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arxivcs.RO2026-07-01

Sensorless Four-Channel Control Architecture Using Inverse Dynamics Modeling for Human-Scale Bilateral Teleoperation

Amir Noohian, Dylan Miller, Justin Valentine, Alan Lynch, Martin Jagersand

The four-channel teleoperation architecture is a well-established framework for achieving transparency in bilateral systems. However, its performance in human-scale teleoperation is limited by high inertia, modeling challenges, and reliance on noisy and costly force/torque sensors. This paper introduces a sensorless four-channel architecture based on inverse dynamics modeling. The controller is implemented and validated on a customized WAM bilateral teleoperation setup. Experiments demonstrate that the proposed approach outperforms conventional two- and four-channel schemes as well as transparency-enhancement methods, improving position and force tracking, reducing operator effort, and increasing maximum transmittable impedance without external sensors. A door-opening case study involving sustained whole-body contact along the manipulator further demonstrates the effectiveness of the method in realistic human-scale manipulation tasks.

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arxivcs.RO2026-07-01

[Preprint] Dynamic Modeling, Gait Synthesis, and Control of a Novel Subsurface Bore Propagator

Lina van Brügge, Shruti Kotpalliwar, Anton Koval, Akshit Saradagi, George Nikolakopoulos

In this article, we present dynamic modeling, gait synthesis, and feedback control design for a modular novel subsurface robot, designed for human-free subsurface exploration and excavation. The subsurface propagator design is based on two major aspects: 1) anchor and propel move…

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arxivcs.RO2026-07-17

Certifiable Safe Model-Based Reinforcement Learning with Control-Affine Dynamics Approximation

Hao Zhou, Yanze Zhang, Cameron Reid, Wenhao Luo

Safe model-based reinforcement learning (RL) often bridges control-theoretic analysis and RL for robots to safely explore (partially) unknown system dynamics while deriving control actions for task efficiency. The control performance and safety assurance typically rely on prior k…

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arxivcs.RO2026-07-06

Efficient Transfer Learning of Robot Dynamic Models Using Morphological Similarity

Pavlo Kupyn, Yuya Hamamatsu, Roza Gkliva, Asko Ristolainen, Maarja Kruusmaa

This study proposes a neural network-based transfer learning framework for modeling the dynamics of soft, fin-actuated underwater robots. We focus on morphologically similar robots that differ in scale and hydrodynamic properties. A model trained on data from a larger robot (sour…

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arxivcs.LGcs.RO2026-07-08

Safe Reinforcement Learning using Ideas from Model Predictive Control

Georg Schäfer, Jakob Rehrl, Stefan Huber, Simon Hirlaender

Reinforcement learning (RL) enables the synthesis of control policies directly from data, making it highly appealing for complex cyber-physical systems (CPSs) and robotics. A persistent challenge, however, is ensuring strict, hard safety constraints during the active learning pha…

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arxiveess.SYcs.AIcs.RO2026-07-23

Interaction Dynamics Modeling and Predictive Control for Safe Steerable Catheter--Tissue Interaction

Yongyan Cao

Safe steerable catheter control is fundamentally a problem of interaction dynamics: the tip must follow a planned motion, remain compliant against moving tissue, reject friction and hysteresis, and respect a clinically meaningful never-exceed contact-force bound. We formulate cat…

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