Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2611.08209

RoboARCade: Benchmarking Agentic Robot Control

Satvik Sharma·Hengyuan Hu·Mihika Kamat·Dorsa Sadigh·Jeannette Bohg
View original source ↗Read source document ↗Export metadata ↓

Abstract

Improvements in multi-modal capabilities have enabled agents to act directly as robot policies, a setting we refer to as agentic robot control (ARC). We introduce RoboARCade, a benchmark of long-horizon manipulation tasks across three embodiments designed to test in-context adaptation, memory, and reasoning for precise control. The tasks build on existing open-source benchmarks, which we make harder by widening the range of initial conditions. RoboARCade is easy to use and extend: all tasks run on a single simulation backend, MuJoCo, each task is a self-contained data pack, and agents connect through interchangeable interfaces, so new tasks, agents, and interfaces can be added independently. The benchmark separates leading agents: GPT-6 Astra achieves 49.8% compared with 43.6% for Claude Fable 5.1, leaving substantial room for improvement. Comparing two interfaces, we find that a richer visual interface adds only 1.5 to 6.8 points at 1.7 to 2.3 times the cost. Failures stem mostly from choosing a poor manipulation strategy or executing a good one imprecisely, rarely from misunderstanding the scene, and agents incorrectly claim success on 24% to 39% of attempted subtasks. Agents can also improve without human input: one successful demonstration in context raises Astra's score from 53.5% to 73.0% on a five-task subset, and a prompt the agent writes and revises itself reaches 72.0%. Code, videos and full traces are available at roboarcade.org.

Explore related subjects

cs.RO

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Satvik Sharma, Hengyuan Hu, Mihika Kamat, Dorsa Sadigh, Jeannette Bohg. 2026-11-05. RoboARCade: Benchmarking Agentic Robot Control. https://arxiv.org/abs/2611.08209

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Sketch-MoMa: Teleoperation for Mobile Manipulator via Interpretation of Hand-Drawn Sketches

To use assistive robots in everyday life, a remote control system with common devices, such as 2D devices, is helpful to control the robots anytime and anywhere as intended. Hand-drawn sketches are one of the intuitive ways to control robots with 2D devices. However, since similar sketches have different intentions from scene to scene, existing work needs additional modalities to set the sketches' semantics. This requires complex operations for users and leads to decreasing usability. In this paper, we propose Sketch-MoMa, a teleoperation system using the user-given hand-drawn sketches as instructions to control a robot. We use Vision-Language Models (VLMs) to understand the user-given sketches superimposed on an observation image and infer drawn shapes and low-level tasks of the robot. We utilize the sketches and the generated shapes for recognition and motion planning of the generated low-level tasks for precise and intuitive operations. We validate our approach using state-of-the-art VLMs with 7 tasks and 5 sketch shapes. We also demonstrate that our approach effectively specifies the detailed motions, such as how to grasp and how much to rotate. Moreover, we show the competitive usability of our approach compared with the existing 2D interface through a user experiment with 14 participants.

cs.RO↗
arXiv2026

Evolving Drone Morphologies for Agile Flight

The design of multicopter drones has remained almost the same since their inception. While conventional designs work well in many cases, they may not be optimal in specific tasks. This paper revisits rotary drone design by exploring which body morphologies are best suited for different objectives and constraints. An evolutionary algorithm is used to produce drone morphologies that performs better than standard configurations at thrust-to-weight ratio, maneuverability and size. To generate a range of drones with performance trade-offs between them, the non-dominated sorting genetic algorithm II is used. The evolved drone bodies outperform a standard multicopters in at least one of the three objectives. The Pareto front extrema show improvement of 489.2%, 23% and 4.8% in maneuverability, thrust-to-weight ratio and size, respectively, when compared to a standard quadcopter. The improvement in maneuverability is attributed to the tilt angles of the motors, while the increase in thrust-to-weight ratio is due to the higher number of propellers. Experiments show that the performance improvement transfers to a real world drone racing task, where the evolved drone outperforms the standard drone in terms of number of gates passed,flight speed and lap times.

cs.RO↗
arXiv2026

VLD: Visual Learned Distance as an Interface for Reinforcement Learning Navigation

Training end-to-end policies from image data to directly predict navigation actions for robotic systems has proven inherently difficult. Existing approaches often suffer from either the sim-to-real gap during policy transfer or limited action-labeled training data. To address this problem, we introduce Visual Learned Distance (VLD), a scalable framework for image-goal navigation that decouples perception learning from policy learning. We first train a self-supervised distance-to-goal predictor on about 3,000 hours of internet-scale and simulated video, providing a distance signal that a reinforcement learning (RL) policy can minimize. The RL policy is trained separately in simulation using privileged geometric distances perturbed by GeoNoise, a learned noise model that reproduces the viewpoint- and overlap-dependent errors of the trained distance predictor. At deployment, the policy consumes VLD predictions zero-shot, without retraining, inheriting ``where to go'' from large-scale visual training while retaining the robust navigation behaviors learned in simulation. This error modeling is key to the transfer: with VLD inputs, GeoNoise-trained policies reach $58.7\%$ success on HM3D and $76.2\%$ on Gibson, whereas policies trained with generic temporally correlated noise collapse to $4.9\%$ and $11.5\%$. On a physical robot, the VLD-driven policy succeeds in $88\%$ of trials, compared with $60\%$ for a strong end-to-end image-goal baseline that outperforms it in simulation. Our decoupled design thus provides an alternative and scalable path toward reliable navigation policies that transfer from simulation to the real world.

cs.RO↗