OMRON SINIC X Corporation (HQ: Bunkyo-ku, Tokyo; President and CEO: Masaki Suwa; hereinafter “OSX”) will present the latest research findings at 2026 IEEE International Conference on Robotics & Automation　(ICRA 2026).

ICRA 2026 is one of the world’s largest and most influential international conferences on robotics and automation. In 2026, the conference will be held in Vienna, Austria, from Jun 1 to Jun 5, 2026 (local time).

The three research papers that OSX will present at the local event are as follows.

ICRA 2026 presentations

■ SCU-Hand with Integrated Single-Sheet Valve: A Funnel-Shaped Robotic Hand for Milligram-Scale Powder Handling

Tomoya Takahashi, Yusaku Nakajima, Cristian Camilo Beltran-Hernandez, Yuki Kuroda, Kazutoshi Tanaka, Masashi Hamaya, Kanta Ono, Yoshitaka Ushiku

Laboratory Automation (LA) has the potential to accelerate solid-state materials discovery by enabling continuous robotic operation without human intervention. While robotic systems have been developed for tasks such as powder grinding and X-ray diffraction (XRD) analysis, fully automating powder handling at the milligram scale remains a significant challenge due to the complex flow dynamics of powders and the diversity of laboratory tasks.
 To address this challenge, this study proposes the SCU-Hand-SV (Soft Conical Universal Robotic Hand with Single-sheet Valve), which preserves the softness and conical sheet design of prior work while incorporating a controllable valve at the cone apex to enable precise, incremental dispensing of milligram-scale powder quantities.
The SCU-Hand-SV is integrated with an external balance through a feedback control system based on a model of powder flow and online parameter identification. Experimental evaluations using glass beads, monosodium glutamate, and titanium dioxide demonstrated that 80% of the trials achieved an error within +2 mg to -2 mg, and the maximum error observed was approximately 20 mg across a target range of 20 mg to 3 g. In addition, by incorporating flow prediction models commonly used for hoppers and performing online parameter identification, the system is able to adapt to variations in powder dynamics. Compared to direct PID control, the proposed model-based control significantly improved both accuracy and convergence speed.
These results highlight the potential of the proposed system to enable efficient and flexible powder weighing, with scalability toward larger quantities and applicability to a broad range of laboratory automation tasks.

https://arxiv.org/abs/2512.07091
https://ttomoya00.github.io/SCU-Hand-SV/

■ Robust and Resilient Soft Robotic Object Insertion with Compliance-Enabled Contact Formation and Failure Recovery

Mimo Shirasaka, Cristian Camilo Beltran-Hernandez, Masashi Hamaya, Yoshitaka Ushiku

We address robust and resilient object insertion using a passively compliant soft wrist that permits large deformations and safely absorbs contacts, without high-frequency control or force sensing. To improve robustness, we structure the task as compliance-enabled contact formations: a sequence of contact states that progressively constrain specific degrees of freedom. While this segmentation mitigates moderate uncertainty, failures still occur under severe pose errors or environmental variations (e.g., friction changes, peg geometry), which traditionally require retuning goals or retraining controllers. To achieve both robustness and resilience, we therefore integrate compliance-enabled failure recovery into the contact-formation framework. Our key insight is that wrist compliance permits safe, repeated recovery attempts. A pre-trained vision-language model (VLM) assesses each skill execution from terminal poses and images, identifies failure modes, and proposes recovery actions by selecting skills and updating goals. In simulation, our method achieved an 83% success rate, recovering from failures induced by randomized conditions—including grasp misalignments up to 5 degrees, hole-pose errors up to 20 mm, fivefold increases in friction, and previously unseen square/rectangular pegs—and we further validate the approach on a real robot.

https://arxiv.org/pdf/2509.17666
https://omron-sinicx.github.io/compliance-enabled-failure-recovery/

■ Simulation-Driven Evolutionary Motion Parameterization for Contact-Rich Granular Scooping with a Soft Conical Robotic Hand

Yongliang Wang, Cristian Camilo Beltran-Hernandez, Tomoya Takahashi, Masashi Hamaya

Tool-based scooping is vital in robot-assisted tasks, enabling interaction with objects of varying sizes, shapes, and material states. Recent studies have shown that flexible, reconfigurable soft robotic end-effectors can adapt their shape to maintain consistent contact with container surfaces during scooping, improving efficiency compared to rigid tools. These soft tools can adjust to varying container sizes and materials without requiring complex sensing or control. However, the inherent compliance and complex deformation behavior of soft robotics introduce significant control complexity that limits practical applications. To address this challenge, this paper presents the development of a physics-based simulation model of a deformable soft conical robotic hand that captures its passive reconfiguration dynamics and enables systematic trajectory optimization for scooping tasks. We propose a novel physics-based simulation approach that accurately models the soft tool’s morphing behavior from flat sheets to adaptive conical structures, combined with an evolutionary strategy framework that automatically optimizes scooping trajectories without manual parameter tuning. We validate the optimized trajectories through both simulation and real-robot experiments. The results demonstrate strong generalization and successfully address a range of challenging tasks previously beyond the reach of existing approaches.

https://arxiv.org/abs/2604.05531
https://sites.google.com/view/scoopsh

※Author information is current as of the date of writing or submission. Please be advised that the information may become outdated after that point.

For any inquiries about OSX, please contact us here.