Rudolf Lioutikov

Touch Improves Learning Dexterous Grasping from Demonstration

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

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 simu...

TACTIC: Understanding Tactile Encoders and Conditioning for Contact-rich Robot Manipulation Policies

Tactile information is essential for contact-rich manipulation tasks in robotics. Vision-based tactile sensors make it particularly easy to design end-to-end manipulation policies with tactile sensing, as they enable the use of existing encoders from computer vision. However, this has led to a huge variety of architect...

ReCAT: Remember, Count, and Time: Structured Recurrent Memory for Robot Manipulation

Comparisons within ReCAT show that the observation encoder and every-block memory conditioning are needed for this performance, and that update rules developed for efficient sequence modeling behave differently as robot memory: additive updates have the highest observed success on counting and timing, and delta-rule up...

From Rollout to Reset: A Graph-Based Harness for Autonomous Long-Horizon Manipulation Evaluation

HALTER, a Harness for Autonomous Long-horizon Task Evaluation and Reset, which restores the scene by planning over a library of learned atomic reset skills, so demonstration cost scales with the size of that library rather than with the number of terminal states.