even under the simplest challenge, state-of-the-art imitation learning methods (BC×3, OpenVLA) suffer 34–67% average performance degradation; crucially, increasing visual diversity alone fails to mitigate this drop. These results establish OSS as a critical bottleneck to robust long-horizon visual servoing—and demonstrate that existing approaches remain fundamentally unaddressed.

Technology Category

• Computer Vision: Vision for Robotics & Autonomous Driving
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Robotics has long sought to develop visual-servoing robots capable of completing previously unseen long-horizon tasks. Hierarchical approaches offer a pathway for achieving this goal by executing skill combinations arranged by a task planner, with each visuomotor skill pre-trained using a specific imitation learning (IL) algorithm. However, even in simple long-horizon tasks like skill chaining, hierarchical approaches often struggle due to a problem we identify as Observation Space Shift (OSS), where the sequential execution of preceding skills causes shifts in the observation space, disrupting the performance of subsequent individually trained skill policies. To validate OSS and evaluate its impact on long-horizon tasks, we introduce BOSS (a Benchmark for Observation Space Shift). BOSS comprises three distinct challenges:"Single Predicate Shift","Accumulated Predicate Shift", and"Skill Chaining", each designed to assess a different aspect of OSS's negative effect. We evaluated several recent popular IL algorithms on BOSS, including three Behavioral Cloning methods and the Visual Language Action model OpenVLA. Even on the simplest challenge, we observed average performance drops of 67%, 35%, 34%, and 54%, respectively, when comparing skill performance with and without OSS. Additionally, we investigate a potential solution to OSS that scales up the training data for each skill with a larger and more visually diverse set of demonstrations, with our results showing it is not sufficient to resolve OSS. The project page is: https://boss-benchmark.github.io/

Problem

• Identifies Observation Space Shift in robotics
• Evaluates OSS impact on long-horizon tasks
• Proposes insufficient data scaling solution

Innovation

• Hierarchical skill combination approach
• Observation Space Shift benchmark
• Diverse demonstration training data