Can Robots Handle the Grocery Run?

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Author: Denis Avetisyan

A new benchmark challenges robotic systems to navigate the complexities of a real-world retail environment, revealing significant gaps in current performance.

The system demonstrates robotic operation within a deliberately complex retail setting, acknowledging that any constructed environment inevitably anticipates its own limitations and eventual decay.
The system demonstrates robotic operation within a deliberately complex retail setting, acknowledging that any constructed environment inevitably anticipates its own limitations and eventual decay.

Researchers introduce RoboBenchMart, a simulated retail environment for evaluating and improving robotic systems’ ability to perform tasks in unstructured, multimodal settings.

Despite advances in robotic manipulation, benchmarks often fall short of capturing the complexities of real-world environments, particularly those with dense, unstructured clutter. To address this gap, we introduce RoboBenchMart: Benchmarking Robots in Retail Environment, a challenging simulated dark store designed to evaluate robotic systems on realistic grocery manipulation tasks. Our results demonstrate that current state-of-the-art generalist models struggle with even common retail actions within this setting, highlighting a significant performance gap. Will this new benchmark spur the development of more robust and adaptable robotic systems capable of navigating and operating in dynamic, real-world retail spaces?

The Illusion of Retail Fidelity

Training robust robotic policies demands increasingly complex environments, yet achieving sufficient realism carries a significant computational cost. Existing simulation platforms struggle to replicate the scale and nuance of modern retail spaces, hindering the transfer of policies to real-world applications. This motivated the development of RoboBenchMart, a standardized, open-source benchmark designed to accelerate retail robotics research, recognizing that every optimization is a trade-off against adaptability.

Simulation time scales with the number of shelving units, but optimization of mesh complexity significantly reduces computational cost, as demonstrated by the comparison between optimized (blue) and original meshes.
Simulation time scales with the number of shelving units, but optimization of mesh complexity significantly reduces computational cost, as demonstrated by the comparison between optimized (blue) and original meshes.

The pursuit of scalable simulation requires accepting the inherent limitations of approximation.

Procedural Generation and the Ghost of Retail

RoboBenchMart establishes a core infrastructure for simulating complex retail environments, built upon the Maniskill3 simulation framework. A key component is the Store Plan Generator, which utilizes procedural generation guided by Tensor Fields to create realistic and scalable store layouts. This system facilitated the creation of a training dataset comprising 2,976 trajectories and 1,401,169 transitions, incorporating a diverse library of visual textures for variations in appearance.

The store generation pipeline leverages a diverse library of ceiling, wall, and floor textures, allowing for a wide range of visual variations in generated environments.
The store generation pipeline leverages a diverse library of ceiling, wall, and floor textures, allowing for a wide range of visual variations in generated environments.

The generated environments are merely echoes of the spaces they attempt to represent.

Automated Trajectory Generation: The Dance of the Machine

The Store Trajectory Sampler automates the collection of trajectories for typical retail manipulation and navigation tasks, addressing a key bottleneck in robotic development. Integrating Motion Planning and Reinforcement Learning, the system generates feasible and optimized robot movements, refining initial trajectories for task completion time and success rate. These generated trajectories serve as ground truth data for evaluating and comparing robotic policies, accelerating progress through objective assessment.

The motion planner utilizes heuristically generated anchor poses to facilitate efficient and effective navigation within complex environments.
The motion planner utilizes heuristically generated anchor poses to facilitate efficient and effective navigation within complex environments.

Every successful trajectory is a temporary reprieve from the inevitable chaos of the real world.

The Benchmark and the Limits of Adaptation

The Store Robotics Benchmark offers a standardized framework for evaluating robotic navigation and manipulation within complex retail environments using the Fetch Robot platform. Evaluations using baseline policies demonstrate significant performance disparities, with limited generalization capability even for moderately novel scenarios. Enhancements through Hierarchical Geometric Models and Level-of-Detail Adjustment enable simulations at larger scales, achieving a 3x speedup without substantial visual degradation.

Asset geometry can be effectively approximated with reduced face counts, balancing visual fidelity with computational efficiency, as shown by the examples of varying mesh complexities.
Asset geometry can be effectively approximated with reduced face counts, balancing visual fidelity with computational efficiency, as shown by the examples of varying mesh complexities.

Every optimization, every simplification, is merely a deferral of eventual systemic collapse.

The pursuit of robotic generality in retail, as illuminated by RoboBenchMart, echoes a familiar pattern. Systems are rarely built; they accrue complexity, adapting – or failing to adapt – to the unpredictable currents of their environment. This benchmark, with its focus on realistic, unstructured settings, doesn’t merely assess performance; it charts the growing pains of these systems. As Tim Berners-Lee observed, “The Web is more a social creation than a technical one.” Similarly, the success of robotic automation isn’t solely about algorithms or hardware, but about how well these creations integrate – or fail to integrate – within the complex social ecosystem of a retail space. The struggle of current state-of-the-art models is not a failing, but a sign of growth, a necessary stage in the evolution of these digital entities.

What Lies Ahead?

RoboBenchMart, as a constructed reality, offers a glimpse into the brittle heart of robotic ambition. Every meticulously modeled shelf, every procedurally generated customer, is a promise made to the past – a desire for control over chaos. Yet, the reported performance suggests these promises are quickly becoming debts. The benchmark doesn’t reveal what robots can do, but rather exposes the limitations of current approaches when faced with genuine, unscripted complexity. It’s a familiar cycle: build a world, measure the failure, refine the world, repeat.

The true challenge isn’t trajectory generation, or multimodal learning, but accepting that the ‘generalist policy’ is an asymptotic ideal. Each improvement will merely reveal a new, subtler form of fragility. The environment, after all, will not remain static. Customers will invent new obstructions, retailers will rearrange displays, and the very definition of ‘retail’ will evolve.

One anticipates a future where these environments cease to be benchmarks, and instead become gardens – spaces for robots to grow resilience. Everything built will one day start fixing itself, adapting not to a predefined test, but to the unpredictable currents of a living system. Control, it seems, is an illusion that demands increasingly stringent SLAs.

Original article: https://arxiv.org/pdf/2511.10276.pdf

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

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2025-11-14 12:39