activity
20242026
most citedTemporally Consistent Object 6D Pose Estimation for Robot Control

2 citations · 2 across the 6 of their papers we have counts for

collaborators

10 papers

cs.CV2026

AlignPose: Generalizable 6D Pose Estimation via Multi-view Feature-metric Alignment

Anna Šárová Mikeštíková, Médéric Fourmy, Martin Cífka +2

Single-view RGB model-based object pose estimation methods achieve strong generalization but are fundamentally limited by depth ambiguity, clutter, and occlusions. Multi-view pose…

cs.RO20262 cited

Temporally Consistent Object 6D Pose Estimation for Robot Control

Kateryna Zorina, Vojtech Priban, Mederic Fourmy +2

Single-view RGB object pose estimators have reached a level of precision and efficiency that makes them good candidates for vision-based robot control. However, off-the-shelf metho…

cs.RO2026

COSMIK-MPPI: Scaling Constrained Model Predictive Control to Collision Avoidance in Close-Proximity Dynamic Human Environments

Ege Gursoy, Maxime Sabbah, Arthur Haffemayer +5

Ensuring safe physical interaction between torque-controlled manipulators and humans is essential for deploying robots in everyday environments. Model Predictive Control (MPC) has…

cs.RO2026

Persistent Robot World Models: Stabilizing Multi-Step Rollouts via Reinforcement Learning

Jai Bardhan, Patrik Drozdik, Josef Sivic +1

Action-conditioned robot world models generate future video frames of the manipulated scene given a robot action sequence, offering a promising alternative for simulating tasks tha…

cs.RO2026

Warm-Starting Collision-Free Model Predictive Control With Object-Centric Diffusion

Arthur Haffemayer, Alexandre Chapin, Armand Jordana +4

Acting in cluttered environments requires predicting and avoiding collisions while still achieving precise control. Conventional optimization-based controllers can enforce physical…

cs.RO2025

REALM: A Real-to-Sim Validated Benchmark for Generalization in Robotic Manipulation

Martin Sedlacek, Pavlo Yefanov, Georgy Ponimatkin +7

Vision-Language-Action (VLA) models empower robots to understand and execute tasks described by natural language instructions. However, a key challenge lies in their ability to gen…