Papers for

automated warehouse operators

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Passive arm stiffness affects payload stability in quadruped walking

Gait-Dependent Effects on Quadruped Locomotion for Load-Carrying using Passive Mechanism

Abstract: Passive mechanical interfaces offer a lightweight alternative to actuated manipulators for quadruped payload carrying, but their impedance directly couples the payload dynamics with the locomotion pattern. This paper analyzes how passive-arm stiffness-damping selection affects payload-carrying locomotion under different gait and payload conditions. We compare damped and underdamped passive-arm impedance configurations in simulation during flat-ground locomotion. For crawl gaits, where the support polygon remains well defined, the results show that underdamped impedance increases passive-joint oscillations and can reduce the ZMP margin with respect to the support polygon. Trot is retained as a dynamic excitation case for the passive arm, but it is not used for direct ZMP-margin stability comparison. The results are summarized in gait-payload-stiffness-damping maps, where ZMP-margin reduction is evaluated for crawl gaits and trot is retained only as a passive-arm excitation case.

Thu 10 SeptRobotics
The gist
Carrying loads on four-legged robots using simple, spring-like arms is lighter than using powered arms, but the way these arms move depends on the walking style. The authors studied how adjusting the arm’s spring and damping settings changes carrying stability during different walking patterns. They found that with a slow and stable walk (crawl gait), making the arms less damped causes more swinging and can make the robot less stable. The study provides maps that help choose arm settings for different walks and payloads to keep the robot balanced.
Open → 2609.11059v1

Duet-DINO improves robot grasping using two camera views

DUET-DINO: Simultaneous Cross-View World Modeling for Latent Planning in Robot Manipulation

Abstract: Action-conditioned latent world models predict future visual representations, enabling zero-shot goal-conditioned robot planning and control. However, their predictions for fine-grained spatial and rotational actions are unreliable for full 7-DoF end-effector control. To address this gap, we introduce DUET-DINO, a simultaneous cross-view latent world model that jointly learns action-conditioned predictions from static side- and wrist-camera observations through cross-view conditioning. By exploiting complementary global scene and gripper-centric information, DUET-DINO enables latent planning over the full 7-DoF action space. Across spatially diverse reach, orientation-intensive angled-reach, and multi-goal grasp-and-lift tasks, DUET-DINO consistently outperforms single-view and independent dual-view baselines, achieving 92% success on reach, 72.5% on angled-reach, and 60.0% on lift tasks. DUET-DINO is trained from scratch on DROID and RoboArena datasets and generalizes robustly under visual distribution shifts. We further show that while V-JEPA 2 wrist-view predictions underestimate visual dynamics induced by fine-grained actions, DINOv3 predictions better capture action-conditioned scene changes, leading to stronger downstream planning. The code and model checkpoints will be open-sourced. Project page: https://utn-air.github.io/DUET-DINO

Wed 9 SeptRoboticsComputer Vision and Pattern Recognition
The gist
Robots often plan their movements by predicting how the world will look after each action, but this can be tricky when the robot’s hand moves precisely in all directions. The authors introduce Duet-DINO, a system that learns to predict from two camera views at once — one from the side and one from the robot’s wrist — so it can better understand how actions will change what it sees. This helps the robot plan more accurate movements with its full range of motion, leading to improved success in reaching and grasping tasks. Their method works well even when the visual environment changes and outperforms approaches using only one camera view.
Open → 2609.10506v1